energy management possibilities and challenges essay

• Search Menu
• Advanced Articles
• Editor's Choice
• Author Guidelines
• Publish with us
• Submission Site
• Open Access
• Self-Archiving Policy
• About Clean Energy
• About the National Institute of Clean and Low-Carbon Energy
• Editorial Board
• Instructions for Reviewers
• Advertising & Corporate Services
• Journals Career Network
• Journals on Oxford Academic
• Books on Oxford Academic

Article Contents

Introduction, 1 smart-home definition, 2 smart-home infrastructures, 3 smart-home energy-management scheme, 4 technical challenges of smart homes, 5 conclusion, conflict of interest.

• < Previous

Smart homes: potentials and challenges

• Article contents
• Figures & tables
• Supplementary Data

Rasha El-Azab, Smart homes: potentials and challenges, Clean Energy , Volume 5, Issue 2, June 2021, Pages 302–315, https://doi.org/10.1093/ce/zkab010

• Permissions Icon Permissions

Decentralized distributed clean-energy sources have become an essential need for smart grids to reduce the harmful effects of conventional power plants. Smart homes with a suitable sizing process and proper energy-management schemes can share in reducing the whole grid demand and even sell clean energy to the utility. Smart homes have been introduced recently as an alternative solution to classical power-system problems, such as the emissions of thermal plants and blackout hazards due to bulk plants/transmission outages. The appliances, sources and energy storage of smart homes should be coordinated with the requirements of homeowners via a suitable energy-management scheme. Energy-management systems are the main key to optimizing both home sources and the operation of loads to maximize home-economic benefits while keeping a comfortable lifestyle. The intermittent uncertain nature of smart homes may badly affect the whole grid performance. The prospective high penetration of smart homes on a smart power grid will introduce new, unusual scenarios in both generation and loading. In this paper, the main features and requirements of smart homes are defined. This review aims also to address recent proposed smart-home energy-management schemes. Moreover, smart-grid challenges with a high penetration of smart-home power are discussed.

Smart homes provide comfortable, fully controlled and secure lifestyles to their occupants. Moreover, smart homes can save energy and money with the possibility of profiting from selling clean renewable energy to the grid. On the other hand, the probable decrease in total domestic-energy loads encourages many governments to support promising smart-home technologies. Some countries have already put out many rules, laws and subsidy programmes to encourage the integration of smart homes, such as encouraging the optimization of the heating system, supporting building energy storage and/or deploying smart meters. For instance, the European Standard EN 15232 [ 1 ] and the Energy Performance of Building Directive 2010/31/EU [ 2 ], which is in line with Directive 2009/72/EC as well as the Energy Road Map 2050 [ 3 ], encourage the integration of smart-home technologies to decrease power demand in residential areas.

To control the environment, a smart home is automated by controlling some appliances, such as those used for lighting and heating, based on different climatic conditions. Now, recent control schemes adapt many functions besides classical switching ones. They can monitor the internal environment and the activities of the home occupants. They also can independently take pre-programmed actions and operate devices in set predefined patterns, independently or according to the user’s requirements. Besides the ease of life, smart homes confirm efficient usage of electricity, lowering peak load, reducing energy bills and minimizing greenhouse-gas emissions [ 4 , 5 ].

Smart homes can be studied from many points of view. The communication systems [ 6 ], social impacts [ 7 ], thermal characteristics [ 8 ], technologies and trends of smart homes [ 9 ] are reviewed individually. Moreover, the monitoring and modelling of smart-home appliances via smart meters are reviewed for accurate load forecasting, as in [ 10 , 11 ]. Recently, power-grid authorities have modified residential electrical tariffs to encourage proper demand-side management by homeowners. Different from previous reviews, this paper introduces smart homes from the electrical/economic point of view. It also discusses smart-home energy-management systems (SHEMS) in two different modes, offline load scheduling and real-time management. The prospective impacts of unusual smart-home power profiles on future smart grids are also summarized.

After this introductory section, Section 1 describes the different definitions of smart homes within the last two decades. Smart-home communication schemes and other infrastructures of smart homes are discussed in Section 2. Section 3 discusses in more detail the existing functions of SHEMS, their pre-proposed optimization techniques and related technical/economical objective functions. The impacts of smart homes on modern grids are also discussed in Section 4. Finally, in Section 5, the main conclusions and contributions of the paper are highlighted.

The term ‘smart home’ has been commonly used for about two decades to describe houses with controlled energy schemes. This automation scheme confirms easier lifestyles for homeowners than normal un-automated homes, especially for elderly or disabled persons. Recently, the concept of ‘smart home’ has a wider description to include many applications of technologies in one place.

Sowah et al. [ 12 ] define smart homes as: ‘Houses that provide their occupants a comfortable, secure, and energy efficient environment with minimum possible costs regardless their occupants.’ The Smart Homes Association defines a smart home as: ‘The integration of technology and services through home networking for a better quality of living’ [ 13 ].

Makhadmeh et al. define them as: ‘Incorporated residential houses with smart technology to improve the comfort level of users (residents) by enhancing safety and healthcare and optimizing power consumption. Users can control and monitor smart-home appliances remotely through the home energy-management system (HEMS), which provides a remote monitoring system that uses telecommunication technology’ [ 14 ].

Smart homes can be defined as: any residential buildings using different communication schemes and optimization algorithms to predict, analyse, optimize and control its energy-consumption patterns according to preset users’ preferences to maximize home-economic benefits while preserving predefined conditions of a comfortable lifestyle.

Distributed clean energy generated by smart homes provides many benefits for prospective smart grids. Consequently, the effects of smart homes on future power grids should be extensively studied. In the near future, smart homes will play a major role as a power supplier in modern grids, not only as a power consumer.

The general infrastructure of smart homes consists of control centres, resources of electricity, smart meters and communication tools, as shown in Fig. 1 . Each component of the smart-home model will be discussed in the following subsections.

Infrastructure of SHEMS source

2.1 The control centre

The control centre provides home users with proper units to monitor and control different home appliances [ 15 ]. All real-time data are collected by SHEMS to optimize the demand/generation coordination and verify the predefined objectives. The main functions of the control centre can be summarized as follows [ 15 ]:

(i) collecting data from different meters, homeowners’ commands and grid utility via a proper communication system;

(ii) providing proper monitoring and analysing of home-energy consumption for homeowners;

(iii) coordinating between different appliances and resources to satisfy the optimal solution for predefined objectives.

2.2 Smart meter

The smart meter receives a demand-response signal from power utilities as an input to the SHEMS system [ 16 , 17 ]. Recently, advanced smart-metering infrastructures can monitor many home features such as electrical consumption, gas, water and heating [ 18 ].

2.3 Appliances

Smart-home loads can be divided according to their operating nature into two categories: schedulable and non-schedulable loads. Non-schedulable loads are operated occasionally according to the homeowner’s desires without any predictable operating patterns, such as printers, televisions and hairdryers, whereas schedulable loads have a predictable operating pattern that can be shifted or controlled via SHEMS, such as washing machines and air conditioners [ 19 ].

According to [ 19 ], controllable devices are also classified into interruptible and non-interruptible load according to the effect of supply interruption on their tasks. Electric vehicles (EVs) can be considered as an exceptional load [ 20 , 21 ]. EVs have two operating modes: charging and discharging. Therefore, EVs are interruptible schedulable loads during the charging mode. Moreover, EV battery energy can also be discharged to supply power to the grid during critical events, which is known as vehicle-to-grid [ 22 ]. By SHEMS, EVs can participate in supplying loads during high-priced power periods. In low-priced power periods, EVs restore their energy from the grid [ 23 , 24 ].

2.4 Resources of electricity

Solar and wind plants are the most mature renewable-energy sources in modern grids. Nowadays, many buildings have installed photovoltaic (PV) modules, thermal solar heaters or micro wind turbines. For smart homes, various functions can be supplied by solar energy besides generating electricity, such as a solar water heater (SWH), solar dryer and solar cooler [ 25 ]. Moreover, PV plants are cheap with low requirements of maintenance [ 26 ], whereas hot water produced by SWHs can be used in many home functions, such as washing and cooking, which increases the home-energy efficiency [ 27 ].

Energy storage may be considered as the cornerstone for any SHEMS. SHEMS are usually installed with energy-storage systems (ESSs) to manage their stored energy according to predefined objectives. Many energy-storage technologies are available in the power markets. Batteries and fuel cells are the most compatible energy-storage types of smart-home applications [ 28 ]. A fuel-cell structure is very similar to a battery. During the charging process, hydrogen fuel cells use electricity to produce hydrogen. Hydrogen feeds the fuel cell to create electricity during the discharging process. Fuel cells have relatively low efficiency compared to batteries. Fuel cells provide extra clean storage environments with the capability of storing extra hydrogen tanks. That perfectly matches isolated homes in remote areas [ 29 ].

Although wind energy is more economical for large-scale plants, it has a very limited market for micro wind turbines in homes. Typically, micro wind turbines require at least a wind speed of 2.7 m/s to generate minimum power, 25 m/s for rated power and 40 m/s for continuous generated power [ 30 ]. A micro wind turbine is relatively expensive, intermittent and needs special maintenance requirements and constraints compared to a solar plant [ 31 ].

Recently, biomass energy has been a promising renewable resource alternative for smart homes. Many pieces of research have recommended biomass energy for different types of buildings [ 32 ]. Heating is the main function of biomass in smart homes, as discussed in [ 33 , 34 ]. In addition, a biomass-fuelled generation system is examined for many buildings [ 35 , 36 ].

2.5 Communication schemes

Recently, communication systems are installed as built-in modules in smart homes. Both home users and grid operators will be able to monitor and control several home appliances in the near future to satisfy the optimum home-energy profile while preserving a comfortable lifestyle. Therefore, both wired and wireless communication schemes are utilized, which is known as a home area network (HAN), to cover remote-control signals as home occupants’ ones. Fig. 1 shows an example of a HAN that consists of Wi-Fi and cloud computing networks for both indoor and outdoor data exchange, respectively [ 37 , 38 ].

Energy-management systems for homes require three main components: the computational embedded controllers, the local-area network communication middleware and the transmission control protocol/internet protocol (TCP/IP) communication for wide-area integration with the utility company using wide-area network communication [ 37 ].

According to home characteristics, many wired communication schemes can be selected, such as power-line communication (PLC), inter-integrated circuit (I2C) and serial peripheral interface or wireless technologies such as Zigbee, Wi-Fi, radio-frequency identification (RFID) and the Internet of Things (IoT) to develop HANs. A few of the most common techniques will be discussed briefly in the following subsections [ 38 ].

PLC is a technique that uses power lines to transmit both power and data via the same cable to customers simultaneously. Such wired schemes provide fast communication with low interference of data. Moreover, PLC provides many communication terminals, as all power plugs can be used for data transferring. As all electrical home devices are connected by power cables, PLC can communicate with all these devices via the same cable.

PLC set-up has a low cost, as it uses pre-installed power cables with minimum hardware requirements. With a PLC communication scheme, home controllers can also be integrated easily with a high speed of data transfer. On the other hand, PLC has a high probability of data-signal attenuation. Furthermore, data signals suffer from electromagnetic interference of transmitted power signals.

2.5.2 Zigbee

Zigbee is a wireless communication technique [ 37–46 ]. Zigbee follows the IEEE 802.15.4 standard as a radio-frequency wireless communication scheme. It does not require any licenses for limited zones such as homes [ 37 ]. Also, Zigbee is a low-power-consuming technique. Therefore, it is suitable for basic home appliances, such as lighting, alarm systems and air conditioners [ 39 , 40 ]. Zigbee usually considers all home devices as slaves with a master coordinator/controller, which is known as a master–slave architecture.

Zigbee provides highly secured transferred data [ 38 , 41 ] with high reliability and capacity [ 42 ]. It also has self-organizing capabilities [ 42 ]. Conversely, Zigbee is relatively expensive due to special hardware requirements with low data-transfer rates. Moreover, Zigbee is not compatible with many other protocols, such as internet-supported protocols and Wi-Fi.

2.5.3 Wi-Fi technology

Wi-Fi is a wireless communication technique that follows the IEEE 802.11 standard. Wi-Fi provides high-rate data transfer that is compatible with many information-based devices such as computers, laptops, etc. [ 43 , 44 ].

Wi-Fi is a highly secured scheme with many of the familiar internet capabilities and low data-transfer delays (<3 ms) [ 45 ]. On the contrary, it is a relatively high-power-consuming scheme compared to Zigbee schemes [ 45 ]. Also, home devices can affect transmitted data signals by their emitted electromagnetic fields [ 46 ]. Wi-Fi can also suffer from interference from other communication protocols such as Zigbee and Bluetooth [ 43 ].

RFID is a wireless communication technique that conforms to the electronic product code protocol [ 47–52 ]. It can coincide with other communication schemes such as Wi-Fi and Zigbee. It can be utilized for a relatively widespread range of frequencies, from 120 kHz to 10 GHz. It also covers a wide range of distances, from 10 cm to 200 m [ 48 ]. Many researchers are investigating RFID home applications, such as energy-management systems [ 49 ], door locks [ 50 ] and lighting controls [ 51 ].

RFID operates on tags and reader-identification systems with a high data-transfer rate. Nevertheless, RFID has expensive chips with low bandwidth. The possibility of tag collision within the same zone decreases the accuracy of the RFID scheme.

This scheme connects home devices, users and grid operators via the internet to monitor and manage smart homes [ 6 , 38 , 53–65 ]. Consequently, the IoT and cloud computing have proven to be cheap, popular and easy services for smart homes. Moreover, IoT schemes are compatible with many other communication protocols, such as Zigbee, Bluetooth, etc., as listed in Table 1 . Internet hacking is the main problem with IoT schemes. System security and privacy are critical challenges for such internet-based schemes.

IoT protocols features

Today, building energy-management systems (BEMS) are utilized within residential, commercial, administration and industrial buildings. Moreover, the integration of variable renewable-energy sources with proper ESSs deployed in buildings represents an essential need for reliable, efficient BEMS.

For small-scale residential buildings or ‘homes’, BEMS should deal with variable uncertain load behaviours according to the home occupants’ desires and requirements, which is known as SHEMS. Throughout recent decades, many SHEMS have been presented and defined in many research studies.

In [ 66 ], SHEMS are defined as services that efficiently monitor and manage electricity generation, storage and consumption in smart houses. Nazabal et al. [ 67 ] include a collaborative exchange between smart homes and the utility as a main function of SHEMS. In [ 68 ], SHEMS are defined from the electrical-grid point of view as important tools that provide several benefits such as flattening the load curve, a reduction in peak demand and meeting the demand-side requirements.

3.1 Functions of SHEMS

Adaptive SHEMS are required to conserve power, especially with the increasing evolution in home loads. SHEMS should control both home appliances and available energy resources according to the real-time tariff and home user’s requirements [ 4 ]. Home-management schemes should provide an interface platform between home occupants and the home controller to readjust occasionally the load priority [ 5 ].

As shown in Fig. 2 , the majority of smart-home centres can be summarized as having five main functions [ 5 ], as follows:

Functions of SHEMS

(i) Monitoring: provides home residents with visual instantaneous information about the consumed power of different appliances and the status of several home parameters such as temperature, lights, etc. Furthermore, it can guide users to available alternatives for saving energy according to the existing operating modes of different home appliances.

(ii) Logging: collects and saves data pertaining to the amount of electricity consumed by each appliance, generated out of energy-conservation states. This functionality includes analysing the demand response for real-time prices.

(iii) Control: both direct and remote-control schemes can be implemented in smart homes. Different home appliances are controlled directly by SHEMS to match the home users’ desires, whereas other management functions are controlled remotely via cell phones or laptops, such as logging and controlling the power consumption of interruptible devices.

(iv) Management: the main function of SHEMS. It concerns the coordination between installed energy sources such as PV modules, micro wind turbines, energy storage and home appliances to optimize the total system efficiency and/or increase economic benefits.

(v) Alarms: SHEMS should respond to specific threats or faults by generating proper alarms according to fault locations, types, etc.

3.2 Economic analysis

Economic factors affecting home-management systems are classified into two classes. First, sizing costs include expanses of smart-home planning. Second, operating costs consist of bills of consumed energy. These costs depend mainly on the electrical tariff.

3.2.1 Sizing costs

These include capital, maintenance and replacement costs of smart-home infrastructures, such as PV systems, wind turbines, batteries/fuel cells and communication systems. In most previous SHEMS, such planning costs usually are not taken into consideration, as management schemes usually concern the daily operating costs only [ 69 ].

3.2.2 Operating costs

The electricity tariff is the main factor that gives an indication of the value of saving energy, according to the governmental authority; there are many types of tariffs, as follows [ 70–74 ]:

(i) Flat tariffs: the cost of consumed energy is constant regardless of the continuous change in the load. Load-rescheduling schemes do not affect the electricity bills in this scheme. Therefore, homeowners are not encouraged to rearrange their consumed energy, as they have no any economic benefits from managing the consumption of their appliances.

(ii) Block-rate tariffs: in this scheme, the monthly consumed energy price is classified into different categories. Each category has its own flat-rate price. Therefore, the main target of SHEMS is minimizing the total monthly consumed energy to avoid the risk of high-priced categories.

(iii) Seasonal tariffs: in this scheme, the total grid-demand load is changed significantly from one season to another. Therefore, the utility grid applies a high flat-rate tariff in high-demand seasons and vice versa. SHEMS should minimize the total consumption in such high-priced seasons and get the benefit of consumption in low-priced seasons.

(iv) Time-of-use (TOU) tariff: there are two or three predefined categories of tariffs daily in this scheme. First, a high-priced-hours tariff is applied during high-demand hours, which is known as a peak-hours tariff. Second, an off-peak-hours tariff is applied during low-demand hours with low prices for energy consumption. Sometimes, three levels of pricing are defined by the utility grid during the day, i.e. off-, middle- and high-peak costs, as discussed in [ 75 ]. SHEMS shift interruptible loads with low priority to off-peak hours to minimize the bill.

(v) Super peak TOU: this can be considered as a special case of the previously described TOU tariff but with a short peak-hours period of ~4 hours daily.

(vi) Critical peak pricing (CPP): the utility grid uses this tariff scheme during expected critical events of increasing the gap between generation and power demand. The price is increased exceptionally during these critical events by a constant predefined rate.

(vii) Variable peak pricing: this is a subcategory of the CPP tariff in which the exceptional increase in the tariff is variable. The utility grid informs consumers of the exceptional dynamic price increase according to its initial expectations.

(viii) Real-time pricing (RTP): the price is changing continuously during pre-identified intervals that range from several minutes to an hour. This tariff is the riskiest pricing scheme for homeowners. The electricity bill can increase significantly without a proper management system. SHEMS should communicate with grid utility and reschedule both home appliances, sources and energy storage continuously to minimize the total bill.

(viii) Peak-time rebates (PTRs): a proper price discount is considered for low-consumption loads during peak hours, which can be refunded later by the grid.

Depending on the electricity tariff, SHEMS complexity varies dramatically. In the case of using a flat-rate tariff, the algorithm becomes simpler, as one value is recorded for selling or buying the electricity. Tariffs may be published from the proper authority or predicted according to historical data. Prediction of the dynamic tariff is a main step in any SHEMS. Many time frames of tariff prediction are proposed that vary from hourly, daily or even a yearly prediction. Many optimization techniques with various objective functions are proposed to handle different features of both smart-home infrastructures and electricity tariffs, as will be discussed in the following section.

3.3 Pre-proposed SHEMS

Different SHEMS may be classified according to four features: operational planning of load-scheduling techniques, system objective functions, optimization techniques and smart-home model characteristics, as will be discussed in the following subsections.

3.3.1 Load-scheduling techniques

SHEMS concern the generation/load power balance to provide a comfortable lifestyle with the minimum possible costs. Scheduling loads according to their priority and the periods of renewable energy (solar, wind and EV state) can help in reducing the overall energy consumption daily. According to data collected by the management system, an initial load schedule is suggested daily to minimize the daily cost of consumed energy [ 76 ].

By using a proper optimal scheduling algorithm, electricity bills can be reduced by shifting loads from high-priced to low-priced intervals [ 77 , 78 ]. Many techniques have been proposed for home load scheduling, as will be discussed in the following subsections:

(i) Rule-based scheduling: in this algorithm, all home appliances and resources are connected to smart data-collector taps. By processing the collected data, different appliances are scheduled according to their priorities and based on the if/then rule. Also, some high-priority loads are supplied by home renewable sources/storage to maintain their function during predicted peak hours [ 79 , 80 ].

(ii) Artificial intelligence (AI): many AI controllers have been proposed for home load scheduling, such as artificial neural networks (ANNs), fuzzy logic (FL) and adaptive neural fuzzy inference systems (ANFISs). Table 2 compares between the three types of scheduling scheme based on AI.

Optimization techniques for load scheduling

3.3.2 Objective functions

(i) Single-objective techniques: in these schemes, only one criterion is minimized or maximized according to the home-user requirements. Several minimization objective functions were proposed, as follows:

lifetime degradation [ 47–49 ];

life-cycle costs [ 93 ];

gas emissions [ 94–96 ];

both active and reactive losses [ 97 , 98 ].

On the other hand, some research defined other single maximizing objective functions, such as:

net present value [ 96 ].

economic profits [ 97 , 98 ].

increased system reliability: according to many well-known reliability indices, such as loss of power supply probability, loss of load probability and others [ 99 , 100 ].

generated power [ 101 , 102 ].

loadability [ 103 ];

Multi-objective techniques: homeowners may have several criteria to be optimized together. Multi-objective optimization (MOO) problems consider many functions simultaneously. MOO finds a proper coordination that moderately satisfies the considered objectives. In [ 102 ], SHEMS with MOO techniques are summarized. Table 3 lists some examples of such multi-objective functions.

Multi-objective functions of SHEMS

3.3.3 Optimization techniques

Optimization techniques aim usually to identify the best coordination taking into consideration predefined constraints. Many approaches are available for addressing optimization problems. These approaches can be classified into two categories: classical and AI-based techniques. Table 4 lists various SHEMS optimization techniques and their main features.

Optimization techniques in SHEMS

Classical methods, especially linear programming types, have been usually applied in the last decade for smart homes with limited objective functions and simple model characteristics of tariff and home appliances. Recently, AI-based techniques have been proposed to cover more complicated models of smart homes with multi-objective functions with high levels of comfortable lifestyles.

3.3.4 Home-model characteristics

The smart-home model differs significantly according to three factors: installed variable energy sources, applied tariff and EV deployment. PV systems have been applied for nearly all studied smart homes due to their low price, simplicity of installation, low maintenance requirements and easily predicted daily power profile. On the other hand, a few pieces of research have considered micro wind turbines in their home models, such as [ 120 ]. Wind turbines are limited by high-wind-speed zones that are usually located in rural areas. In addition, homeowners usually do not prefer wind turbines due to their high prices, mechanical maintenance requirements and the unpredictable variation in wind power.

Dynamic tariffs are applied in most smart-home research. Specifically, the TOU tariff is analysed in a lot of studies, such as [ 121 , 122 ], whereas little research uses RTP, such as [ 123 , 124 ]. EV is studied as an energy source in the parking period or vehicle-to-grid (V2G) mode. In [ 75 , 125 ], EV in V2G mode reduces the electricity bill in peak hours, whereas, in [ 126–130 ], ESSs are managed only to reduce the electricity usage from the grid.

Many technical challenges arise for modern grids due to the increasing mutual exchange between smart homes and utility grids, especially power-quality control. Electric-power-quality studies usually confirm the acceptable behaviour of electrical sources such as voltage limits and harmonics analysis. Recently, smart power grids have diverse generation sources from different technologies that depend mainly on power electronics devices that increase the difficulty in power-quality control. Power-quality constraints should be taken into consideration for any energy-management systems to provide harmony between modern sources and loads.

On the other hand, power-quality issues should not form an additional obstacle against the integration of new technologies in modern grids. Therefore, both advanced communication schemes and AI-based techniques make modern grids ‘smart’ enough to cope with selective power-quality management. Smart homes exchange power with utility grids. With the prospective increase in such smart homes, the effect of their behaviour should be studied and controlled. Smart homes affect the grid-power quality in three different areas, as will be discussed in the following paragraphs [ 154–156 ].

4.1 Generating equipment

Integrated micro generation schemes in smart homes are mainly single-phase sources based on inverters with high switching frequencies that reach to many kHz. Low-order harmonics of such a generation type can usually be disregarded. However, with the expected continuous increase in such micro generators, the harmonics of low-voltage networks may shift into a range of higher frequencies, perhaps from 2 to 9 kHz [ 157 ]. Therefore, more research is needed to re-evaluate the appropriate limits for generation equipment in smart homes. Moreover, single-phase generation increases the risk of an unbalanced voltage in low-voltage grids. Therefore, negative-sequence voltage limits should be re-evaluated particularly for weak distribution networks. Also, a need for zero-sequence voltage limits may arise [ 154 ].

4.2 Home appliances

Modern home appliances depend mainly on electronic devices, such as newer LED lighting systems, EV battery chargers, etc., with relatively low fundamental current and high harmonic contents compared to traditional ones. According to many power-system analysers, many harmonics will increase significantly to risky levels, particularly fifth-harmonic voltage, with increase in such new electronic appliances [ 155 ].

4.3 Distribution network

In future grids, significant unusual operating scenarios may be possible with high penetration of domestic generation, especially with the possibility of an islanded (self-balanced) operation of smart homes. Short-circuit power will differ significantly during different operating conditions compared to classical grids. Moreover, low-voltage networks may suffer from damping-stability problems due to the continuous decrease in resistive loads, in conjunction with the increase in capacitive loads of electronic equipment. In addition, resonance problems may occur with low frequencies according to the continuous change in the nature of the load [ 156 ].

Although smart homes have bad impacts on utility grids, there are no charges applied from the grid authority to homeowners based on their buildings’ effects on grid-power quality. Therefore, home planners and SHEMS designers are usually concerned only with the economic benefits of their proposed schemes.

Smart homes, using new revolutions in communication systems and AI, provide residential houses with electrical power of a dual nature, i.e. as producer and consumer or ‘prosumer’. The energy-management system includes many components that mainly depend on a suitable communication scheme to coordinate between available sources, loads and users’ desire. Among many proposed communication systems, the IoT has many advantages and was chosen in many studies. Besides the popularity of the IoT, it does not need any special equipment installation and is compatible with many other communications protocols.

Many functions are applied by management systems such as monitoring and logging to facilitate a proper interaction between home occupants and the management scheme. Home security also should be confirmed via the management scheme by using different alarms corresponding to preset threats. Home users control different home appliances according their desires by SHEMS and via cell phones or manually.

The electricity tariff plays an important role in defining management-system characteristics. Tariffs vary from simple fixed flat rates to complicated variable dynamic ones according to the electrical-grid authority’s rules for residential loads. According to the tariff and selected objective functions, pre-proposed optimization techniques vary significantly from simple classical linear programming to sophisticated AI ones.

Modern electronic-based home appliances increase power-grid-quality problems, such as high harmonic contents, unbalanced loading and unpredictable short-circuit currents. On the other hand, power-grid authorities do not charge homeowners according to their buildings’ effects on the power quality. Therefore, all proposed energy-management systems are concerned mainly with the economic profits from reducing electricity consumption or even selling electrical power to the utility grids. In the future, price-based power-quality constraints should be defined by the grid authorities to confirm proper power exchange between both smart homes and grids. A possible future direction is behaviour modelling of aggregated smart homes/smart cities in different operating scenarios to conclude probable power-grid scenarios for stability and quality.

This work was supported by the project entitled ‘Smart Homes Energy Management Strategies’, Project ID: 4915, JESOR-2015-Cycle 4, which is sponsored by the Egyptian Academy of Scientific Research and Technology (ASRT), Cairo, Egypt.

None declared.

Comité Européen de Normalisation . Energy Performance of Buildings—Impact of Building Automation, Control, and Building Management; European Technical Standard EN 15232 ; CEN: Brussels, Belgium . 2012 . http://www.cres.gr/greenbuilding/PDF/prend/set4/WI_22_TC-approval_version_prEN_15232_Integrated_Building_Automation_Systems.pdf ( 20 December 2020 , date last accessed).

European Parliament . Directive 2010/31/EU of the European Parliament and of the Council of 19 May 2010 on the Energy Performance of Buildings; Directive 2010/31/EU ; The European Parliament and the Council of the European Union: Brussels, Belgium . 2010 . https://eurlex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2010:153:0013:0035:en:PDF ( 20 December 2020 , date last accessed).

European Climate Foundation . Roadmap 2050 Project .   http://www.roadmap2050.eu/ ( 20 December 2020 , date last accessed).

Al-Ali   AR , El-Hag   A , Bahadiri   M , et al.    Smart home renewable energy management system . Energy Procedia , 2011 , 12 : 120 – 126 .

Google Scholar

van Dam   S , Bakker   CA , Buiter   J . Do home energy management systems make sense? Assessing their overall lifecycle impact . Energy Policy,   2013 , 63 : 398 – 407 .

Kuzlu   M , Pipattanasomporn   M , Rahman   S . Review of communication technologies for smart homes/building applications. In: 2015 IEEE Innovative Smart Grid Technologies—Asia (ISGT ASIA) , Bangkok, Thailand , 3–6 November 2015 , 1 – 6 .

Wilson   C , Hargreaves   T , Hauxwell-Baldwin   R . Smart homes and their users: a systematic analysis and key challenges . Personal and Ubiquitous Computing , 2015 , 19 : 463 – 476 .

Schieweck   A , Uhde   E , Salthammer   T , et al. .   Smart homes and the control of indoor air quality . Renewable and Sustainable Energy Reviews,   2018 , 94 : 705 – 718 .

Nacer   A , Marhic   B , Delahoche   L . Smart home, smart HEMS, smart heating: an overview of the latest products and trends. In: 2017 6th International Conference on Systems and Control (ICSC) , Batna, Algeria , 7–9 May 2017 , 90 – 95 .

Yuan   X , Han   P , Duan   Y , et al. .   Residential electrical load monitoring and modeling—state of the art and future trends for smart homes and grids . Electric Power Components and Systems , 2020 , 48 : 1125 – 1143 .

Wang   Y , Chen   Q , Hong   H , et al. .   Review of smart meter data analytics: applications, methodologies, and challenges . IEEE Transactions on Smart Grid , 2019 , 10 : 3125 – 3148 .

Sowah   RA , Ofoli   AR , Tetteh   MK , et al. .   Demand side management of smart homes using OpenHAB framework for interoperability of devices. In: 2018 IEEE 7th International Conference on Adaptive Science & Technology (ICAST) , Accra, Ghana , 22–24 August 2018 , 1 – 8 .

Robles   RJ , Kim   T . Applications, systems and methods in smart home technology: a review . Int. J. Adv. Sci. Technol. , 2010 , 15 : 37 – 48 .

Makhadmeh   SN , Khader   AT , Al-Betar   MA , et al. .   Optimization methods for power scheduling problems in smart home: survey . Renewable and Sustainable Energy Reviews , 2019 , 115 : 109362 .

Zhao   Z , Lee   WC , Shin   Y , et al. .   An optimal power scheduling method for demand response in home energy management system . IEEE Transactions on Smart Grid , 2013 , 4 : 1391 – 1400 .

Benzi   F , Anglani   N , Bassi   E , et al. .   Electricity smart meters interfacing the households . IEEE Transactions on Industrial Electronics , 2011 , 58 : 4487 – 4494 .

Depuru   SR , Wang   L , Devabhaktuni   V , et al. .   Smart meters for power grid — challenges, issues, advantages and status. In: 2011 IEEE/PES Power Systems Conference and Exposition , Phoenix, AZ (USA) , 20–23 March 2011 , 1 – 7 .

Zheng   J , Gao   W , Lin   L . Smart meters in smart grid: an overview. In: IEEE Green Technologies Conference , Denver, Colorado, USA , 4–5 April 2013 , 57 – 64 .

Chavali   P , Yang   P , Nehorai   A . A distributed algorithm of appliance scheduling for home energy management system . IEEE Transactions on Smart Grid , 2014 , 5 : 282 – 290 .

Ma   Y , Houghton   T , Cruden   A , et al. .   Modeling the benefits of vehicle-to-grid technology to a power system . IEEE Transactions on power systems , 2012 , 27 : 1012 – 1020 .

Yiyun   T , Can   L , Lin   C , et al. .   Research on vehicle-to-grid technology. In: International Conference on Computer Distributed Control and Intelligent Environmental Monitoring , Changsha, Hunan, China , 19–20 February 2011 , 1013 – 1016 .

Kempton   W , Tomić   J . Vehicle-to-grid power fundamentals: calculating capacity and net revenue . Journal of Power Sources , 2005 , 144 : 268 – 279 .

Makhadmeh   SN , Khader   AT , Al-Betar   MA , et al. .   A novel hybrid grey wolf optimizer with min-conflict algorithm for power scheduling problem in a smart home . Swarm and Evolutionary Computation , 2021 , 60 : 100793 .

Makhadmeh   SN , Khader   AT , Al-Betar   MA , et al. .   Multi-objective power scheduling problem in smart homes using grey wolf optimizer . Journal of Ambient Intelligence and Humanized Computing , 2019 , 10 : 3643 – 3667 .

Parida   B , Iniyan   S , Goic   R . A review of solar photovoltaic technologies . Renewable and Sustainable Energy Reviews , 2011 , 15 : 1625 – 1636 .

Schwerin   A . Analysis of the potential solar energy market in the Caribbean . REPOSITORIO NACIONAL CONACYT . 2010 . https://www.solarthermalworld.org/sites/default/files/story/2015-04-06/solar_market_analysis_caribbean.pdf ( 20 December 2020 , date last accessed).

Brown   CE.   World Energy Resources . Berlin : Springer Verlag , 2002 .

Google Preview

Wan   C , Zhao   J , Song   Y , et al. .   Photovoltaic and solar power forecasting for smart grid energy management . CSEE Journal of Power and Energy Systems , 2015 , 1 : 38 – 46 .

Ewais   AM , El-Azab   R , Adma   MAA . Stand-alone microgrid energy storage schemes: comparative study. In: 2018 Twentieth International Middle East Power Systems Conference (MEPCON) , Cairo, Egypt , 18–20 December 2018 , 564 – 569 .

Fthenakis   V , Kim   HC . Land use and electricity generation: a life-cycle analysis . Renewable and Sustainable Energy Reviews , 2009 , 13 : 1465 – 1474 .

Lei   M , Shiyan   L , Chuanwen   J , et al. .   A review on the forecasting of wind speed and generated power . Renewable and Sustainable Energy Reviews , 2009 , 13 : 915 – 920 .

Mazen   R , Radwan   M , Abdel-Samiea   M . Utilization of biomass energy in high-rise buildings. In: 2013 4th International Youth Conference on Energy (IYCE) , Siófok, Hungary , 6–8 June 2013 , 1 – 3 .

Carpio   M , Zamorano   M , Costa   M . Impact of using biomass boilers on the energy rating and CO 2 emissions of Iberian Peninsula residential buildings . Energy and Buildings , 2013 , 66 : 732 – 744 .

Berković-Šubić   M , Rauch   M , Dović   D , et al. .   Primary energy consumption of the dwelling with solar hot water system and biomass boiler . Energy Conversion and Management , 2014 , 87 : 1151 – 1161 .

Huang   Y , Wang   Y , Rezvani   S , et al. .   Biomass fuelled trigeneration system in selected buildings . Energy Conversion and Management , 2011 , 52 : 2448 – 2454 .

Wu   G , Yu   X . Remote control system for energy efficient home. In: 2013 IEEE Energytech , Cleveland, OH, USA , 21–23 May 2013 , 1 – 5 .

Safaric   S , Malaric   K . ZigBee wireless standard. In: Proceedings ELMAR 2006 , Zadar, Croatia , 7–10 June 2006 , 259 – 262 .

Huq   MZ , Islam   S . Home Area Network technology assessment for demand response in smart grid environment. In: 2010 20th Australasian Universities Power Engineering Conference , Christchurch, New Zealand , 5–8 December 2010 , 1 – 6 .

Dou   N , Mei   Y , Yanjuan   Z , Yan   Z . The networking technology within smart home system—ZigBee technology. In: 2009 International Forum on Computer Science-Technology and Applications , Chongqing, China , 25–27 December 2009 , 29 – 33 .

Ajgaonkar   P , Wang   L , Alam   M . Simulation studies on ZigBee communications for home automation and networking. In: 2010 IEEE AUTOTESTCON , Orlando, FL, USA , 13–16 September 2010 , 1 – 6 .

Li   Y , Zhang   K , Zhang   X . Research on application of ZigBee technology in flammable and explosive environment . In: 2009 First International Conference on Information Science and Engineering , Nanjing, China , 26–28 December 2009 , 4074 – 4078 .

Khan   SA , Khan   FA , Shahid   A , et al. .   Zigbee based reconfigurable clustered home area network. In: 2009 Third International Conference on Sensor Technologies and Applications , Athens, Greece , 18–23 June 2009 , 32 – 37 .

Han   T , Han   B , Zhang   L , et al. .   Coexistence study for WiFi and ZigBee under smart home scenarios. In: 2012 3rd IEEE International Conference on Network Infrastructure and Digital Content , Beijing, China , 21–23 September 2012 , 669 – 674 .

Sarijari   MA , Lo   A , Abdullah   MS , et al. .   Coexistence of heterogeneous and homogeneous wireless technologies in smart grid-home area network. In: 2013 International Conference on Parallel and Distributed Systems , Seoul, South Korea , 15–18 December 2013 , 576 – 581 .

Kounev   V , Tipper   D . Advanced metering and demand response communication performance in Zigbee based HANs. In: 2013 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) , Turin, Italy , 14–19 April 2013 , 31 – 36 .

Batista   NC , Melício   R , Matias   JCO , Catalão   JPS . ZigBee wireless area network for home automation and energy management: field trials and installation approaches. In: 2012 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe) , Berlin, Germany , 14–17 October 2012 , 1 – 5 .

Salazar   J.   Wireless Networks . 1st edn. Prague : Czech Technical University of Prague Faculty of electrical engineering , 2017 .

Phillips   T , Karygiannis   T , Kuhn   R . Security standards for the RFID market . IEEE Security & Privacy , 2005 , 3 : 85 – 89 .

Li   Y . Design of a key establishment protocol for smart home energy management system. In: 2013 Fifth International Conference on Computational Intelligence, Communication Systems and Networks , Madrid, Spain , 5–7 June 2013 , 88 – 93 .

Park   YT , Sthapit   P , Pyun   J . Smart digital door lock for the home automation. In: TENCON 2009—2009 IEEE Region 10 Conference , Singapore , 23–26 November 2009 , 1 – 6 .

Hussain   S , Schaffner   S , Moseychuck   D . Applications of wireless sensor networks and RFID in a smart home environment. In: 2009 Seventh Annual Communication Networks and Services Research Conference , Moncton, NB, Canada , 11–13 May 2009 , 153 – 157 .

Yao   W , Chu   C , Li   Z . The use of RFID in healthcare: benefits and barriers. In: 2010 IEEE International Conference on RFID-Technology and Applications , Guangzhou, China , 17–19 June 2010 , 128 – 134 .

Wang   C , Li   X , Liu   Y , Wang   H . The research on development direction and points in IoT in China power grid. In: 2014 International Conference on Information Science, Electronics and Electrical Engineering , Sapporo, Japan , 2–4 July 2014 , 245 – 248 .

Chettri   L , Bera   R . A comprehensive survey on Internet of Things (IoT) toward 5G wireless systems . IEEE Internet of Things Journal , 2020 , 7 : 16 – 32 .

Samuel   SSI . A review of connectivity challenges in IoT-smart home. In: 2016 3rd MEC International Conference on Big Data and Smart City (ICBDSC) , Muscat, Oman , 15–16 March 2016 , 1 – 4 .

Withanage   C , Ashok   R , Yuen   C , Otto   K . A comparison of the popular home automation technologies. In: 2014 IEEE Innovative Smart Grid Technologies—Asia (ISGT ASIA) , Kuala Lumpur, Malaysia , 20–23 May 2014 , 600 – 605 .

Zhu   YH , Qiu   S , Chi   K , Fang   Y . Latency aware IPv6 packet delivery scheme over IEEE 802.15.4 based battery-free wireless sensor networks . IEEE Transactions on Mobile Computing , 2017 , 16 : 1691 – 1704 .

Yi   P , Iwayemi   A , Zhou   C . Developing Zigbee deployment guideline under Wi-Fi interference for smart grid applications . IEEE Transactions on Smart Grid , 2011 , 2 : 110 – 120 .

Mincov   N , Zanatta   L , Lopes   JA , Lôbo   M . Communication technologies for differentiated smart grid environments. In: 2013 IEEE PES Conference on Innovative Smart Grid Technologies (ISGT Latin America) , Sao Paulo, Brazil , 24–27 February 2013 , 1 – 4 .

Chhaya   L , Sharma   P , Bhagwatikar   G , Kumar   A . Wireless sensor network based smart grid communications: cyber attacks, intrusion detection system and topology control . Electronics , 2017 , 6 : 5 .

Gungor   VC , Hancke   GP . Industrial wireless sensor networks: challenges, design principles, and technical approaches . IEEE Transactions on Industrial Electronics , 2009 , 56 : 4258 – 4265 .

Liu   Y . Wireless sensor network applications in smart grid: recent trends and challenges . International Journal of Distributed Sensor Networks , 2012 , 8 : 492819 .

Al-Sarawi   S , Anbar   M , Alieyan   K , Alzubaidi   M . Internet of Things (IoT) communication protocols: review. In: 2017 8th International Conference on Information Technology (ICIT) , Amman, Jordan , 17–18 May 2017 , 685 – 690 .

Li   Y , Cheng   X , Cao   Y , et al.    Smart choice for the smart grid: narrowband Internet of Things (NB-IoT) . IEEE Internet of Things Journal , 2018 , 5 : 1505 – 1515 .

Ratasuk   RR , Vejlgaard   B , Mangalvedhe   N , Ghosh   A . NB-IoT system for M2M communication. In: 2016 IEEE Wireless Communications and Networking Conference Workshops (WCNCW) , Doha, Qatar , 3–6 April 2016 , 428 – 432 .

Son   Y , Moon   K . Home energy management system based on power line communication. In: 2010 Digest of Technical Papers International Conference on Consumer Electronics (ICCE) , Las Vegas, NV, USA , 9–13 January 2010 , 115 – 116 .

Nazabal   JA , Falcone   FJ , Fernandez-Valdivielso   C , et al.    Energy Management System proposal for efficient smart homes. In: 2013 International Conference on New Concepts in Smart Cities: Fostering Public and Private Alliances (SmartMILE) , Gijon, Spain , 11–13 December 2013 , 1 – 5 .

Boynuegri   AR , Yagcitekin   B , Baysal   M , et al.    Energy management algorithm for smart home with renewable energy sources. In: 4th International Conference on Power Engineering, Energy and Electrical Drives , Istanbul, Turkey , 13–17 May 2013 , 17531758 .

Torriti   J . Price-based demand side management: assessing the impacts of time-of-use tariffs on residential electricity demand and peak shifting in Northern Italy . Energy , 2012 , 44 : 576 – 583 .

Asadinejad   A , Tomsovic   K . Optimal use of incentive and price based demand response to reduce costs and price volatility . Electric Power Systems Research , 2017 , 144 : 215 – 223 .

Hart   DG . Using AMI to realize the Smart Grid. In: 2008 IEEE Power and Energy Society General Meeting-Conversion and Delivery of Electrical Energy in the 21st Century , Pittsburgh, PA, USA , 20–24 July 2008 , 1 – 2 .

Aigner   DJ , Leamer   EE . Estimation of time-of-use pricing response in the absence of experimental data: an application of the methodology of data transferability . Journal of Econometrics , 1984 , 26 : 205 – 222 .

de Sá Ferreira   R , Barroso   LA , Lino   PR , et al.    Time-of-use tariff design under uncertainty in price-elasticities of electricity demand: a stochastic optimization approach . IEEE Transactions on Smart Grid , 2013 , 4 : 2285 – 2295 .

Dutta   G , Mitra   K . A literature review on dynamic pricing of electricity . J Oper Res Soc , 2017 , 68 : 1131 – 1145 .

Erdinc   O , Paterakis   NG , Mendes   TDP , et al.    Smart household operation considering Bi-directional EV and ESS utilization by real-time pricing-based DR . IEEE Transactions on Smart Grid , 2015 , 6 : 1281 – 1291 .

Marzband   M , Alavi   H , Ghazimirsaeid   S , et al.    Optimal energy management system based on stochastic approach for a home microgrid with integrated responsive load demand and energy storage . Sustainable Cities and Society , 2017 , 28 : 256 – 264 .

Patel   K , Khosla   A . Home energy management systems in future smart grid networks: a systematic review. In: 2015 1st International Conference on Next Generation Computing Technologies (NGCT) , Dehradun, India , 4–5 September 2015 , 479 – 483 .

Zhou   B , Li   W , Chan   KW , et al.    Smart home energy management systems: concept, configurations, and scheduling strategies . Renewable and Sustainable Energy Reviews , 2016 , 61 : 30 – 40 .

Kawakami   T , Yoshihisa   T , Fujita   N , Tsukamoto   M . A rule-based home energy management system using the Rete algorithm. In: 2013 IEEE 2nd Global Conference on Consumer Electronics (GCCE) , Tokyo, Japan , 1–4 October 2013 , 162 – 163 .

Yoshihisa   T , Fujita   N , Tsukamoto   M . A rule generation method for electrical appliances management systems with home EoD. In: The 1st IEEE Global Conference on Consumer Electronics , Tokyo, Japan , 2–5 October 2012 , 248 – 250 .

Yuce   B , Rezgui   Y , Mourshed   M . ANN–GA smart appliance scheduling for optimised energy management in the domestic sector . Energy and Buildings , 2016 , 111 : 311 – 325 .

Gharghan   SK , Nordin   R , Ismail   M , Abd Ali   J . Accurate wireless sensor localization technique based on hybrid PSO-ANN algorithm for indoor and outdoor track cycling . IEEE Sensors Journal , 2015 , 16 : 529 – 541 .

Ahmed   MS , Mohamed   A , Homod   RZ , Shareef   H . Hybrid LSA-ANN based home energy management scheduling controller for residential demand response strategy . Energies , 2016 , 9 : 716 .

Onder   Z , Sezer   S , Çanak   I . A Tauberian theorem for the weighted mean method of summability of sequences of fuzzy numbers . Journal of Intelligent and Fuzzy Systems , 2015 , 28 : 1403 – 1409 .

Wu   Y , Zhang   B , Lu   J , et al.    Fuzzy logic and neuro-fuzzy systems: a systematic introduction . International Journal of Artificial Intelligence and Expert Systems , 2011 , 2 : 2011 – 2047 .

Hong   YY , Lin   JK , Wu   CP , Chuang   CC . Multi-objective air-conditioning control considering fuzzy parameters using immune clonal selection programming . IEEE Transactions on Smart Grid , 2012 , 3 : 1603 – 1610 .

Mohsenzadeh   A , Shariatkhah   M , Haghifam   MR . Applying fuzzy techniques to model customer comfort in a smart home control system. In: 22nd International Conference and Exhibition on Electricity Distribution (CIRED 2013) , Stockholm, Sweden , 10–13 June 2013 , 1 – 4 .

Ciabattoni   L , Grisostomi   M , Ippoliti   G , et al.    Home energy management benefits evaluation through fuzzy logic consumptions simulator. In: Proceedings of the International Joint Conference on Neural Networks . Beijing, China , 6–11 July 2014 .

Wu   Z , Zhou   S , Li   J , et al.    Real-time scheduling of residential appliances via conditional risk-at-value . IEEE Transactions on Smart Grid , 2014 , 5 : 1282 – 1291 .

Ainsworth   N , Johnson   B , Lundstrom   B . A fuzzy-logic subsumption controller for Home Energy Management Systems. In: 2015 North American Power Symposium (NAPS) , Charlotte, NC, USA , 4–6 October 2015 , 1 – 7 .

Wu   Z , Zhang   XP , Brandt   J ,   et al . Three control approaches for optimized energy flow with home energy management system . IEEE Power and Energy Technology Systems Journal , 2015 , 2 : 21 – 31 .

Choi   IH , Yoo   SH , Jung   JH , et al.    Design of Neuro-Fuzzy based intelligent inference algorithm for energy management system with legacy device . The Transactions of the Korean Institute of Electrical Engineers , 2015 , 64 : 779 – 785 .

Cao   T , Hwang   Y , Radermacher   R . Development of an optimization based design framework for microgrid energy systems . Energy , 2017 , 140 : 340 – 351 .

Kanchev   H , Francois   B , Lazarov   V . Unit commitment by dynamic programming for microgrid operational planning optimization and emission reduction. In: International Aegean Conference on Electrical Machines and Power Electronics and Electromotion, Joint Conference , Istanbul, Turkey , 8–10 September 2011 , 502 – 507 .

Tsikalakis   AG , Hatziargyriou   ND . Environmental benefits of distributed generation with and without emissions trading . Energy Policy , 2007 , 35 : 3395 – 3409 .

Moradi   M , Eskandari   M , Showkati   H . A hybrid method for simultaneous optimization of DG capacity and operational strategy in microgrids utilizing renewable energy resources . International Journal of Electrical Power & Energy Systems , 2014 , 56 : 241 – 258 .

Kirthiga   MV , Daniel   SA , Gurunathan   S . A methodology for transforming an existing distribution network into a sustainable autonomous micro-grid . IEEE Transactions on Sustainable Energy , 2012 , 4 : 31 – 41 .

Abou El-Ela   AA , Allam   SM , Shatla   MM . Maximal optimal benefits of distributed generation using genetic algorithms . Electric power systems research , 2010 , 80 : 869 – 877 .

Oudalov   A , Cherkaoui   R , Beguin   A . Sizing and optimal operation of battery energy storage system for peak shaving application. In: 2007 IEEE Lausanne Power Tech 2007 , Lausanne, Switzerland , 1–5 July 2007 , 621 – 625 .

Arefifar   SA , Mohamed   Y . DG mix, reactive sources and energy storage units for optimizing microgrid reliability and supply security . IEEE Transactions on smart grid , 2014 , 5 : 1835 – 1844 .

Luna   R , Perea   M , Vargas-Vázquez   D , et al.    Optimal sizing of renewable hybrids energy systems: a review of methodologies . Solar Energy , 2012 , 86 : 1077 – 1088 .

Iqbal   M , Azam   M , Naeem   M , et al.    Optimization classification, algorithms and tools for renewable energy: a review . Renewable and Sustainable Energy Reviews , 2014 , 39 : 640 – 654 .

Aman   MM , Jasmon   GB , Bakar   AH , Mokhlis   H . A new approach for optimum simultaneous multi-DG distributed generation Units placement and sizing based on maximization of system loadability using HPSO (hybrid particle swarm optimization) algorithm . Energy , 2014 , 66 : 202 – 215 .

Schwaegerl   C , Tao   L , Mancarella   P ,   et al . A multi-objective optimization approach for assessment of technical, commercial and environmental performance of microgrids . European Transactions on Electrical Power , 2011 , 21 : 1271 – 1290 .

Boloukat   MH , Foroud   AA . Stochastic-based resource expansion planning for a grid-connected microgrid using interval linear programming . Energy , 2016 , 113 : 776 – 787 .

Zhao   M , Chen   Z , Blaabjerg   F . Optimisation of electrical system for offshore wind farms via genetic algorithm . IET Renewable Power Generation , 2009 , 3 : 205 – 216 .

Lin   N , Zhou   B , Wang   X . Optimal placement of distributed generators in micro-grid. In: 2011 International Conference on Consumer Electronics, Communications and Networks (CECNet) , Xianning, China , 16–18 April 2011 , 4239 – 4242 .

Zhang   D , Evangelisti   S , Lettieri   P , et al.    Economic and environmental scheduling of smart homes with microgrid: DER operation and electrical tasks . Energy Conversion and Management , 2016 , 110 : 113 – 124 .

Dey   B , Roy   SK , Bhattacharyya   B . Solving multi-objective economic emission dispatch of a renewable integrated microgrid using latest bio-inspired algorithms . Engineering Science and Technology, an International Journal , 2019 , 22 : 55 – 66 .

Zhao   B , Zhang   X , Chen   J , et al.    Operation optimization of standalone microgrids considering lifetime characteristics of battery energy storage system . IEEE Transactions on Sustainable Energy , 2013 , 4 : 934 – 943 .

Pourmousavi   SA , Sharma   RK , Asghari   B . A framework for real-time power management of a grid-tied microgrid to extend battery lifetime and reduce cost of energy. In: 2012 IEEE PES Innovative Smart Grid Technologies (ISGT) , Washington, DC, USA , 16–20 Jan. 2012 , 1 – 8 .

Shadmand   MB , Balog   RS . Multi-objective optimization and design of photovoltaic-wind hybrid system for community smart DC microgrid . IEEE Transactions on Smart Grid , 2014 , 5 : 2635 – 2643 .

Zhang   M , Chen   J . The energy management and optimized operation of electric vehicles based on microgrid . IEEE Transactions on Power Delivery , 2014 , 29 : 1427 – 1435 .

Shi   L , Luo   Y , Tu   GY . Bidding strategy of microgrid with consideration of uncertainty for participating in power market . International Journal of Electrical Power & Energy Systems , 2014 , 59 : 1 – 13 .

Alsayed   M , Cacciato   M , Scelba   G ,   et al . Optimal sizing of hybrid power generation systems based on multi criteria decision analysis. In: International Symposium on Power Electronics Power Electronics, Electrical Drives, Automation and Motion , Sorrento, Italy , 20–22 June 2012 , 1442 – 1447 .

Kaabeche   A , Belhamel   M , Ibtiouen   R . Sizing optimization of grid-independent hybrid photovoltaic/wind power generation system . Energy , 2011 , 36 : 1214 – 1222 .

Arnette   A , Zobel   C . An optimization model for regional renewable energy development . Renewable and Sustainable Energy Reviews , 2012 , 16 : 4606 – 4615 .

Alsayed   M , Cacciato   M , Scarcella   G , et al.    Multicriteria optimal sizing of photovoltaic-wind turbine grid connected systems . IEEE Transactions on Energy Conversion , 2013 , 28 : 370 – 379 .

Bahramirad   S , Reder   W , Khodaei   A . Reliability-constrained optimal sizing of energy storage system in a microgrid . IEEE Transactions on Smart Grid , 2012 , 3 : 2056 – 2062 .

Taşcıkaraoğlu   A , Boynuegri   AR , Uzunoglu   M . A demand side management strategy based on forecasting of residential renewable sources: a smart home system in Turkey . Energy and Buildings , 2014 , 80 : 309 – 320 .

Erdinc   O , Paterakis   NG , Pappi   IN ,   et al . A new perspective for sizing of distributed generation and energy storage for smart households under demand response . Applied Energy , 2015 , 143 : 26 – 37 .

Geng   L , Wei   Y , Lu   Z , et al.    A novel model for home energy management system based on Internet of Things. In: 2016 IEEE International Conference on Power and Renewable Energy (ICPRE) , Shanghai, China , 21–23 October 2016 , 474 – 480 .

Lee   JY , Choi   SG . Linear programming based hourly peak load shaving method at home area. In: 16th International Conference on Advanced Communication Technology , Pyeongchang, South Korea , 16–19 February 2014 , 310 – 313 .

Erdinc   O , Paterakis   NG , Catalão   JPS , et al.    Smart households and home energy management systems with innovative sizing of distributed generation and storage for customers. In: 2015 48th Hawaii International Conference on System Sciences , Kauai, HI, USA , 5–8 January 2015 , 1462 – 1471 .

Brahman   F , Honarmand   M , Jadid   S . Optimal electrical and thermal energy management of a residential energy hub, integrating demand response and energy storage system . Energy and Buildings , 2015 , 90 : 65 – 75 .

Asare-Bediako   B , Kling   WL , Ribeiro   PF . Integrated agent-based home energy management system for smart grids applications. In: IEEE PES ISGT Europe 2013 , Lyngby, Denmark , 6–9 October 2013 , 1 – 5 .

Hubert   T , Grijalva   S . Modeling for residential electricity optimization in dynamic pricing environments . IEEE Transactions on Smart Grid , 2012 , 3 : 2224 – 2231 .

Paterakis   NG , Erdinç   O , Bakirtzis   AG , et al.    Optimal household appliances scheduling under day-ahead pricing and load-shaping demand response strategies . IEEE Transactions on Industrial Informatics , 2015 , 11 : 1509 – 1519 .

Wang   J , Sun   Z , Zhou   Y ,   et al . Optimal dispatching model of smart home energy management system. In: IEEE PES Innovative Smart Grid Technologies , Tianjin, China , 21–24 May 2012 , 1 – 5 .

Jo   HC , Kim   S , Joo   SK . Smart heating and air conditioning scheduling method incorporating customer convenience for home energy management system . IEEE Transactions on Consumer Electronics , 2013 , 59 : 316 – 322 .

Rajasekharan   J , Koivunen   V . Optimal energy consumption model for smart grid households with energy storage . IEEE Journal of Selected Topics in Signal Processing , 2014 , 8 : 1154 – 1166 .

Xing   H , Lin   Z , Fu   M . A new decentralized algorithm for optimal load shifting via electric vehicles . In: 2017 36th Chinese Control Conference (CCC) , Dalian, China , 26–28 July 2017 , 10708 – 10713 .

Chen   Z , Xia   B , You   C , et al.    A novel energy management method for series plug-in hybrid electric vehicles . Applied Energy , 2015 , 145 : 172 – 179 .

Wu   X , Hu   X , Teng   Y , et al.    Optimal integration of a hybrid solar-battery power source into smart home nanogrid with plug-in electric vehicle . Journal of Power Sources , 2017 , 363 : 277 – 283 .

Hu   X , Martinez   CM , Yang   Y . Charging, power management, and battery degradation mitigation in plug-in hybrid electric vehicles: a unified cost-optimal approach . Mechanical Systems and Signal Processing , 2017 , 87 : 4 – 16 .

Henao-Muñoz   AC , Saavedra-Montes   AJ , Ramos-Paja   CA . Energy management system for an isolated microgrid with photovoltaic generation . In: 2017 14th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD) , Giardini Naxos, Greece , 12–15 June 2017 , 1 – 4 .

Misra   S , Bera   S , Ojha   T . D2P: distributed dynamic pricing policy in smart grid for PHEVs management . IEEE Transactions on Parallel and Distributed Systems , 2015 , 26 : 702 – 712 .

Setlhaolo   D , Xia   X . Combined residential demand side management strategies with coordination and economic analysis . International Journal of Electrical Power & Energy Systems , 2016 , 79 : 150 – 160 .

Akhavan-Rezai   E , Shaaban   MF , El-Saadany   EF ,   et al . New EMS to incorporate smart parking lots into demand response . IEEE Transactions on Smart Grid , 2016 , 9 : 1376 – 1386 .

Zhang   H , Hu   Z , Xu   Z , et al.    Evaluation of achievable vehicle-to-grid capacity using aggregate PEV model . IEEE Transactions on Power Systems , 2017 , 32 : 784 – 794 .

Su   W , Wang   J , Roh   J . Stochastic energy scheduling in microgrids with intermittent renewable energy resources . IEEE Transactions on Smart Grid , 2014 , 5 : 1876 – 1883 .

Khodakarami   A , Farahani   H , Aghaei   J . Stochastic characterization of electricity energy markets including plug-in electric vehicles . Renewable and Sustainable Energy Reviews , 2017 , 69 : 112 – 122 .

Alipour   M , Zare   K , Abapour   M . MINLP probabilistic scheduling model for demand response programs integrated energy hubs . IEEE Transactions on Industrial Informatics , 2018 , 14 : 79 – 88 .

Klein   L , Kwak   JY , Kavulya   G ,   et al . Coordinating occupant behavior for building energy and comfort management using multi-agent systems . Automation in construction , 2012 , 22 : 525 – 536 .

Megahed   TF , Abdelkader   SM , Zakaria   A . Energy management in zero-energy building using neural network predictive control . IEEE Internet of Things Journal , 2019 , 6 : 5336 – 5344 .

Ruiz-Cortes   M , González-Romera   E , Amaral-Lopes   R , et al.    Optimal charge/discharge scheduling of batteries in microgrids of prosumers . IEEE Transactions on Energy Conversion , 2018 , 34 : 468 – 477 .

Elsied   M , Oukaour   A , Youssef   T ,   et al . An advanced real time energy management system for microgrids . Energy , 2016 , 114 : 742 – 752 .

Radosavljević   J , Jevtić   M , Klimenta   D . Energy and operation management of a microgrid using particle swarm optimization . Engineering Optimization , 2016 , 48 : 811 – 830 .

Marzband   M , Ghazimirsaeid   SS , Uppal   H ,   et al . A real-time evaluation of energy management systems for smart hybrid home microgrids . Electric Power Systems Research , 2017 , 143 : 624 – 633 .

Sousa   T , Morais   H , Vale   Z , et al.    Intelligent energy resource management considering vehicle-to-grid: a simulated annealing approach . IEEE Transactions on Smart Grid , 2012 , 3 : 535 – 542 .

Chaouachi   A , Kamel   RM , Andoulsi   R , et al.    Multiobjective intelligent energy management for a microgrid . IEEE Transactions on Industrial Electronics , 2013 , 60 : 1688 – 1699 .

Solanki   BV , Bhattacharya   K , Cañizares   CA . A sustainable energy management system for isolated microgrids . IEEE Transactions on Sustainable Energy , 2017 , 8 : 1507 – 1517 .

Guo   Y , Zhao   C . Islanding-aware robust energy management for microgrids . IEEE Transactions on Smart Grid , 2018 , 9 : 1301 – 1309 .

Viciana   E , Alcayde   A , Montoya   FG ,   et al . An open hardware design for Internet of Things power quality and energy saving solutions . Sensors , 2019 , 19 : 627 .

Bollen   M , Zhong   J , Zavoda   F ,   et al . Power Quality aspects of smart grids. In: International Conference on Renewable Energies and Power Quality (ICREPQ’10) , Granada, Spain , 23–25 March 2010 , 1 – 6 .

Hartono   BS , Mursid   SP , Prajogo   S . Home energy management system in a Smart Grid scheme to improve reliability of power systems . IOP Conference Series: Earth and Environmental Science , 2018 , 105 : 012081 .

Nguyen   DT , Le   LB . Risk-constrained profit   maximization for microgrid aggregators with demand response . IEEE Transactions on smart grid , 2014 , 6 : 135 – 146 .

Email alerts

Citing articles via.

• Advertising and Corporate Services

Affiliations

• Online ISSN 2515-396X
• Print ISSN 2515-4230
• Copyright © 2024 National Institute of Clean-and-Low-Carbon Energy
• About Oxford Academic
• Publish journals with us
• University press partners
• What we publish
• New features  
• Open access
• Institutional account management
• Rights and permissions
• Get help with access
• Accessibility
• Advertising
• Media enquiries
• Oxford University Press
• Oxford Languages
• University of Oxford

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide

• Copyright © 2024 Oxford University Press
• Cookie settings
• Cookie policy
• Privacy policy
• Legal notice

This Feature Is Available To Subscribers Only

Sign In or Create an Account

This PDF is available to Subscribers Only

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.

• Reference Manager
• Simple TEXT file

People also looked at

Original research article, transitioning to sustainable energy: opportunities, challenges, and the potential of blockchain technology.

• School of International Business, Qingdao Huanghai University, Qingdao, China

The pressing issues of climate change and the limited availability of non-renewable energy resources have created a growing need for sustainable energy alternatives. This study provides a comprehensive overview of the pressing need for sustainable energy solutions and the complex relationship between energy and the economy. The challenges and opportunities presented by the transition to sustainable energy sources are explored, including the need for investment in renewable energy technologies, policy changes to incentivize sustainable energy use, and the potential for job creation in the sustainable energy sector. On the other hand, it is recognized that there are considerable hurdles that need to be addressed, including the substantial initial expenses associated with establishing renewable energy systems, as well as the political and societal barriers to enacting change. The economic benefits of transitioning to sustainable energy, such as improved energy security, reduced dependence on fossil fuels, and the potential for increased economic growth, are evaluated. The complex relationship between energy and the economy is thoroughly analyzed, presenting a valuable contribution to the academic literature on sustainable energy. Furthermore, an inquiry is being made into the potential contribution of blockchain technology in advancing a sustainable energy landscape. This includes its ability to augment the effectiveness and openness of energy markets, as well as its capacity to assist in the assimilation of renewable energy resources. Hence, this research underscores the importance of transitioning to sustainable energy sources for their environmental and economic merits. The findings presented offer valuable insights to inform policy decisions and guide future research endeavors in this field. By promoting the advancement of sustainable energy technologies, this study contributes to the development of a more sustainable global economy.

1 Introduction

The significance of energy in the functioning of a nation’s economy and society cannot be overstated. Nevertheless, the bulk of global energy demand is still satisfied by non-renewable fossil fuels like oil, coal, and natural gas ( Abban et al., 2022 ; Amin et al., 2022 ). Nonetheless, these sources are finite, contribute to environmental pollution and climate change, and are progressively more arduous and costly to extract. Consequently, an urgent and imperative need exists to shift towards sustainable energy alternatives that are both renewable and cleaner, capable of mitigating the detrimental impacts of climate change. This transition towards sustainable energy presents a spectrum of challenges and opportunities for the global economy. Fossil fuels have long served as the predominant energy source worldwide due to their high energy density and affordability. However, the combustion of these fuels has resulted in emissions that have caused escalating global temperatures, an increase in extreme weather events, and a myriad of other catastrophic environmental transformations ( Ansari et al., 2022 ; Asif et al., 2022 ; Chenic et al., 2022 ). As a result, numerous scholars have extensively investigated this subject matter through the utilization of modeling, empirical investigations, and optimization techniques ( Li et al., 2018 ; Xu et al., 2022 ; Tong et al., 2023 ; Xie et al., 2023 ; Yin et al., 2023 ).

The primary objective of every society is to achieve development in all its dimensions ( Meinshausen et al., 2022 ). Access to high standards of energy production and consumption is a key indicator of a country’s success in achieving development. However, energy production and consumption, whether fossil or non-fossil, pose significant threats and challenges to the environment and sustainable development, despite their role in contributing to economic growth and development ( Chien, 2022 ; Fang et al., 2022 ; Farghali et al., 2023 ). Sustainable energy can only be achieved through the integration of the environment, energy, and development sectors. Conversely, the absence of integration can lead to conflicts between the objectives of these sectors, which can hinder the achievement of sustainable development ( Holechek et al., 2022 ; Ishaq et al., 2022 ; Islam et al., 2022 ). At the international level, policies related to energy and development have been largely aligned, while environmental policies have not. The misalignment between these factors can create difficulties and discord, and resolving this dilemma requires the development of effective solutions by the global community that strike a balance between energy supply security, economic advancement, and environmental safeguarding. Hence, the primary obstacle to sustainable development in the energy industry is to ensure that the advantages of energy services are extended to the world’s population and future generations without causing harm to the environment ( Nnabuife et al., 2022 ; Meydani, 2023 ; Mohideen et al., 2023 ).

During the 1980s and 1990s, energy-related matters garnered considerable attention; however, environmental considerations within the energy sector were largely overlooked by governments. The intricate nature and diverse array of interests involved posed challenges in implementing substantial policy changes. Moreover, many countries possessing oil and gas resources faced constraints in terms of political, economic, and technological capacities, impeding their ability to transition towards sustainable development by reducing dependence on fossil fuels and transforming core economic strategies. The substantial reliance on non-renewable fossil fuels in global energy production and consumption has proven to be a significant obstacle in achieving sustainable development objectives ( Kanwal et al., 2022 ; Jie et al., 2023 ; Kocak et al., 2023 ). Sustainable energy is characterized by a lower per capita production of greenhouse gases. However, unstable energy production and consumption patterns have led to several environmental problems, including climate change, acid rain, ozone depletion, nuclear radiation, urban air pollution, and marine pollution caused by oil transportation. Developing and developed countries have blamed each other for these issues, with developing countries accusing developed countries of environmental destruction due to excessive energy consumption resulting from increased demand, and developed countries accusing developing countries of environmental degradation due to increased consumption resulting from population growth ( Nnabuife et al., 2022 ; Meydani, 2023 ). Both claims are valid, but research indicates that developing and developed countries face different challenges in relation to the environment and energy. Developing countries face energy resource scarcity and lack of access to energy, while developed countries face pollution and energy waste ( Luo et al., 2013 ; Gielen et al., 2016 ). The international community eventually recognized that sustainable development cannot be achieved without sustainable energy ( Adamowicz, 2022 ).

When discussions about the pollution caused by traditional energy sources arise, there is often a shift towards non-conventional energy sources ( Ramzan et al., 2022 ; Mohideen et al., 2023 ). Lovin’s guidelines, introduced in 1972, provide a precise framework for establishing acceptable and conventional energy policies using both hard and soft methods. In Lovin’s model, the hard path emphasizes the rapid development of energy resources such as coal and nuclear energy, with sustainable energy production for low consumption and efficiency being highlighted. This approach involves extensive research on nuclear energy and rapid development of energy resources like coal. Lovin’s theory suggests that the hard path leads to the creation of elite technology, concentration of economic and political power, vulnerability to technological threats, and the potential for social and economic injustice and complexity ( Lovins, 1974 ; Lovins, 1976 ). On the other hand, the soft energy path focuses on limiting energy production and maximizing efficiency in consumption. Soft energy leads to a small, decentralized system and is flexible, sustainable, and environmentally safe. The advantages of this approach are manifold and include the potential to extend energy generation beyond centralized production centers, self-sufficiency in energy supply, reduced reliance on public or private energy infrastructure, a preference for renewable energy over non-renewable sources, eliminating the use of uneven energy resources, prioritizing energy conservation, and employing low-risk technology that is suitable for energy sources with high risk factors ( Takase et al., 2022 ; Yasmeen et al., 2022 ; Sharma et al., 2023 ).

Renewable energy technologies have experienced significant advancements in recent years, with increased efficiency and declining costs. They offer a promising pathway to a sustainable energy system, but significant investments and policy changes are still necessary to facilitate the transition. The high upfront costs associated with renewable energy infrastructure remain a significant barrier for many. Government policies and incentives that favor renewable energy development and use are crucial to overcoming the obstacles to adoption and accelerating the transition towards sustainability. Some key policies that can support renewable energy include tax incentives, direct subsidies, renewable energy mandates, and carbon pricing ( Siddik et al., 2023 ; Zakaria et al., 2023 ). Through appropriate policies and economic support, renewable energy has the potential to surpass fossil fuels in competitiveness. The transition to renewable energy offers various economic advantages, including the creation of new job opportunities in the sustainable energy sector, enhanced energy security and independence, and long-term cost savings. The renewable energy industry is generating numerous employment opportunities in manufacturing, technology, installation, and related fields. Shifting towards locally available renewable sources also enables countries to achieve greater energy security and independence. While renewable energy technologies often involve initial high costs, their operating costs remain low as the sun or wind (as fuel sources) is freely available. Over time, these technologies can yield cost savings alongside environmental benefits. Nevertheless, the transition presents economic challenges that necessitate attention. Established interests striving to maintain the status quo may pose social and political obstacles. A successful transition requires initiatives such as worker retraining programs, support for affected communities, and effective communication with citizens and policymakers to highlight the advantages of renewable energy. Nonetheless, with the rapid decline in the costs of renewable technologies, the economic arguments in favor of sustainability and renewables are becoming more compelling over time ( Takase et al., 2022 ; Yu et al., 2023 ; Zhang et al., 2023 ).

The implementation of blockchain technology presents a significant opportunity for advancing sustainable energy systems. This technology operates as a decentralized ledger that records transactions in a permanent and unalterable manner, thereby enabling transparent monitoring of energy production and consumption throughout the network. Numerous projects are leveraging blockchain to enable peer-to-peer energy trading between producers and consumers, monitor the origin of renewable energy generation, and facilitate innovative financing models for renewable energy projects. The intermittency of renewable energy sources poses a significant challenge for renewable energy. Blockchain can help address this by enabling decentralized energy trading networks. When a solar panel owner generates excess energy, they can sell it to neighboring buildings using smart contracts on the blockchain. Neighbors who need additional energy can buy it instantly in a transparent marketplace, resulting in minimal waste and improved overall efficiency and reliability of renewable energy ( Afzal et al., 2022 ; Gawusu et al., 2022 ; Dwivedi et al., 2023 ). Several startups facilitating these energy trading networks have launched in New York, California, and European countries. Another promising application of blockchain for sustainable energy is renewable energy certification and tracking the origin of energy. Certification systems like Renewable Energy Certificates (RECs) help fund renewable energy projects by enabling businesses and individuals to purchase renewable energy credits. However, the current system for RECs involves cumbersome paperwork, administration fees, and a lack of transparency. Implementing RECs on an open blockchain platform can reduce costs, simplify the process, and provide a clear link between the generation and consumption of renewable energy. Several companies are piloting blockchain-based renewable energy certificate platforms ( Juszczyk and Shahzad, 2022 ; Polas et al., 2022 ; Wu et al., 2022 ).

Although there has been a proliferation of research on sustainable energy and the potential of emerging technologies, there still exists a discernible gap in knowledge regarding the assimilation of blockchain technology in the shift towards sustainable energy alternatives. Additionally, the interplay between energy policy, economic factors, and technological advancements in facilitating this transition has not been comprehensively explored. Thus, the importance of this study lies in addressing this research gap and providing valuable insights into the challenges, opportunities, and implications of transitioning to sustainable energy sources, while emphasizing the role of blockchain technology and policy changes. The primary objective of this investigation is to examine the intricate facets of the worldwide energy milieu and assess the viability of blockchain technology in advancing sustainable energy solutions. The research methodology employed in this study is a qualitative research design, which includes an extensive literature review and content analysis. This systematic approach facilitates a thorough examination of the interconnections among energy, policy, technology, and the economy in the transition towards sustainable energy sources. The study’s findings have multiple practical implications, including offering evidence-based insights for policymakers and industry stakeholders in the formulation of sustainable energy strategies. Moreover, the research outcomes inform future studies in this field. These insights play a crucial role in enhancing policy effectiveness, encouraging investments in renewable energy technologies, and fostering the expansion of the sustainable energy sector. Ultimately, this study aims to advance the understanding of the role of blockchain technology and policy changes in facilitating the transition to sustainable energy sources, and the benefits and challenges associated with this transition, thereby contributing to the development of a more sustainable and resilient global economy.

2 Fundamental concept of the study and definitions

2.1 sustainable energy and technologies.

The term sustainable energy pertains to energy resources that can fulfill existing energy requirements while preserving the capacity of future generations to meet their own energy needs. This widely accepted definition is supported by scientific literature and the international community. The adoption of sustainable energy solutions is essential in curtailing the release of greenhouse gases into the environment and ameliorating the impacts of climate change. Moreover, it promotes energy security by decreasing reliance on fossil fuels and promoting energy independence. The development and implementation of sustainable energy technologies require a complex process that involves technological innovation, policy support, and public awareness ( Chai and Zhang, 2010 ; Qazi et al., 2019 ).

Various sustainable energy technologies are currently being developed, including solar PV, wind turbines, geothermal energy, energy storage, smart grids, hydrogen fuel cells, and biofuels. Solar PV technology converts sunlight into electricity through solar panels and is rapidly advancing, becoming more efficient and cost-effective. Biofuels, derived from renewable biomass sources, are becoming more sustainable and environmentally friendly than traditional fossil fuels due to advances in technology. These technologies aim to provide a stable and reliable supply of electricity while balancing energy supply and demand, integrating renewable energy sources into the grid, and offering a clean source of energy for transportation and other applications. Also, they are expected to become more efficient, cost-effective, and widely adopted as research and development continue. They hold the potential to build a sustainable energy future, addressing the world’s growing energy needs while reducing the environmental impact of energy production and use ( Chu and Majumdar, 2012 ; Vujanović et al., 2021 ).

2.2 Economic benefits of sustainable energy transition

Sustainable energy practices, policies, and technologies offer numerous economic benefits, including cost savings, job creation, and increased competitiveness. The adoption of renewable energy sources can reduce dependence on fossil fuels, which are susceptible to price volatility and supply disruptions. Renewable sources like solar and wind power have become cost-competitive with traditional sources in many regions, with costs expected to decrease further. Energy efficiency measures can also reduce energy costs by improving energy use efficiency and decreasing waste. The development and deployment of sustainable energy technologies create new job opportunities in industries such as renewable energy, energy efficiency, and energy storage. Businesses that adopt sustainable energy practices and technologies can reduce energy costs, enhance their reputation, and meet the growing demand for sustainable products and services, leading to increased market share and profitability ( Bulavskaya and Reynès, 2018 ; Osorio-Aravena et al., 2021 ; Dong et al., 2022 ; Tirkolaee et al., 2022 ; Wang H. et al., 2023 ). The shift towards a sustainable energy future can have a positive impact on public health by decreasing air pollution, which has severe health consequences. The costs of air pollution to the environment and public health can be considerable, and the adoption of sustainable energy practices and technologies can help to minimize these costs. The integration of sustainable energy practices and technologies can also decrease the environmental impacts related to energy production and use, such as land use impacts and greenhouse gas emissions. The environmental costs of conventional energy sources can be significant, and the adoption of sustainable energy practices and technologies can help to address these costs and promote a more sustainable and resilient energy system. To conclude, the implementation of sustainable energy practices, policies, and technologies can offer numerous economic benefits, including job creation, cost savings, improved public health outcomes, and reduced environmental impacts. By leveraging technologies like blockchain to enhance the efficiency and transparency of energy systems, we can expedite the transition to an energy system that is both sustainable and resilient and that benefits the economy and the environment ( Jenniches, 2018 ; Gielen et al., 2019 ; Ghasemi et al., 2021 ; Mirzaei et al., 2021 ).

2.3 Blockchain for sustainable energy

Distributed ledger technology, known as blockchain, facilitates secure, transparent, and tamper-proof recording of data and transactions. It operates by using a decentralized network of computers to validate and verify transactions, thereby eliminating intermediaries like banks or governments. The integration of blockchain technology presents significant potential in the shift towards a sustainable energy future, as it enables more efficient and transparent energy systems. It can address many of the challenges associated with the transition by enabling more effective management of energy systems, improving energy efficiency and reducing waste, and increasing transparency and accountability in energy systems ( Arabian et al., 2022 ; Barenji and Nejad, 2022 ). Blockchain technology can provide a secure and transparent platform for tracking the production and consumption of energy and enables peer-to-peer energy trading ( Wu and Tran, 2018 ; Ahl et al., 2020 ; Otoum et al., 2022 ; Goli, 2023 ). Additionally, blockchain-based smart contracts can automate energy transactions and incentivize energy conservation, leading to more efficient and sustainable energy use. Blockchain technology can help address this challenge by enabling the development of smart grid systems that use real-time data to optimize energy production, storage, and consumption. These systems can automate energy transactions and incentivize energy conservation through blockchain-based smart contracts, leading to more efficient and sustainable energy use ( Andoni et al., 2019 ; Akram et al., 2020 ).

2.4 Policy changes for sustainable energy

This study highlights the significance of policy adjustments in facilitating the transition towards a sustainable energy future. Policy modifications encompass the development and implementation of new regulations and policies designed to promote sustainable energy practices and technologies. These policy changes can manifest at different levels, ranging from local to global, and can take various forms. Examples of policy changes that can advance sustainable energy practices include the implementation of incentives and targets for renewable energy adoption, the establishment of standards and incentives for energy efficiency improvements, and the enactment of policies that impose a price on carbon emissions ( Kern and Smith, 2008 ; Kuzemko et al., 2016 ). According to scientific literature, policy changes are essential in promoting a sustainable energy future. As a result, policymakers must meticulously design policies that can achieve the desired outcomes while minimizing potential negative impacts. The use of blockchain technology can also be critical in enabling more efficient and transparent energy systems. Therefore, policymakers must prioritize policy changes that promote sustainable energy practices and technologies to expedite the transition to a sustainable and resilient energy system. The success of these policies depends on meticulous consideration of political, economic, and social factors, along with the potential role of blockchain technology in facilitating more efficient and transparent energy systems ( Streimikiene and Šivickas, 2008 ; Lu et al., 2020 ; Yildizbasi, 2021 ).

3 Research methodology

The current research employs a qualitative research methodology to investigate the potential of blockchain technology in promoting the adoption of sustainable energy alternatives. The choice of a qualitative research design is deemed appropriate as it allows for a comprehensive understanding of complex social phenomena, specifically the global energy system and the transition towards sustainable energy sources. By utilizing a qualitative approach, this study can explore the intricate relationships between energy, sustainability, policy, technology, and the economy in an open-ended and flexible manner.

To gather data, an extensive review of pertinent scientific literature, policy documents, industry reports, and media articles was undertaken. The literature review encompassed various domains, including sustainable energy, renewable energy policy, energy economics, blockchain technology, and innovation. This comprehensive review provided a foundation for identifying key themes, challenges, opportunities, and arguments concerning the transition towards sustainable energy sources and the potential role of blockchain technology and policy changes in facilitating this transition.

In this research study, the analysis primarily relied on secondary data obtained from reliable sources. Through this analysis, the study aimed to identify and examine the main themes and insights related to the transition towards sustainable energy sources and the potential of blockchain technology. By drawing upon a wide range of sources, this study sought to gain a deeper understanding of the challenges, opportunities, and arguments surrounding the integration of blockchain technology in the pursuit of sustainable energy alternatives.

The collected data underwent qualitative content analysis, which involved identifying key concepts, themes, and arguments. The data was categorized based on focal areas such as sustainable energy technologies, economic factors, policy issues, blockchain applications, challenges, and opportunities. Through this coding process, relationships between categories and overarching themes were established. The study synthesized the significant findings and insights, leading to a comprehensive discussion of various aspects. These included the current state of the global energy system, the imperative need for transitioning to sustainable energy sources, challenges and opportunities associated with the transition, the role of policy changes and blockchain technology in facilitating the transition, and the implications for the economy.

To minimize bias, the data collection and analysis process incorporated multiple perspectives from diverse and reputable sources. Various viewpoints were considered to ensure a comprehensive analysis. The study substantiated its findings with evidence from the literature, while also considering alternative explanations or counterarguments.

The research process was transparently documented, allowing for scrutiny of the logical reasoning behind the analysis and conclusions. Additionally, member checking was conducted by presenting the preliminary findings to experts in the fields of energy policy and blockchain technology. Their feedback was incorporated, further enhancing the credibility, transferability, dependability, and confirmability of the study.

Consequently, this study adopted a qualitative research design that encompassed an extensive literature review and content analysis to comprehensively investigate the research topic and fulfill the study objectives. By employing this methodology, a systematic and rigorous process was established to gain profound insights into the intricate relationships among energy, policy, technology, and the economy within the context of transitioning towards sustainable energy sources.

4 Finding and discussion

4.1 exploring the environmental impacts of sustainable energy.

The overconsumption of fossil fuels has resulted in an increase in air pollution and global warming, which has propelled climate change to the forefront of public discourse ( Leiserowitz, 2007 ; Perera, 2018 ). As a result, there is a growing demand for alternative energy sources, particularly renewable energy. However, research has indicated that renewable energy sources may also have negative environmental impacts ( Al-Shetwi, 2022 ; Rahman et al., 2022 ). This section focuses on the environmental effects of solar energy, wind energy, and hydroelectric systems, including their impact on air pollution, soil quality, noise levels, and wildlife.

4.1.1 Environmental impacts of solar energy

Solar energy is acknowledged as the most significant renewable energy source due to its simplicity of deployment and eco-friendly characteristics in contrast to other energy sources. There are various methods of converting direct solar energy into useable energy, such as solar heating systems, building systems, and photovoltaic systems. The installation of solar energy production systems requires large ground components that can absorb a significant amount of solar energy without being too expensive. It is ideal that these components are not placed in agricultural or forested areas, and they should be located near population centers to reduce transportation costs and energy loss. The northwestern region of the unit is the ideal location for the central system due to its high sunlight exposure ( Xu et al., 2021 ; Jiang J. et al., 2022 ; Cai et al., 2022 ; Yu and Zhou, 2023 ). However, the effects of large-scale solar units on desert ecosystems need to be investigated. Additionally, the construction and equipment protection of solar units utilize many materials such as glass, cement, and steel. During the construction phase, pollution effects need to be studied, as it is estimated that the amount of materials required for solar units is greater than that of fossil-fuel units. Photovoltaic-based units use unconventional and toxic materials such as cadmium sulfide, which is flammable. Large-scale use of solar energy creates significant problems in terms of water pollution due to the use of anti-icing agents, anti-corrosion agents, and metals that enter the water during system washing. The use of herbicides to prevent weed growth around collectors also indirectly leads to water pollution ( Mahajan, 2012 ; Hosenuzzaman et al., 2015 ; Tawalbeh et al., 2021 ).

The environmental impacts of solar energy production systems are significant and include permanent land use during the unit’s operation, as well as the production of non-renewable materials such as insulation and glass. Photovoltaic-based systems also produce toxic materials such as cadmium and arsenic. Other adverse effects include damage to the landscape, eye damage due to reflection of solar radiation, and land erosion and compaction, wind deflection, and increased potential for soil evaporation. An analysis conducted in 1977 compared the particulate pollution associated with the construction of a solar unit to that of oil or coal-based units producing the same amount of energy. The study found that solar units produced significantly less particulate matter. It is important to carefully consider the costs, hazardous waste, and land use associated with solar energy production compared to other forms of energy. Although there is potential for large-scale replacement of nuclear and fossil-fuel units with solar units, the risks posed by solar technologies, such as safety and health risks, must also be taken into account. For photovoltaic-based units, the most significant negative effect is water consumption for cooling, which can lead to the destruction of surface and underground units. This can also destroy the habitats of soil-dwelling organisms and other animals that live in the desert. Additionally, the energy produced must be transported to residential and industrial centers, resulting in significant energy loss during transmission ( Hernandez et al., 2014 ; Mahmud et al., 2018 ; Sánchez-Pantoja et al., 2018 ).

Solar energy is deemed an eco-friendly energy option for temperature regulation through heating and cooling. Research has shown that the only negative consequence of this method is the potential for urban aesthetic issues. In some cases, compatibility issues may arise between solar systems and trees near homes, and the extensive use of collectors installed on roofs can alter reflection and have minor impacts on weather patterns. However, these changes are not considered significant threats to the environment. The only environmental risk associated with solar energy for heating and cooling is during the construction of the equipment in factories ( Kumar et al., 2020 ; Rabaia et al., 2021 ; Tang et al., 2022 ; Yavari et al., 2022 ).

4.1.2 Environmental impacts of wind energy

Wind energy is considered the least risky among energy sources due to its lack of need for a cooling system. However, wind energy has its disadvantages, such as noise pollution, interference with nature, and reduction of the area’s aesthetic appeal. Wind turbines may also cause damage to the ecosystem by reducing wind speed, leading to warmer lakes and decreased surface evaporation. Nonetheless, the environmental effects of wind turbines are not significant. Risks to human safety during construction and operation are similar to those in other industries, with small wind turbines in densely populated areas posing a greater risk to human health. Wind turbines can pose a risk to birds since they may not be able to avoid the high-speed blades. There are two types of pollution associated with wind turbines: machine noise, which can be mitigated through proper design and sound insulation measures, and rotational noise generated by the vortex flow of air. Additionally, wind motion can produce noise, which is often perceived as pleasant and enjoyable, particularly at higher wind speeds ( Wang and Wang, 2015 ; Dhar et al., 2020 ; Nazir et al., 2020 ). However, low-frequency and subsonic sounds may cause vibrations in houses and metal structures, particularly in turbines that react with the tower based on blades. Wind turbines may also create signals that interfere with television waves and cause disturbances in rainfall and surface evaporation from the ground due to wind movement resulting from windmills on the ground. Despite limited cases of such disturbances reported in 1995, the impact is not considered significant. Energy storage or auxiliary systems are required for wind energy, though it should be noted that these systems are more vulnerable to wind energy. Auxiliary facilities are used in high-risk situations ( Etheridge, 2000 ). Distributed wind energy systems are considered more environmentally compatible than other energy sources if wind turbines are scattered throughout the country’s agricultural lands and connected to a network. In this scenario, only a small portion of the turbines would be considered undesirable ( Jaber, 2013 ; Mendecka and Lombardi, 2019 ).

4.1.3 Environmental impacts of large and small hydroelectric projects

While large hydroelectric energy production projects are considered renewable energy sources, micro and small water systems are classified as unconventional energy sources. Before discussing the potential effects of small and micro water systems projects, it is important to briefly examine the main findings of experiments conducted by scientists. Although environmental impacts are expected to be smaller and different in smaller systems ( Egré and Milewski, 2002 ; Abbasi and Abbasi, 2011 ; Başkaya et al., 2011 ).

Hydroelectric energy production projects have been extensively studied alongside thermal units in terms of their environmental impacts. While some experts believe that hydroelectric units, particularly large ones, have negative effects on the environment and have the greatest destructive impact compared to other renewable energy sources, there is not a complete consensus. In the 1950s, when only a few of these units were in use worldwide, it was believed that this energy source was the cleanest form of energy compared to other sources. Water is one of the most expensive and even the most expensive natural resource, and dams provide this water in abundant quantities and in a way that can be used several times. Dams provide the possibility of water use throughout the year (for public use, fishing, and recreation). After electricity is obtained from water, it can be used for irrigating agricultural lands downstream of the dam. During this process, it is possible to recharge underground water resources. This energy and these benefits will be obtained without creating any smoke from thermal units or any hazardous waste from nuclear power plants ( Jumani et al., 2017 ; Nautiyal and Goel, 2020 ; Oladosu et al., 2021 ). However, now, after 50 years, such units are considered hazardous by some experts. The most important environmental effects caused by large hydroelectric units are storing rainfall in the area, creating an artificial lake, reducing water flow downstream, and changing the flow of the river. Changes in the water flow can have various environmental effects, including increased water evaporation and potential soil leakage, displacement of small aquatic organisms, formation of distinct temperature layers, alterations in habitat and food availability, and reduction of inhabitable lands due to the creation of artificial lakes. Moreover, there is a risk of nutrient accumulation in the lake area and downstream river regions. Organisms located at river mouths may experience negative impacts due to the mixing of saltwater and the diminished flow of freshwater. Reproduction of organisms, fish, and other aquatic conditions are affected by changes in river flow and move towards the river’s border areas. Increased water stagnation and human activities in the lake area lead to increased deforestation and reduced animal habitats. Often, water-borne diseases increase in these areas. The latest research shows an increase in the production of greenhouse gases such as methane from the lakes created by human-made dams. Some researchers believe that the amount of gas emitted by these units is comparable to the gases emitted in fossil fuel units, although there are differences between the greenhouse gases produced by humans. This recent issue is more significant in terms of weight and size than other problems resulting from hydroelectric systems ( Pinho et al., 2007 ; Pang et al., 2015 ).

Small and micro-hydro systems are created by constructing numerous small dams with low heads or generators placed in the path of water flow. China leads other countries in having small hydro units, with approximately 100,000 units in rural areas. In 1980, the Philippines generated 4 MW of electricity from these units. However, these units are not without their problems, and their production in kilowatts is not significantly higher than centralized energy units. Some of the environmental impacts associated with these units include hindering animal movement, increasing water evaporation due to slow movement, reducing river boundaries and animal habitats, which incurs high costs to create such habitats elsewhere. Access roads must be built to reach these units, which in turn harms the environment. These units also contribute to environmental degradation by producing sediment and accumulating nutrients, which is a significant problem in large and small hydro units, leading to a decrease in the depth and size of the space. The production of greenhouse gases is another problem associated with these units, as they act as shallow water reservoirs that emit gases such as methane. Therefore, the environmental impacts of small and dispersed hydro units are significant and influential in all cases, and more attention should be paid to their potential negative effects ( Gleick, 1992 ; Hennig et al., 2013 ; Zeleňáková et al., 2018 ).

Figure 1 presents an outline of the environmental consequences of renewable energy sources, with particular emphasis on wind energy, solar energy, and hydroelectric systems. The figure highlights the potential adverse effects of these energy sources on greenhouse gas emissions, wildlife habitats, noise pollution, water pollution, and land use. A thorough evaluation of the costs and benefits of each energy source, as well as careful consideration of these impacts, is crucial when transitioning to renewable energy sources.

FIGURE 1 . Assessing the environmental impacts of renewable energy sources: a comprehensive overview of solar, wind, and hydroelectric energy.

4.2 Challenges and opportunities in the transition to sustainable energy sources

The shift towards sustainable energy sources poses both opportunities and obstacles. One of the most significant obstacles is the requirement for investment in renewable energy technologies, as the current energy infrastructure is heavily reliant on traditional energy sources. The adoption of sustainable energy sources necessitates substantial investment in renewable energy technologies such as hydro, wind, solar, and geothermal, which is essential in ensuring their accessibility and affordability, facilitating widespread implementation ( Liu L. et al., 2022 ; Chen, 2022 ; Guo B. et al., 2023 ; Liu et al., 2023 ). Another significant challenge is policy changes to incentivize sustainable energy use. Governments play a critical role in promoting sustainable energy use through policy changes. These policies create a favorable environment for renewable energy technologies to thrive, making them more accessible and affordable for consumers. However, implementing these policies can be challenging, requiring political will and public support ( Dominković et al., 2018 ; Hassan et al., 2019 ; Ghasemi et al., 2022 ; Al-Housani et al., 2023 ; Ibeanu et al., 2023 ).

Despite these challenges, the transition to sustainable energy sources presents significant opportunities, such as job creation in the sustainable energy sector. The sustainable energy sector is an industry that is expanding rapidly, offering significant potential for generating new jobs. Job opportunities in the sustainable energy sector range from manufacturing and installation to research and development, offering a wide range of opportunities for workers with diverse skill sets. The shift towards sustainable energy sources also presents an opportunity for a more environmentally-friendly world. The adoption of sustainable energy sources like hydro, wind, and solar can mitigate the adverse environmental effects of traditional energy sources, resulting in a cleaner and healthier environment. Additionally, renewable energy technologies are inexhaustible and do not produce greenhouse gas emissions ( Armaroli and Balzani, 2007 ; Magar et al., 2023 ; Owusu and Asumadu-Sarkodie, 2016 ; Bahlouli et al., 2023 ).

The sustainable energy sector offers promising prospects for job creation, yet it faces various challenges. One of the significant challenges is the skills gap, requiring a highly skilled workforce with specialized technical skills, including engineering, science, and technology. Nonetheless, the sustainable energy sector is relatively new, and many workers may not have prior experience in this field. As a result, companies may face challenges finding experienced workers, leading to longer training periods and additional costs. Another challenge is the uncertainty of government policies, such as tax incentives and renewable energy standards, which can make it difficult for companies to plan and invest in their workforce. Moreover, funding challenges pose a significant hurdle for the sustainable energy sector, as it requires significant investment to develop and expand ( Vidadili et al., 2017 ; Pérez et al., 2019 ; Bayulgen, 2020 ; Ishaq et al., 2022 ). Many companies may not have the resources to invest in expanding their workforce, and securing funding can be difficult due to the high risk associated with the sector. Additionally, the competition with established industries, such as oil and gas, makes it challenging for the sustainable energy sector to attract workers and establish a foothold in the job market. To overcome these challenges, a collaborative effort between governments, businesses, and educational institutions is required to create the necessary skills, policies, and funding to support job creation in this critical sector. This effort can include upskilling workers in specialized technical skills, providing training and education programs to develop experience, and creating more predictable and supportive government policies. Furthermore, funding opportunities and incentives can be developed to support the growth of the sustainable energy sector and reduce the risk associated with investing in it ( Erat et al., 2021 ; Kabeyi and Olanrewaju, 2022 ; Guo L. et al., 2023 ; Japir Bataineh et al., 2023 ).

Academic institutions have a crucial role to play in filling the skills gap in the sustainable energy sector. To tackle this challenge, there are a variety of approaches that educational institutions can take to narrow this gap. One such step is to offer targeted programs tailored to the specific needs of the sustainable energy sector. These initiatives aid students in acquiring the technical skills and understanding necessary to excel in the sustainable energy sector, such as sustainable energy management or renewable energy engineering. Collaborating with industry partners can also help ensure that educational programs align with current and future industry needs, enabling students to acquire the necessary skills and knowledge to work in the sustainable energy sector. In addition, hands-on training is crucial in the sustainable energy sector. Educational institutions can provide students with access to labs, workshops, and internships to gain practical experience working with renewable energy technologies. Furthermore, incorporating sustainability into existing programs, such as business or engineering, can help students understand the importance of sustainability in all fields. Continuing education programs can also help bridge the skills gap by providing established industry workers with the necessary skills and knowledge required to work in renewable energy. Finally, educational institutions can provide career services to help students and graduates find jobs in the sustainable energy sector, including job fairs, networking opportunities, and career counseling ( Di Somma and Graditi, 2002 ; Kyriakopoulos et al., 2022 ).

The sustainable energy sector holds a plethora of opportunities for job creation, but it is not without its challenges. One such challenge is the skills gap, which necessitates a workforce with specialized technical skills in engineering, science, and technology. Unfortunately, there is a shortage of skilled workers in this area, and the supply of qualified candidates does not always align with the demand, posing a significant challenge for companies seeking to expand their workforce. Another challenge is the limited experience of workers in the sustainable energy sector, a relatively new field. As a result, companies may face difficulties finding experienced workers, which can lead to lengthier training periods and additional expenses. Moreover, government policies play a crucial role in job creation in the sustainable energy sector. Furthermore, the sustainable energy sector requires substantial investment to develop and expand, and securing funding can be challenging due to the high risk associated with the sector. Consequently, many companies may not have the resources to invest in expanding their workforce. Furthermore, the sustainable energy sector faces competition from established industries such as oil and gas, which have a well-established workforce and infrastructure. This competition can make it difficult for the sustainable energy sector to attract workers and establish a foothold in the job market ( Sakellariou and Mulvaney, 2013 ; Sen and Ganguly, 2017 ; Inês et al., 2020 ).

In order to attract workers to the sustainable energy sector, businesses can implement a number of strategies to compete with established industries. These strategies include providing competitive salaries and benefits packages that are comparable to those offered by established industries. Companies can also offer training and development opportunities to help workers build the necessary skills and knowledge required to work in the sustainable energy sector. This can include on-the-job training, mentorship programs, and professional development opportunities. Highlighting the impact of the work being done in the sustainable energy sector can also be an effective strategy to attract workers. This emphasizes the importance of creating a sustainable future and reducing the environmental impact of energy production, which can be instrumental in attracting workers who value environmental sustainability. Furthermore, partnering with educational institutions to develop programs that prepare students for careers in the sustainable energy sector can be an effective means of recruiting new talent. This includes providing internships, mentorship programs, and other opportunities for students to gain hands-on experience in the field. Finally, businesses can connect with the community to enhance consciousness of the importance of sustainability and to advocate for the advantages of working in the sustainable energy sector. This includes participating in community events, hosting workshops, and partnering with local organizations to support sustainability initiatives. These strategies can help companies effectively compete with established industries and attract workers who are passionate about creating a sustainable future ( Stolten and Scherer, 2013 ; Surendra et al., 2014 ; Quitzow et al., 2019 ).

The shift towards a sustainable energy infrastructure is not without its difficulties. One of the primary hurdles is the considerable initial costs required for constructing sustainable energy infrastructure. Although renewable energy has long-term cost advantages, governments, investors, and businesses must be willing to make substantial financial commitments to develop new sustainable energy initiatives and modernize existing infrastructure. Another critical challenge is the political and social resistance to change ( Hu et al., 2021 ; Luo et al., 2023 ; Wu et al., 2023 ; Yi et al., 2023 ). The fossil fuel industry has a strong presence in many countries, which may resist efforts to transition to renewable energy. Additionally, local communities may be resistant to large-scale renewable energy projects due to concerns about potential impacts such as noise pollution or changes to the landscape. Technical impediments also represent a significant challenge to the adoption of sustainable energy. Aslo, there is a need for more research and development in renewable energy technologies to improve their efficiency, reduce costs, and enhance their scalability. Technological advancements will be critical to the continued growth and adoption of renewable energy ( Bose et al., 2019 ; Pietrosemoli and Rodríguez-Monroy, 2019 ; Popescu et al., 2022 ). Figure 2 provides a comprehensive overview of the obstacles and possibilities in the shift towards sustainable energy alternatives. The figure shows the need for investment in renewable energy technologies, policy changes to incentivize sustainable energy use, and the potential for job creation in the sustainable energy sector. It is important to consider these challenges and opportunities when transitioning to sustainable energy sources and to carefully evaluate the costs and benefits of each energy source.

FIGURE 2 . Challenges and opportunities in the transition to sustainable energy sources: investment in renewable energy technologies, policy changes to incentivize sustainable energy use, and job creation in the sustainable energy sector.

Table 1 compares the challenges, opportunities, advantages, and disadvantages of transitioning to sustainable energy sources in developed and developing countries ( Herzog et al., 2001 ; Verbruggen et al., 2010 ; Broman and Robèrt, 2017 ; Safari et al., 2019 ; Hoang et al., 2021 ; Mourtzis et al., 2022 ; Neacsa et al., 2022 ; Tian et al., 2022 ; Usman et al., 2022 ). Attaining a sustainable and resilient future through sustainable energy alternatives is a global challenge that necessitates cooperation from individuals, businesses, and governments. Although there are similarities in the challenges and opportunities associated with the transition, regional differences must be taken into account. The table highlights the significant differences in challenges, opportunities, advantages, and disadvantages between developed and developing countries. Recognizing these regional disparities is crucial in designing effective policies and strategies to promote sustainable energy solutions and foster a more sustainable and resilient future.

TABLE 1 . Comparing challenges, opportunities, advantages, and disadvantages of the transition to sustainable energy sources in developed and developing countries.

In developed countries, the transition towards sustainable energy sources is accompanied by various challenges, including substantial initial investments, resistance from social and political entities, and technological barriers. Despite these obstacles, this transition also offers opportunities for job creation, economic growth, and enhanced energy security. The adoption of sustainable energy alternatives in developed nations yields multiple benefits, such as mitigating the impacts of climate change, reducing dependence on traditional energy sources, and stimulating economic progress. However, this transition is not without drawbacks, as it involves high upfront costs, encounters opposition from social and political forces, and may contribute to environmental degradation. Conversely, developing countries confront distinct challenges as they strive to transition to sustainable energy sources, including limitations in financial resources, inadequate infrastructure, and institutional capacity constraints. Despite these difficulties, transitioning to sustainable energy sources in developing nations also presents opportunities for economic advantages, environmental improvements, and enhanced energy security. However, this process brings disadvantages in the form of limited financing options, insufficient infrastructure, energy insecurity, and potential environmental degradation ( Press and Arnould, 2009 ; Peeters, 2012 ; Nejad and Kashan, 2019 ; Majid, 2020 ; Wang H. et al., 2023 ).

4.3 Economic benefits of the transition to sustainable energy

The shift towards sustainable energy sources has the potential to yield substantial economic advantages, such as enhanced energy security, decreased reliance on traditional energy sources, and a rise in economic growth. This section will provide a detailed explanation of these benefits. One of the key economic advantages of adopting sustainable energy alternatives is the enhancement of energy security. By diversifying the energy mix and decreasing reliance on a solitary energy source, countries can bolster their energy security and decrease their susceptibility to supply interruptions. Sustainable energy sources like solar and wind power are readily accessible and can be sourced locally, resulting in decreased necessity for transporting energy resources over long distances. This can help to mitigate the risk of supply disruptions due to geopolitical tensions or natural disasters. Another economic benefit of transitioning to sustainable energy is the potential for reduced dependence on fossil fuels. This can improve their energy security and reduce the economic risks associated with fossil fuel dependency. Furthermore, the transition to sustainable energy can spur economic growth. The creation and implementation of renewable energy technologies can generate novel job opportunities and stimulate economic activity in the sustainable energy field. This can result in the emergence of new industries and the expansion of new markets for sustainable energy commodities and services. Additionally, the transition to sustainable energy can lead to cost savings over the long term, as renewable energy sources are generally cheaper than fossil fuels once the initial investment is made ( Perelman, 1980 ; Merven et al., 2019 ; Siampour et al., 2021 ; Wang et al., 2022 ; Wang X. et al., 2023 ).

The adoption of sustainable energy alternatives is instrumental in reducing the economic costs associated with climate change. The combustion of conventional energy sources emits greenhouse gases, which contribute to global warming and the exacerbation of climate-related impacts, including more frequent and severe weather events. By reducing reliance on traditional energy sources, countries can help mitigate the economic consequences of climate change, such as infrastructure damage, increased insurance premiums, and decreased productivity. Therefore, transitioning to sustainable energy is an essential step towards building a more resilient and sustainable economy. Another notable economic advantage of adopting sustainable energy alternatives is the potential for enhanced energy efficiency. Sustainable energy sources, such as solar and wind power, are intrinsically more efficient than traditional energy sources, as they convert a greater percentage of the energy input into practical energy. This can help to reduce energy waste and lower energy costs. Additionally, the adoption of sustainable energy alternatives can encourage the implementation of energy-efficient technologies, such as energy-efficient appliances and smart meters, which can further decrease energy consumption and expenses. This can result in substantial economic benefits for individuals, businesses, and governments, while also diminishing greenhouse gas emissions ( Tirkolaee et al., 2020a ; Cantarero, 2020 ; Liu H. et al., 2022 ; Wang and Razzaq, 2022 ).

The adoption of sustainable energy alternatives can have a significant impact in decreasing the economic expenses related to air pollution. Fossil fuels, when burned, release pollutants into the air, contributing to significant health impacts and increased healthcare costs. Through the adoption of sustainable energy alternatives, nations can diminish their dependence on traditional energy sources, resulting in decreased air pollution and associated health expenses. Consequently, this can lead to noteworthy economic benefits for individuals, businesses, and governments. Another significant economic benefit of transitioning to sustainable energy is the potential for increased energy independence. Furthermore, renewable energy sources are abundant and widely available, reducing the need for countries to rely on foreign energy sources and thereby lowering their vulnerability to geopolitical tensions ( Van Der Schoor and Scholtens, 2015 ; Chen J. et al., 2023 ).

The shift towards sustainable energy alternatives can also yield noteworthy economic advantages for rural communities. Renewable energy can be located in rural areas, creating new economic opportunities and stimulating local economic growth. This can help to create new jobs and generate income for rural communities. Moreover, renewable energy projects can provide a new source of income for farmers and landowners, as they can lease their land for renewable energy projects and receive regular payments. Furthermore, transitioning to sustainable energy can help to attract investment and improve a country’s competitiveness. Investors and businesses are increasingly looking for opportunities to invest in sustainable energy projects, and countries that have a strong commitment to sustainability and renewable energy are likely to be more attractive to these investors. This can lead to increased investment in sustainable energy projects, creating new jobs and economic growth opportunities. Additionally, countries that are investing in sustainable energy are likely to be more competitive in the global economy, as they will have lower energy costs and a more diversified and secure energy mix. This can help to attract new businesses and industries to the country, further stimulating economic growth and job creation ( Kemp and Loorbach, 2006 ; Barbir, 2009 ; Salam and Khan, 2018 ; Tirkolaee et al., 2020b ).

The relationship between energy and the economy is a complex one with many facets. Energy is a crucial input for economic growth and development. Nevertheless, the manners in which energy is generated, dispersed, and consumed can result in significant effects on both the economy and society. Energy’s impact on the economy is mainly through its role as an input for production. It is used in various economic activities such as manufacturing, transportation, agriculture, and services. The cost and accessibility of energy exert substantial influence on production costs and the competitive edge of industries, particularly energy-intensive sectors like manufacturing. The pivotal role of energy in shaping frameworks for economic growth and progress is of great significance. Historically, access to affordable and abundant energy sources has been a crucial catalyst for economic development, particularly in economies undergoing industrialization. Additionally, the connection between energy and the economy is influenced by political and institutional factors. Energy policy choices, such as subsidies and regulations, can significantly shape the distribution of economic advantages and costs. Moreover, aspects such as vested interests, international trade, and resource allocation can influence the political economy of energy production and consumption. Energy’s role in shaping patterns of social inequality and vulnerability is also important. Access to reliable and affordable energy services is a fundamental aspect of meeting basic human needs such as heating, lighting, and cooking, as well as accessing education, healthcare, and other services. However, patterns of energy access and consumption can be shaped by economic and social inequalities, leading to higher energy costs and exposure to energy-related risks such as energy poverty, energy insecurity, and environmental pollution for low-income households and marginalized communities ( Kelly-Richards et al., 2017 ; Bogdanov et al., 2021 ; Kabeyi and Olanrewaju, 2022 ). Technical aspects also have a notable impact on shaping the correlation between energy and the economy. However, the adoption and diffusion of new energy technologies can be influenced by factors such as financing, regulation, and cost-benefit analysis. Therefore, the relationship between energy and the economy is a complex one with many facets, shaped by a range of economic, social, political, institutional, and technological factors. Understanding this relationship is essential for developing effective policies and strategies for achieving sustainable and equitable economic development while addressing the environmental and social challenges associated with energy production and consumption. Further research is needed to deepen our understanding of this complex relationship and identify effective strategies for promoting sustainable energy transitions and building more resilient and inclusive economies ( Quitzow et al., 2019 ; Emna et al., 2022 ; Neacsa et al., 2022 ; Zhou et al., 2022 ; Zhu et al., 2023 ).

Figure 3 shows an overview of the economic benefits associated with transitioning to sustainable energy. The figure emphasizes the potential for enhanced energy security, decreased reliance on fossil fuels, job creation and economic growth, cost savings and improved energy efficiency, reduced economic costs related to climate change and air pollution, augmented energy independence, economic advantages for rural communities, and improved competitiveness and investment attraction. Each category represents a core aspect in which transitioning to sustainable energy could generate considerable economic benefits, and comprehending these benefits is pivotal in developing effective policies and strategies for achieving sustainable and equitable economic growth while addressing the environmental and social challenges linked with energy production and consumption.

FIGURE 3 . Economic benefits of the transition to sustainable energy.

4.4 The role of blockchain technology in the transition towards sustainable energy

The equilibrium of smart grids can be disturbed by changes in consumer behavior. Blockchain technology can provide solutions to integrate new disruptors into the existing industrial structure of the electricity market. By enabling peer-to-peer transactions, blockchain technology can enhance consumer empowerment, but it also poses challenges to the existing regulatory frameworks in the industry ( Brilliantova and Thurner, 2019 ; Jiang S. et al., 2022 ). Hence, it is crucial to design blockchain technology that addresses trust and regulatory structures in the industry. The electricity market is continuously changing, and blockchain technology offers solutions to integrate new disruptors into the existing industrial structure. Peer-to-peer (P2P) transactions using blockchain technology can increase consumer empowerment and challenge traditional regulatory frameworks in the industry. To address trust and regulatory structures, the text discusses four different approaches to designing blockchain technology for the electricity market: unlicensed, licensed, private, and a combination of both. Each approach has its own unique features and implications for consumer trust and regulatory structures in the industry. Comprehending the necessities of each strategy is crucial for the energy sector to assess the significance of developing blockchain technology for the electricity market. The article emphasizes that technological design is critical in determining the influence of commercial structures on consumer conduct and regulatory frameworks ( Liu et al., 2021 ; Nygaard and Silkoset, 2022 ).

P2P transactions facilitate the direct exchange of electricity between consumers, eliminating the need for traditional intermediaries such as utility companies. This decentralized trading approach empowers consumers by granting them the authority to determine the price of electricity and enables them to sell any excess energy they generate. This level of control over energy consumption and production represents a significant advantage of P2P transactions. Furthermore, the utilization of blockchain technology enhances transparency and security within the energy market, thereby fostering consumer trust. Transactions are recorded in an immutable ledger, reducing the risk of fraudulent or erroneous transactions and promoting market efficiency. By leveraging blockchain for P2P transactions, consumers are afforded increased empowerment in the electricity market, allowing them to actively participate and make informed decisions regarding their energy consumption and production ( Mannaro et al., 2017 ; Di Silvestre et al., 2020 ; Afzal et al., 2022 ; Chen W. et al., 2023 ).

The potential of blockchain technology lies in its ability to combat the problem of energy poverty in developing nations by boosting access to inexpensive and dependable energy sources. One of the primary hurdles in addressing energy poverty is the dearth of access to established financial systems and infrastructure, which makes it challenging to finance and distribute energy resources to underprivileged communities. Blockchain technology provides a decentralized platform for energy transactions, enabling peer-to-peer energy trading and facilitating the distribution of energy resources to underserved communities. Blockchain-based energy platforms can also address trust and transparency issues in the energy sector. By utilizing a tamper-proof and transparent ledger system, blockchain technology increases accountability and reduces the risk of fraud and corruption in energy transactions. This helps to establish trust between energy producers, distributors, and consumers, creating a more efficient and equitable energy market. Furthermore, blockchain technology can facilitate the integration of sustainable energy sources into the energy grid, which is critical in developing nations where sustainable energy sources like wind or solar may be more accessible and economical than conventional fossil fuel-based energy sources ( Enescu et al., 2020 ; Mukherjee et al., 2021 ; Almutairi et al., 2022 ; Govindan, 2022 ).

The implementation of blockchain-based energy platforms in developing countries is accompanied by various challenges that need to be addressed. The first challenge pertains to limited infrastructure, which includes insufficient access to reliable electricity, inadequate internet connectivity, and a lack of necessary hardware to support blockchain-based energy platforms. These infrastructure limitations can impede the deployment and maintenance of blockchain technology in developing countries. The second challenge revolves around the scarcity of technical expertise required for the successful implementation of blockchain technology. Proficiency in areas such as software development, cryptography, and cybersecurity is vital for the development, deployment, and upkeep of blockchain-based energy platforms. The limited availability of technical expertise in these domains poses difficulties in incorporating blockchain technology in developing countries. The third challenge involves the absence of a comprehensive regulatory framework that supports the deployment of blockchain technology in developing countries. The lack of regulatory guidelines creates ambiguity, heightening the risk of non-compliance, deterring investment, and impeding the growth of blockchain-based energy platforms. The fourth challenge concerns the economic viability of blockchain-based energy platforms in developing countries. Due to low levels of energy consumption and limited access to financing, attracting investments and achieving the economies of scale necessary for the financial sustainability of blockchain-based energy platforms can be challenging. Lastly, social and cultural factors play a significant role in the adoption of blockchain-based energy platforms in developing countries. Some communities may exhibit skepticism towards new technologies or have a preference for traditional energy sources, thereby creating obstacles to the widespread adoption of blockchain-based energy platforms. Addressing these challenges is crucial to ensure the successful implementation of blockchain-based energy platforms in developing countries. By overcoming these obstacles, the potential benefits of blockchain technology in enhancing energy access, efficiency, and transparency can be harnessed to support sustainable development and address energy challenges in these regions ( Giungato et al., 2017 ; Truby, 2018 ; Aybar-Mejía et al., 2021 ; Popkova et al., 2023 ).

The licensed approach to designing blockchain technology for the electricity market involves the use of licensed and regulated intermediaries to facilitate transactions between producers and consumers. This approach aims to provide a higher level of trust and security than unlicensed approaches, while still enabling the benefits of blockchain technology. In the licensed approach, licensed intermediaries act as trusted third parties to validate transactions and ensure compliance with regulatory requirements. These intermediaries are usually regulated by government agencies and must adhere to specific standards for security, transparency, and integrity. Additionally, they may be required to maintain records of transactions and provide reports to regulators. The primary advantage of the licensed approach is the higher level of trust and security it provides compared to unlicensed approaches. By utilizing licensed intermediaries, consumers can have greater confidence in the integrity of transactions and the regulatory compliance of market participants. This can help to reduce the risk of fraudulent and illegal activities in the electricity market. However, the licensed approach has some drawbacks. For instance, the use of licensed intermediaries can increase transaction costs and reduce market efficiency. Furthermore, the regulatory requirements for licensed intermediaries can be complex and may vary across different jurisdictions, making it challenging to implement a standardized approach to blockchain-based energy platforms ( Svetec et al., 2019 ; Yildizbasi, 2021 ; Lei et al., 2022 ).

Thus, blockchain technology holds the potential to make a substantial impact in promoting a sustainable energy future by augmenting the efficiency and transparency of energy markets and streamlining the incorporation of sustainable energy sources. The technology can aid in addressing some of the primary challenges confronting the worldwide energy sector, such as energy security, climate change, and sustainable growth. As mentioned, one of the ways that blockchain technology can enhance the efficiency and transparency of energy markets is through peer-to-peer energy trading. This allows consumers to determine electricity prices and sell any excess energy they generate, giving them greater control over their energy consumption and production. The transparency and security afforded by blockchain technology can also increase consumer trust in the market, as transactions are recorded in a tamper-proof ledger, reducing the risk of fraudulent or inaccurate transactions and increasing market efficiency. Blockchain technology can also contribute to promoting a sustainable energy future by simplifying the integration of sustainable energy sources into the energy grid. The technology can aid in addressing the obstacles presented by the sporadic nature of sustainable energy sources and the insufficiency of energy storage capacity. Decentralized energy systems can be built on blockchain-based energy platforms that allow for the seamless integration of sustainable energy sources. These platforms can streamline the effective administration and synchronization of energy resources, enabling the maximization of energy production and consumption. For example, blockchain-based energy platforms can be used to create virtual power plants that aggregate sustainable energy sources and use energy storage systems to smooth out fluctuations in energy supply and demand. Lastly, blockchain technology can have a pivotal function in promoting sustainable development by enhancing access to energy resources in underprivileged communities. Blockchain-based energy platforms provide a decentralized platform for energy transactions, enabling P2P energy trading and facilitating the distribution of energy resources to underserved communities. This is particularly beneficial in developing countries where traditional energy infrastructure may be lacking or unreliable ( Sweeney et al., 2020 ; Ante et al., 2021 ; Wünsche and Fernqvist, 2022 ; Mao et al., 2023 ).

The application of blockchain technology in the energy industry could face various potential challenges or limitations. Scalability is a major challenge, where an increase in the number of transactions on a blockchain can lead to slower transaction times and higher transaction fees, making blockchain-based energy platforms less efficient and less cost-effective than traditional energy systems. Another challenge is the technical complexity of blockchain technology, which necessitates specialized technical expertise to develop and maintain, making it difficult for energy companies and regulators to adopt and implement blockchain-based energy platforms. Interoperability is another issue of concern, as there are presently numerous distinct blockchain platforms, each with its distinct features and stipulations, making the consolidation of various blockchain-based energy platforms with one another and with conventional energy systems challenging. Furthermore, the highly regulated nature of the energy sector can create regulatory challenges that require new or updated regulations to ensure compliance with existing laws and regulations, leading to uncertainty and delaying the adoption of blockchain-based energy platforms. The security and privacy of user data are also significant concerns, and any breach of this data could result in significant risks for both consumers and market participants. Furthermore, there could be apprehensions about the storage and dissemination of confidential energy data on a public blockchain network. Furthermore, while blockchain technology can promote energy efficiency in certain contexts, it also requires significant energy consumption, particularly with proof-of-work consensus mechanisms, creating concerns about the environmental sustainability of blockchain-based energy platforms. In conclusion, the implementation of blockchain technology in the energy sector requires careful consideration of potential challenges and drawbacks, including scalability, technical complexity, interoperability, regulatory challenges, data privacy and security, and energy consumption. Addressing these challenges will require collaboration between stakeholders across different sectors, such as governments, energy companies, technology providers, and regulators ( Wang and Su, 2020 ; Wang et al., 2021 ; Juszczyk and Shahzad, 2022 ).

Policy frameworks play a pivotal role in supporting the implementation of energy systems based on blockchain technology. The successful integration of blockchain platforms into the energy sector necessitates the establishment of specific policy measures and regulatory frameworks. Standards aimed at ensuring interoperability between blockchain platforms and existing energy infrastructure are crucial for facilitating seamless integration and optimizing the exchange of data. Regulations governing peer-to-peer energy trading, smart contracts, and the protection of customer data are vital for ensuring equitable and transparent transactions while safeguarding individual privacy. By offering incentives such as tax credits, governments can stimulate investments in blockchain energy projects, thereby fostering innovation and encouraging the widespread adoption of this technology. The integration of renewable energy certificates and carbon trading systems with blockchain platforms enhances the transparency and accountability of renewable energy markets. Policies that support decentralized energy production, smart grid infrastructure, and net metering are instrumental in effectively integrating distributed energy resources into the existing energy landscape. Additionally, the establishment of blockchain sandboxes provides controlled testing environments where policymakers can evaluate the feasibility and impact of new regulatory frameworks. By implementing these comprehensive policy frameworks, governments can foster an enabling environment for the successful deployment of blockchain-based energy systems, thereby promoting transparency, efficiency, and sustainability within the energy sector.

Figure 4 depicts the primary advantages and potential applications of blockchain technology in the energy sector. The first category, P2P Energy Trading, elucidates how blockchain technology can empower consumers, enhance transparency, and augment security in the energy market. The second category, Integration of Renewable Energy Sources, illustrates how blockchain technology can enable the seamless integration of renewable energy sources, energy storage systems, and smart grid technologies. The third category, Addressing Energy Poverty, explores how blockchain technology can offer a decentralized platform for energy transactions, enabling peer-to-peer energy trading and facilitating the distribution of energy resources to underserved communities. Finally, the fourth category, Challenges and Potential Drawbacks, highlights the primary challenges and potential drawbacks associated with implementing blockchain technology in the energy sector, such as scalability, technical complexity, regulatory, data privacy and security, and environmental sustainability concerns. Comprehending these advantages and challenges is imperative in determining the significance of designing blockchain technology for the electricity market and promoting a sustainable energy future.

FIGURE 4 . Potential benefits and challenges of implementing blockchain technology in the energy sector.

5 Conclusion

A qualitative research methodology was used in this study, which involved an extensive literature review and content analysis, enabling an in-depth exploration of the research topic and addressing the study objectives. The objective of the study was to comprehensively investigate the challenges, opportunities, and role of blockchain technology in facilitating the transition towards sustainable energy sources. The transition towards sustainable energy is essential for environmental and economic reasons, as it reduces dependence on fossil fuels, mitigates the impacts of climate change, and promotes economic growth. Thus, the findings from this study can inform policy decisions and future research to promote sustainable energy solutions.

Several challenges were identified in the transition to sustainable energy, including high upfront costs, social and political resistance, and technological barriers. However, opportunities such as job creation, economic benefits, and a cleaner environment also exist. Blockchain technology has the potential to enable more efficient and transparent energy markets through peer-to-peer transactions and a distributed ledger. It can also facilitate the integration of renewable energy sources. However, addressing challenges such as scalability, technical complexity, and security risks is necessary for blockchain technology to effectively contribute to a sustainable energy transition. Policy changes that incentivize sustainable energy use and investment in renewable energy technologies are also crucial enablers.

The transition to sustainable energy sources offers promising economic prospects, including improved energy security, reduced dependence on imports, and potential for increased economic growth. However, achieving sustainable development requires policies that balance energy supply, economic growth, and environmental protection. The sustainable energy sector faces the challenge of attracting and developing a skilled workforce to meet growing demand. Educational institutions have an important role to play in bridging the skills gap and preparing students for careers in sustainable energy.

Also, addressing the skills gaps and implementing effective training strategies is crucial for building a proficient workforce in the renewable energy sector. The identified skills gaps encompass technical proficiencies, understanding of renewable energy technologies, engineering expertise, data analytics skills, and knowledge of regulations and policy frameworks. Educational institutions play a vital role in developing these skills through targeted sustainable energy programs, cross-disciplinary courses, apprenticeships, and vocational training. Integrating renewable energy and blockchain topics into mainstream engineering and business curriculums ensures that graduates are well-equipped for the evolving energy landscape. Reskilling programs are also essential for enabling a smooth transition for individuals from fossil fuel-related occupations to renewable energy careers. By addressing these skills gaps and implementing comprehensive training strategies, stakeholders can foster a competent workforce capable of driving the successful implementation of renewable energy technologies and the integration of blockchain platforms. This, in turn, will contribute to the advancement of sustainable energy systems and the achievement of global climate goals.

Looking towards the future, there are several promising trends that have the potential to accelerate the transition towards sustainable energy systems. Advancements in renewable energy technologies, coupled with declining costs, are expected to drive their widespread adoption on a global scale. Policy frameworks at various levels of governance have the capacity to evolve progressively, becoming more supportive of sustainability initiatives, as public concern over climate change continues to grow. The emergence of potentially disruptive technologies such as blockchain, artificial intelligence, and advanced data analytics holds promise for unlocking new capabilities and business models within the energy sector. Furthermore, the green energy workforce is anticipated to experience significant expansion, supported by targeted training initiatives offered by educational institutions and industry partners. Innovative financing mechanisms can play a pivotal role in making the economics of sustainability more viable, while community-based approaches offer creative solutions to overcome local resistance. By comprehensively capitalizing on these opportunities, a future can be envisioned where affordable, decentralized, and clean energy empowers societies worldwide. To translate these promising trends into tangible reality, further rigorous interdisciplinary research and enhanced collaboration among diverse stakeholders will be crucial.

In conclusion, transitioning to sustainable energy sources is crucial for environmental and economic sustainability but faces significant challenges that require concerted efforts from governments, companies, and educational institutions. The utilization of promising technologies such as blockchain and policy changes incentivizing renewable energy can help enable a sustainable energy transition. This transition offers promising economic benefits and prospects for job creation in the sustainable energy sector. However, addressing the skills gap through targeted training programs and continued education is critical to realize the full potential of the sustainable energy transition. The insights from this study can inform policies and strategies to promote sustainable energy solutions and build a more sustainable and resilient economy.

Data availability statement

The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.

Author contributions

YL: Conceptualization, Data curation, Formal Analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing—original draft, Writing—review and editing.

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Acknowledgments

The authors would like to express their sincere appreciation to all those who provided valuable support and assistance during the research process.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Abban, O. J., Hongxing, Y., Nuta, A. C., Dankyi, A. B., Ofori, C., and Cobbinah, J. (2022). Renewable energy, economic growth, and CO2 emissions contained Co-movement in african oil-producing countries: A wavelet based analysis. Energy Strategy Rev. 44, 100977. doi:10.1016/j.esr.2022.100977

CrossRef Full Text | Google Scholar

Abbasi, T., and Abbasi, S. (2011). Small hydro and the environmental implications of its extensive utilization. Renew. Sustain. energy Rev. 15, 2134–2143. doi:10.1016/j.rser.2010.11.050

Adamowicz, M. (2022). Green deal, green growth and green economy as a means of support for attaining the sustainable development goals. Sustainability 14, 5901. doi:10.3390/su14105901

Afzal, M., Li, J., Amin, W., Huang, Q., Umer, K., Ahmad, S. A., et al. (2022). Role of blockchain technology in transactive energy market: A review. Sustain. Energy Technol. Assessments 53, 102646. doi:10.1016/j.seta.2022.102646

Ahl, A., Yarime, M., Goto, M., Chopra, S. S., Kumar, N. M., Tanaka, K., et al. (2020). Exploring blockchain for the energy transition: opportunities and challenges based on a case study in Japan. Renew. Sustain. energy Rev. 117, 109488. doi:10.1016/j.rser.2019.109488

Akram, S. V., Malik, P. K., Singh, R., Anita, G., and Tanwar, S. (2020). Adoption of blockchain technology in various realms: opportunities and challenges. Secur. Priv. 3, e109. doi:10.1002/spy2.109

Al-Housani, M. I., Koç, M., and Al-Sada, M. S. (2023). Investigations on entrepreneurship needs, challenges, and models for countries in transition to sustainable development from resource-based economy—Qatar as a case. Sustainability 15, 7537. doi:10.3390/su15097537

Al-Shetwi, A. Q. (2022). Sustainable development of renewable energy integrated power sector: trends, environmental impacts, and recent challenges. Sci. Total Environ. 822, 153645. doi:10.1016/j.scitotenv.2022.153645

PubMed Abstract | CrossRef Full Text | Google Scholar

Almutairi, K., Hosseini Dehshiri, S. J., Hosseini Dehshiri, S. S., Hoa, A. X., Arockia Dhanraj, J., Mostafaeipour, A., et al. (2022). Blockchain Technology application challenges in renewable energy supply chain management. Environ. Sci. Pollut. Res. 30, 72041–72058. doi:10.1007/s11356-021-18311-7

Amin, M., Shah, H. H., Fareed, A. G., Khan, W. U., Chung, E., Zia, A., et al. (2022). Hydrogen production through renewable and non-renewable energy processes and their impact on climate change. Int. J. hydrogen energy 47, 33112–33134. doi:10.1016/j.ijhydene.2022.07.172

Andoni, M., Robu, V., Flynn, D., Abram, S., Geach, D., Jenkins, D., et al. (2019). Blockchain technology in the energy sector: A systematic review of challenges and opportunities. Renew. Sustain. energy Rev. 100, 143–174. doi:10.1016/j.rser.2018.10.014

Ansari, M. A., Akram, V., and Haider, S. (2022). A link between productivity, globalisation and carbon emissions: evidence from emissions by coal, oil and gas. Environ. Sci. Pollut. Res. 29, 33826–33843. doi:10.1007/s11356-022-18557-9

Ante, L., Steinmetz, F., and Fiedler, I. (2021). Blockchain and energy: A bibliometric analysis and review. Renew. Sustain. Energy Rev. 137, 110597. doi:10.1016/j.rser.2020.110597

Arabian, M., Ghadiri Nejad, M., and Barenji, R. V. (2022). Blockchain technology in supply chain management: Challenge and future perspectives, industry 4.0: Technologies, applications, and challenges . Springer , 201–220.

Armaroli, N., and Balzani, V. (2007). The future of energy supply: challenges and opportunities. Angew. Chem. Int. Ed. 46, 52–66. doi:10.1002/anie.200602373

Asif, M., Salman, M. U., Anwar, S., Gul, M., and Aslam, R. (2022). Renewable and non-renewable energy resources of Pakistan and their applicability under the current scenario in Pakistan. OPEC Energy Rev. 46, 310–339. doi:10.1111/opec.12230

Aybar-Mejía, M., Rosario-Weeks, D., Mariano-Hernández, D., and Domínguez-Garabitos, M. (2021). An approach for applying blockchain technology in centralized electricity markets. Electr. J. 34, 106918. doi:10.1016/j.tej.2021.106918

Bahlouli, K., Lotfi, N., and Ghadiri Nejad, M. (2023). A new multi-heuristic method to optimize the ammonia–water power/cooling cycle combined with an HCCI engine. Sustainability 15, 6545. doi:10.3390/su15086545

Barbir, F. (2009). Transition to renewable energy systems with hydrogen as an energy carrier. Energy 34, 308–312. doi:10.1016/j.energy.2008.07.007

Barenji, R. V., and Nejad, M. G. (2022). Intelligent and fuzzy techniques in aviation 4.0 . Theory and Applications , 411–430.Blockchain applications in UAV-towards aviation 4.0

Başkaya, Ş., Başkaya, E., and Sari, A. (2011). The principal negative environmental impacts of small hydropower plants in Turkey. Afr. J. Agric. Res. 6, 3284–3290.

Google Scholar

Bayulgen, O. (2020). Localizing the energy transition: town-level political and socio-economic drivers of clean energy in the United States. Energy Res. Soc. Sci. 62, 101376. doi:10.1016/j.erss.2019.101376

Bogdanov, D., Ram, M., Aghahosseini, A., Gulagi, A., Oyewo, A. S., Child, M., et al. (2021). Low-cost renewable electricity as the key driver of the global energy transition towards sustainability. Energy 227, 120467. doi:10.1016/j.energy.2021.120467

Bose, S., Dong, G., Simpson, A., Bose, S., Dong, G., and Simpson, A. (2019). Financing clean technology innovation and the transition to renewable energy. Financial Ecosyst. Role Finance Achiev. Sustain. , 339–368.

Brilliantova, V., and Thurner, T. W. (2019). Blockchain and the future of energy. Technol. Soc. 57, 38–45. doi:10.1016/j.techsoc.2018.11.001

Broman, G. I., and Robèrt, K.-H. (2017). A framework for strategic sustainable development. J. Clean. Prod. 140, 17–31. doi:10.1016/j.jclepro.2015.10.121

Bulavskaya, T., and Reynès, F. (2018). Job creation and economic impact of renewable energy in The Netherlands. Renew. Energy 119, 528–538. doi:10.1016/j.renene.2017.09.039

Cai, T., Dong, M., Chen, K., and Gong, T. (2022). Methods of participating power spot market bidding and settlement for renewable energy systems. Energy Rep. 8, 7764–7772. doi:10.1016/j.egyr.2022.05.291

Cantarero, M. M. V. (2020). Of renewable energy, energy democracy, and sustainable development: A roadmap to accelerate the energy transition in developing countries. Energy Res. Soc. Sci. 70, 101716. doi:10.1016/j.erss.2020.101716

Chai, Q., and Zhang, X. (2010). Technologies and policies for the transition to a sustainable energy system in China. Energy 35, 3995–4002. doi:10.1016/j.energy.2010.04.033

Chen, J., Huang, S., and Kamran, H. W. (2023a). Empowering sustainability practices through energy transition for sustainable development goal 7: the role of energy patents and natural resources among European union economies through advanced panel. Energy Policy 176, 113499. doi:10.1016/j.enpol.2023.113499

Chen, W., Zou, W., Zhong, K., and Aliyeva, A. (2023b). Machine learning assessment under the development of green technology innovation: A perspective of energy transition. Renew. Energy 214, 65–73. doi:10.1016/j.renene.2023.05.108

Chen, Y. (2022). Research on collaborative innovation of key common technologies in new energy vehicle industry based on digital twin technology. Energy Rep. 8, 15399–15407. doi:10.1016/j.egyr.2022.11.120

Chenic, A. Ș., Cretu, A. I., Burlacu, A., Moroianu, N., Vîrjan, D., Huru, D., et al. (2022). Logical analysis on the strategy for a sustainable transition of the world to green energy—2050. Smart cities and villages coupled to renewable energy sources with low carbon footprint. Sustainability 14, 8622. doi:10.3390/su14148622

Chien, F. (2022). How renewable energy and non-renewable energy affect environmental excellence in N-11 economies? Renew. Energy 196, 526–534. doi:10.1016/j.renene.2022.07.013

Chu, S., and Majumdar, A. (2012). Opportunities and challenges for a sustainable energy future. nature 488, 294–303. doi:10.1038/nature11475

Dhar, A., Naeth, M. A., Jennings, P. D., and El-Din, M. G. (2020). Perspectives on environmental impacts and a land reclamation strategy for solar and wind energy systems. Sci. total Environ. 718, 134602. doi:10.1016/j.scitotenv.2019.134602

Di Silvestre, M. L., Gallo, P., Guerrero, J. M., Musca, R., Sanseverino, E. R., Sciumè, G., et al. (2020). Blockchain for power systems: current trends and future applications. Renew. Sustain. Energy Rev. 119, 109585. doi:10.1016/j.rser.2019.109585

Di Somma, M., and Graditi, G. (2022). Challenges and opportunities of the energy transition and the added value of energy systems integration. Technol. Integr. energy Syst. Netw. , 1–14. doi:10.1002/9783527833634.ch1

Dominković, D. F., Bačeković, I., Pedersen, A. S., and Krajačić, G. (2018). The future of transportation in sustainable energy systems: opportunities and barriers in a clean energy transition. Renew. Sustain. Energy Rev. 82, 1823–1838. doi:10.1016/j.rser.2017.06.117

Dong, F., Li, Y., Gao, Y., Zhu, J., Qin, C., and Zhang, X. (2022). Energy transition and carbon neutrality: exploring the non-linear impact of renewable energy development on carbon emission efficiency in developed countries. Resour. Conservation Recycl. 177, 106002. doi:10.1016/j.resconrec.2021.106002

Dwivedi, A., Agrawal, D., Jha, A., and Mathiyazhagan, K. (2023). Studying the interactions among industry 5.0 and circular supply chain: towards attaining sustainable development. Comput. Industrial Eng. 176, 108927. doi:10.1016/j.cie.2022.108927

Egré, D., and Milewski, J. C. (2002). The diversity of hydropower projects. Energy policy 30, 1225–1230. doi:10.1016/s0301-4215(02)00083-6

Emna, O., Chtourou, N., and Bazin, D. (2022). Technological, economic, institutional, and psychosocial aspects of the transition to renewable energies: A critical literature review of a multidimensional process. Renew. Energy Focus 43, 37–49. doi:10.1016/j.ref.2022.08.004

Enescu, F. M., Bizon, N., Onu, A., Răboacă, M. S., Thounthong, P., Mazare, A. G., et al. (2020). Implementing blockchain technology in irrigation systems that integrate photovoltaic energy generation systems. Sustainability 12, 1540. doi:10.3390/su12041540

Erat, S., Telli, A., Ozkendir, O. M., and Demir, B. (2021). Turkey’s energy transition from fossil-based to renewable up to 2030: milestones, challenges and opportunities. Clean Technol. Environ. Policy 23, 401–412. doi:10.1007/s10098-020-01949-1

Etheridge, D. (2000). Unsteady flow effects due to fluctuating wind pressures in natural ventilation design—Mean flow rates. Build. Environ. 35, 111–133. doi:10.1016/s0360-1323(99)00007-4

Fang, J., Gozgor, G., Mahalik, M. K., Mallick, H., and Padhan, H. (2022). Does urbanisation induce renewable energy consumption in emerging economies? The role of education in energy switching policies. Energy Econ. 111, 106081. doi:10.1016/j.eneco.2022.106081

Farghali, M., Osman, A. I., Mohamed, I. M., Chen, Z., Chen, L., Ihara, I., et al. (2023). Strategies to save energy in the context of the energy crisis: A review. Environ. Chem. Lett. 21, 2003–2039. doi:10.1007/s10311-023-01591-5

Gawusu, S., Zhang, X., Ahmed, A., Jamatutu, S. A., Miensah, E. D., Amadu, A. A., et al. (2022). Renewable energy sources from the perspective of blockchain integration: from theory to application. Sustain. Energy Technol. Assessments 52, 102108. doi:10.1016/j.seta.2022.102108

Ghasemi, M., Nejad, M. G., and Aghaei, I. (2021). Knowledge management orientation and operational performance relationship in medical tourism (overview of the model performance in the COVID-19 pandemic and post-pandemic era). Health Serv. Manag. Res. 34, 208–222. doi:10.1177/0951484820971438

Ghasemi, M., Nejad, M. G., Alsaadi, N., Abdel-Jaber, M. t., Ab Yajid, M. S., and Habib, M. (2022). Performance measurment and lead-time reduction in epc project-based organizations: A mathematical modeling approach. Math. Problems Eng. 2022, 1–15. doi:10.1155/2022/5767356

Gielen, D., Boshell, F., Saygin, D., Bazilian, M. D., Wagner, N., and Gorini, R. (2019). The role of renewable energy in the global energy transformation. Energy strategy Rev. 24, 38–50. doi:10.1016/j.esr.2019.01.006

Gielen, D., Boshell, F., and Saygin, D. (2016). Climate and energy challenges for materials science. Nat. Mater. 15, 117–120. doi:10.1038/nmat4545

Giungato, P., Rana, R., Tarabella, A., and Tricase, C. (2017). Current trends in sustainability of bitcoins and related blockchain technology. Sustainability 9, 2214. doi:10.3390/su9122214

Gleick, P. H. (1992). Environmental consequences of hydroelectric development: the role of facility size and type. Energy 17, 735–747. doi:10.1016/0360-5442(92)90116-h

Goli, A. (2023). Integration of blockchain-enabled closed-loop supply chain and robust product portfolio design. Comput. Industrial Eng. 179, 109211. doi:10.1016/j.cie.2023.109211

Govindan, K. (2022). Tunneling the barriers of blockchain technology in remanufacturing for achieving sustainable development goals: A circular manufacturing perspective. Bus. Strategy Environ. 31, 3769–3785. doi:10.1002/bse.3031

Guo, B., Feng, Y., and Hu, F. (2023a). Have carbon emission trading pilot policy improved urban innovation capacity? Evidence from a quasi-natural experiment in China. Environ. Sci. Pollut. Res. , 1–14. doi:10.1007/s11356-023-25699-x

Guo, L., Nowzari, R., Saygin, H., Al-Bahrani, M., Nejad, M. G., and Baghaei, S. (2023b). Experimental investigation and two-factor factorial analysis of a solar air heater with scrap wire meshes and cans as energy storage components. J. Energy Storage 70, 107998. doi:10.1016/j.est.2023.107998

Hassan, S. S., Williams, G. A., and Jaiswal, A. K. (2019). Moving towards the second generation of lignocellulosic biorefineries in the EU: drivers, challenges, and opportunities. Renew. Sustain. Energy Rev. 101, 590–599. doi:10.1016/j.rser.2018.11.041

Hennig, T., Wang, W., Feng, Y., Ou, X., and He, D. (2013). Review of Yunnan's hydropower development. Comparing small and large hydropower projects regarding their environmental implications and socio-economic consequences. Renew. Sustain. Energy Rev. 27, 585–595. doi:10.1016/j.rser.2013.07.023

Hernandez, R. R., Easter, S., Murphy-Mariscal, M. L., Maestre, F. T., Tavassoli, M., Allen, E. B., et al. (2014). Environmental impacts of utility-scale solar energy. Renew. Sustain. energy Rev. 29, 766–779. doi:10.1016/j.rser.2013.08.041

Herzog, A. V., Lipman, T. E., and Kammen, D. M. (2001). Renewable energy sources, Encyclopedia of life support systems (EOLSS). Forerunn. Volume-‘Perspectives Overv. life support Syst. Sustain. Dev. , 76.

Hoang, A. T., Nižetić, S., Olcer, A. I., Ong, H. C., Chen, W.-H., Chong, C. T., et al. (2021). Impacts of COVID-19 pandemic on the global energy system and the shift progress to renewable energy: opportunities, challenges, and policy implications. Energy Policy 154, 112322. doi:10.1016/j.enpol.2021.112322

Holechek, J. L., Geli, H. M., Sawalhah, M. N., and Valdez, R. (2022). A global assessment: can renewable energy replace fossil fuels by 2050? Sustainability 14, 4792. doi:10.3390/su14084792

Hosenuzzaman, M., Rahim, N. A., Selvaraj, J., Hasanuzzaman, M., Malek, A. A., and Nahar, A. (2015). Global prospects, progress, policies, and environmental impact of solar photovoltaic power generation. Renew. Sustain. energy Rev. 41, 284–297. doi:10.1016/j.rser.2014.08.046

Hu, F., Xi, X., and Zhang, Y. (2021). Influencing mechanism of reverse knowledge spillover on investment enterprises’ technological progress: an empirical examination of Chinese firms. Technol. Forecast. Soc. Change 169, 120797. doi:10.1016/j.techfore.2021.120797

Ibeanu, C., Ghadiri Nejad, M., and Ghasemi, M. (2023). Developing effective project management strategy for urban flood disaster prevention project in EDO state capital, Nigeria. Urban Sci. 7, 37. doi:10.3390/urbansci7020037

Inês, C., Guilherme, P. L., Esther, M.-G., Swantje, G., Stephen, H., and Lars, H. (2020). Regulatory challenges and opportunities for collective renewable energy prosumers in the EU. Energy Policy 138, 111212. doi:10.1016/j.enpol.2019.111212

Ishaq, H., Dincer, I., and Crawford, C. (2022). A review on hydrogen production and utilization: challenges and opportunities. Int. J. Hydrogen Energy 47, 26238–26264. doi:10.1016/j.ijhydene.2021.11.149

Islam, M. M., Irfan, M., Shahbaz, M., and Vo, X. V. (2022). Renewable and non-renewable energy consumption in Bangladesh: the relative influencing profiles of economic factors, urbanization, physical infrastructure and institutional quality. Renew. Energy 184, 1130–1149. doi:10.1016/j.renene.2021.12.020

Jaber, S. (2013). Environmental impacts of wind energy. J. Clean Energy Technol. 1, 251–254. doi:10.7763/jocet.2013.v1.57

Japir Bataineh, M. A., Ghasemi, M., and Ghadiri Nejad, M. (2023). The role of green training in the ministry of education’s corporate environmental performance: A mediation analysis of organizational citizenship behavior towards the environment and moderation role of perceived organizational support. Sustainability 15, 8398. doi:10.3390/su15108398

Jenniches, S. (2018). Assessing the regional economic impacts of renewable energy sources–A literature review. Renew. Sustain. Energy Rev. 93, 35–51. doi:10.1016/j.rser.2018.05.008

Jiang, J., Zhang, L., Wen, X., Valipour, E., and Nojavan, S. (2022a). Risk-based performance of power-to-gas storage technology integrated with energy hub system regarding downside risk constrained approach. Int. J. Hydrogen Energy 47, 39429–39442. doi:10.1016/j.ijhydene.2022.09.115

Jiang, S., Jakobsen, K., Bueie, J., Li, J., and Haro, P. H. (2022b). A tertiary review on blockchain and sustainability with focus on Sustainable Development Goals. IEEE access 10, 114975–115006. doi:10.1109/access.2022.3217683

Jie, H., Khan, I., Alharthi, M., Zafar, M. W., and Saeed, A. (2023). Sustainable energy policy, socio-economic development, and ecological footprint: the economic significance of natural resources, population growth, and industrial development. Util. Policy 81, 101490. doi:10.1016/j.jup.2023.101490

Jumani, S., Rao, S., Machado, S., and Prakash, A. (2017). Big concerns with small projects: evaluating the socio-ecological impacts of small hydropower projects in India. Ambio 46, 500–511. doi:10.1007/s13280-016-0855-9

Juszczyk, O., and Shahzad, K. (2022). Blockchain technology for renewable energy: principles, applications and prospects. Energies 15, 4603. doi:10.3390/en15134603

Kabeyi, M. J. B., and Olanrewaju, O. A. (2022). Sustainable energy transition for renewable and low carbon grid electricity generation and supply. Front. Energy Res. 9, 1032. doi:10.3389/fenrg.2021.743114

Kanwal, S., Mehran, M. T., Hassan, M., Anwar, M., Naqvi, S. R., and Khoja, A. H. (2022). An integrated future approach for the energy security of Pakistan: replacement of fossil fuels with syngas for better environment and socio-economic development. Renew. Sustain. Energy Rev. 156, 111978. doi:10.1016/j.rser.2021.111978

Kelly-Richards, S., Silber-Coats, N., Crootof, A., Tecklin, D., and Bauer, C. (2017). Governing the transition to renewable energy: A review of impacts and policy issues in the small hydropower boom. Energy Policy 101, 251–264. doi:10.1016/j.enpol.2016.11.035

Kemp, R., and Loorbach, D. (2006). Dutch policies to manage the transition to sustainable energy .

Kern, F., and Smith, A. (2008). Restructuring energy systems for sustainability? Energy transition policy in The Netherlands. Energy policy 36, 4093–4103. doi:10.1016/j.enpol.2008.06.018

Kocak, E., Ulug, E. E., and Oralhan, B. (2023). The impact of electricity from renewable and non-renewable sources on energy poverty and greenhouse gas emissions (GHGs): empirical evidence and policy implications. Energy 272, 127125. doi:10.1016/j.energy.2023.127125

Kumar, N. M., Chopra, S. S., and Rajput, P. (2020). Life cycle assessment and environmental impacts of solar PV systems, Photovoltaic solar energy conversion . Elsevier , 391–411.

Kuzemko, C., Lockwood, M., Mitchell, C., and Hoggett, R. (2016). Governing for sustainable energy system change: politics, contexts and contingency. Energy Res. Soc. Sci. 12, 96–105. doi:10.1016/j.erss.2015.12.022

Kyriakopoulos, G. L., Streimikiene, D., and Baležentis, T. (2022). Addressing challenges of low-carbon energy transition . MDPI , 5718.

Lei, Y.-T., Ma, C.-Q., Mirza, N., Ren, Y.-S., Narayan, S. W., and Chen, X.-Q. (2022). A renewable energy microgrids trading management platform based on permissioned blockchain. Energy Econ. 115, 106375. doi:10.1016/j.eneco.2022.106375

Leiserowitz, A. (2007). International public opinion, perception, and understanding of global climate change. Hum. Dev. Rep. 2008, 31.

Li, Q.-K., Lin, H., Tan, X., and Du, S. (2018). H∞ consensus for multiagent-based supply chain systems under switching topology and uncertain demands. IEEE Trans. Syst. Man, Cybern. Syst. 50, 4905–4918. doi:10.1109/tsmc.2018.2884510

Liu, H., Khan, I., Zakari, A., and Alharthi, M. (2022b). Roles of trilemma in the world energy sector and transition towards sustainable energy: A study of economic growth and the environment. Energy Policy 170, 113238. doi:10.1016/j.enpol.2022.113238

Liu, J., Lv, J., Dinçer, H., Yüksel, S., and Karakuş, H. (2021). Selection of renewable energy alternatives for green blockchain investments: A hybrid IT2-based fuzzy modelling. Archives Comput. Methods Eng. 28, 3687–3701. doi:10.1007/s11831-020-09521-2

Liu, L., Tang, Y., and Liu, D. (2022a). Investigation of future low-carbon and zero-carbon fuels for marine engines from the view of thermal efficiency. Energy Rep. 8, 6150–6160. doi:10.1016/j.egyr.2022.04.058

Liu, X., Li, Z., Fu, X., Yin, Z., Liu, M., Yin, L., et al. (2023). Monitoring house vacancy dynamics in the pearl river delta region: A method based on NPP-viirs night-time light remote sensing images. Land 12, 831. doi:10.3390/land12040831

Lovins, A. B. (1976). Energy strategy: the road not taken. Foreign Aff. 55, 65. doi:10.2307/20039628

Lovins, A. B. (1974). World energy strategies: facts, issues and options for policies that transcend today's ‘ad-hocracy. Bull. Atomic Sci. 30, 14–32. doi:10.1080/00963402.1974.11458114

Lu, Y., Khan, Z. A., Alvarez-Alvarado, M. S., Zhang, Y., Huang, Z., and Imran, M. (2020). A critical review of sustainable energy policies for the promotion of renewable energy sources. Sustainability 12, 5078. doi:10.3390/su12125078

Luo, J., Zhuo, W., and Xu, B. (2023). Optimal NGO size of human resources and governance quality of entrepreneurship in circular economy . Management Decision .The bigger, the better?

Luo, L., Tang, Q., and Lan, Y. C. (2013). Comparison of propensity for carbon disclosure between developing and developed countries: A resource constraint perspective. Account. Res. J. 26, 6–34. doi:10.1108/arj-04-2012-0024

Magar, V., Peña, A., Hahmann, A. N., Pacheco-Rojas, D. A., García-Hernández, L. S., and Gross, M. S. (2023). Wind energy and the energy transition: challenges and opportunities for Mexico. Sustainability 15, 5496. doi:10.3390/su15065496

Mahajan, B. (2012). Negative environment impact of solar energy . Environmental Science and Policy , 1–6.

Mahmud, M. P., Huda, N., Farjana, S. H., and Lang, C. (2018). Environmental impacts of solar-photovoltaic and solar-thermal systems with life-cycle assessment. Energies 11, 2346. doi:10.3390/en11092346

Majid, M. (2020). Renewable energy for sustainable development in India: current status, future prospects, challenges, employment, and investment opportunities. Energy, Sustain. Soc. 10, 1–36.

Mannaro, K., Pinna, A., and Marchesi, M. (2017). “Crypto-trading: blockchain-oriented energy market,” in 2017 AEIT International Annual Conference , Cagliari, Italy , 20-22 September 2017 ( IEEE ), 1–5.

Mao, Q., Ma, X., and Sun, Y. (2023). Study of impacts of blockchain technology on renewable energy resource findings. Renew. Energy 211, 802–808. doi:10.1016/j.renene.2023.05.038

Meinshausen, M., Lewis, J., McGlade, C., Gütschow, J., Nicholls, Z., Burdon, R., et al. (2022). Realization of Paris Agreement pledges may limit warming just below 2 C. Nature 604, 304–309. doi:10.1038/s41586-022-04553-z

Mendecka, B., and Lombardi, L. (2019). Life cycle environmental impacts of wind energy technologies: A review of simplified models and harmonization of the results. Renew. Sustain. Energy Rev. 111, 462–480. doi:10.1016/j.rser.2019.05.019

Merven, B., Hartley, F., and Arndt, C. (2019). Quantifying the macro-and socio-economic benefits of a transition to renewable energy in South Africa .

Meydani, A. (2023). “Development planning policies for renewable electricity generation in competition with fossil electricity,” in 2023 8th International Conference on Technology and Energy Management (ICTEM) , Mazandaran, Babol, Iran, Islamic Republic of , 08-09 February 2023 ( IEEE ), 1–7.

Mirzaei, N., Nejad, M. G., and Fernandes, N. O. (2021). Combining line balancing methods and discrete event simulation: A case study from a metalworking company. Int. J. Industrial Eng. Manag. 12, 14–24. doi:10.24867/ijiem-2021-1-273

Mohideen, M. M., Subramanian, B., Sun, J., Ge, J., Guo, H., Radhamani, A. V., et al. (2023). Techno-economic analysis of different shades of renewable and non-renewable energy-based hydrogen for fuel cell electric vehicles. Renew. Sustain. Energy Rev. 174, 113153. doi:10.1016/j.rser.2023.113153

Mourtzis, D., Angelopoulos, J., and Panopoulos, N. (2022). A literature review of the challenges and opportunities of the transition from industry 4.0 to society 5.0. Energies 15, 6276. doi:10.3390/en15176276

Mukherjee, A. A., Singh, R. K., Mishra, R., and Bag, S. (2021). Application of blockchain technology for sustainability development in agricultural supply chain: justification framework. Operations Manag. Res. 15, 46–61. doi:10.1007/s12063-021-00180-5

Nautiyal, H., and Goel, V. (2020). Sustainability assessment of hydropower projects. J. Clean. Prod. 265, 121661. doi:10.1016/j.jclepro.2020.121661

Nazir, M. S., Ali, N., Bilal, M., and Iqbal, H. M. (2020). Potential environmental impacts of wind energy development: A global perspective. Curr. Opin. Environ. Sci. Health 13, 85–90. doi:10.1016/j.coesh.2020.01.002

Neacsa, A., Rehman Khan, S. A., Panait, M., and Apostu, S. A. (2022). “The transition to renewable energy—a sustainability issue?,” in Energy transition: Economic, social and environmental dimensions ( Springer ), 29–72.

Nejad, M. G., and Kashan, A. H. (2019). An effective grouping evolution strategy algorithm enhanced with heuristic methods for assembly line balancing problem. J. Adv. Manuf. Syst. 18, 487–509. doi:10.1142/s0219686719500264

Nnabuife, S. G., Ugbeh-Johnson, J., Okeke, N. E., and Ogbonnaya, C. (2022). Present and projected developments in hydrogen production: A technological review*. Carbon Capture Sci. Technol. 3, 100042. doi:10.1016/j.ccst.2022.100042

Nygaard, A., and Silkoset, R. (2022). Sustainable development and greenwashing: how blockchain technology information can empower green consumers. Bus. Strategy Environ. doi:10.1002/bse.3338

Oladosu, G. A., Werble, J., Tingen, W., Witt, A., Mobley, M., and O'Connor, P. (2021). Costs of mitigating the environmental impacts of hydropower projects in the United States. Renew. Sustain. Energy Rev. 135, 110121. doi:10.1016/j.rser.2020.110121

Osorio-Aravena, J. C., Aghahosseini, A., Bogdanov, D., Caldera, U., Ghorbani, N., Mensah, T. N. O., et al. (2021). The impact of renewable energy and sector coupling on the pathway towards a sustainable energy system in Chile. Renew. Sustain. Energy Rev. 151, 111557. doi:10.1016/j.rser.2021.111557

Otoum, S., Al Ridhawi, I., and Mouftah, H. (2022). A federated learning and blockchain-enabled sustainable energy trade at the edge: A framework for industry 4.0. IEEE Internet Things J. 10, 3018–3026. doi:10.1109/jiot.2022.3140430

Owusu, P. A., and Asumadu-Sarkodie, S. (2016). A review of renewable energy sources, sustainability issues and climate change mitigation. Cogent Eng. 3, 1167990. doi:10.1080/23311916.2016.1167990

Pang, M., Zhang, L., Ulgiati, S., and Wang, C. (2015). Ecological impacts of small hydropower in China: insights from an emergy analysis of a case plant. Energy policy 76, 112–122. doi:10.1016/j.enpol.2014.10.009

Peeters, J. (2012). The place of social work in sustainable development: towards ecosocial practice. Int. J. Soc. Welf. 21, 287–298. doi:10.1111/j.1468-2397.2011.00856.x

Perelman, L. J. (1980). Speculations on the transition to sustainable energy. Ethics 90, 392–416. doi:10.1086/292170

Perera, F. (2018). Pollution from fossil-fuel combustion is the leading environmental threat to global pediatric health and equity: solutions exist. Int. J. Environ. Res. public health 15, 16. doi:10.3390/ijerph15010016

Pérez, M. D. L. E. M., Scholten, D., and Stegen, K. S. (2019). The multi-speed energy transition in europe: opportunities and challenges for EU energy security. Energy Strategy Rev. 26, 100415. doi:10.1016/j.esr.2019.100415

Pietrosemoli, L., and Rodríguez-Monroy, C. (2019). The Venezuelan energy crisis: renewable energies in the transition towards sustainability. Renew. Sustain. Energy Rev. 105, 415–426. doi:10.1016/j.rser.2019.02.014

Pinho, P., Maia, R., and Monterroso, A. (2007). The quality of Portuguese environmental impact studies: the case of small hydropower projects. Environ. Impact Assess. Rev. 27, 189–205. doi:10.1016/j.eiar.2006.10.005

Polas, M. R. H., Kabir, A. I., Sohel-Uz-Zaman, A. S. M., Karim, R., and Tabash, M. I. (2022). Blockchain technology as a game changer for green innovation: green entrepreneurship as a roadmap to green economic sustainability in Peru. J. Open Innovation Technol. Mark. Complex. 8, 62. doi:10.3390/joitmc8020062

Popescu, C., Panait, M., Palazzo, M., and Siano, A. (2022). Energy transition in European union—challenges and opportunities. Energy Transition Econ. Soc. Environ. Dimensions , 289–312.

Popkova, E. G., Bogoviz, A. V., Lobova, S. V., Vovchenko, N. G., and Sergi, B. S. (2023). Blockchain, sustainability and clean energy transition. Glob. Transitions 5, 64–78. doi:10.1016/j.glt.2023.04.002

Press, M., and Arnould, E. J. (2009). Constraints on sustainable energy consumption: market system and public policy challenges and opportunities. J. Public Policy & Mark. 28, 102–113. doi:10.1509/jppm.28.1.102

Qazi, A., Hussain, F., Rahim, N. A., Hardaker, G., Alghazzawi, D., Shaban, K., et al. (2019). Towards sustainable energy: A systematic review of renewable energy sources, technologies, and public opinions. IEEE access 7, 63837–63851. doi:10.1109/access.2019.2906402

Quitzow, R., Thielges, S., Goldthau, A., Helgenberger, S., and Mbungu, G. (2019). Advancing a global transition to clean energy–the role of international cooperation. Economics 13, 20190048. doi:10.5018/economics-ejournal.ja.2019-48

Rabaia, M. K. H., Abdelkareem, M. A., Sayed, E. T., Elsaid, K., Chae, K.-J., Wilberforce, T., et al. (2021). Environmental impacts of solar energy systems: A review. Sci. Total Environ. 754, 141989. doi:10.1016/j.scitotenv.2020.141989

Rahman, A., Farrok, O., and Haque, M. M. (2022). Environmental impact of renewable energy source based electrical power plants: solar, wind, hydroelectric, biomass, geothermal, tidal, ocean, and osmotic. Renew. Sustain. Energy Rev. 161, 112279. doi:10.1016/j.rser.2022.112279

Ramzan, M., Raza, S. A., Usman, M., Sharma, G. D., and Iqbal, H. A. (2022). Environmental cost of non-renewable energy and economic progress: do ICT and financial development mitigate some burden? J. Clean. Prod. 333, 130066. doi:10.1016/j.jclepro.2021.130066

Safari, A., Das, N., Langhelle, O., Roy, J., and Assadi, M. (2019). Natural gas: A transition fuel for sustainable energy system transformation? Energy Sci. Eng. 7, 1075–1094. doi:10.1002/ese3.380

Sakellariou, N., and Mulvaney, D. (2013). Engineers and the renewable energy transition: challenges and opportunities. J. Prof. Issues Eng. Educ. Pract. 139, 12–18. doi:10.1061/(asce)ei.1943-5541.0000128

Salam, M. A., and Khan, S. A. (2018). Transition towards sustainable energy production–A review of the progress for solar energy in Saudi Arabia. Energy Explor. Exploitation 36, 3–27. doi:10.1177/0144598717737442

Sánchez-Pantoja, N., Vidal, R., and Pastor, M. C. (2018). Aesthetic impact of solar energy systems. Renew. Sustain. Energy Rev. 98, 227–238. doi:10.1016/j.rser.2018.09.021

Sen, S., and Ganguly, S. (2017). Opportunities, barriers and issues with renewable energy development–A discussion. Renew. Sustain. Energy Rev. 69, 1170–1181. doi:10.1016/j.rser.2016.09.137

Sharma, G. D., Verma, M., Taheri, B., Chopra, R., and Parihar, J. S. (2023). Socio-economic aspects of hydrogen energy: an integrative review. Technol. Forecast. Soc. Change 192, 122574. doi:10.1016/j.techfore.2023.122574

Siampour, L., Vahdatpour, S., Jahangiri, M., Mostafaeipour, A., Goli, A., Shamsabadi, A. A., et al. (2021). Techno-enviro assessment and ranking of Turkey for use of home-scale solar water heaters. Sustain. Energy Technol. Assessments 43, 100948. doi:10.1016/j.seta.2020.100948

Siddik, A. B., Khan, S., Khan, U., Yong, L., and Murshed, M. (2023). The role of renewable energy finance in achieving low-carbon growth: contextual evidence from leading renewable energy-investing countries. Energy 270, 126864. doi:10.1016/j.energy.2023.126864

Stolten, D., and Scherer, V. (2013). Transition to renewable energy systems . John Wiley & Sons .

Streimikiene, D., and Šivickas, G. (2008). The EU sustainable energy policy indicators framework. Environ. Int. 34, 1227–1240. doi:10.1016/j.envint.2008.04.008

Surendra, K., Takara, D., Hashimoto, A. G., and Khanal, S. K. (2014). Biogas as a sustainable energy source for developing countries: opportunities and challenges. Renew. Sustain. Energy Rev. 31, 846–859. doi:10.1016/j.rser.2013.12.015

Svetec, E., Nađ, L., Pašičko, R., and Pavlin, B. (2019). “Blockchain application in renewable energy microgrids: an overview of existing technology towards creating climate-resilient and energy independent communities,” in 2019 16th international conference on the European Energy Market (EEM) , Ljubljana, Slovenia , 18-20 September 2019 ( IEEE ), 1–7.

Sweeney, C., Bessa, R. J., Browell, J., and Pinson, P. (2020). The future of forecasting for renewable energy. Wiley Interdiscip. Rev. Energy Environ. 9, e365. doi:10.1002/wene.365

Takase, M., Aboah, M., and Kipkoech, R. (2022). A review on renewable energy potentials and energy usage statistics in Ghana. Fuel Commun. 11, 100065. doi:10.1016/j.jfueco.2022.100065

Tang, A., Alsultany, F. H., Borisov, V., Mohebihafshejani, A., Goli, A., Mostafaeipour, A., et al. (2022). Technical, environmental and ranking analysis of using solar heating: A case study in South Africa. Sustain. Energy Technol. Assessments 52, 102299. doi:10.1016/j.seta.2022.102299

Tawalbeh, M., Al-Othman, A., Kafiah, F., Abdelsalam, E., Almomani, F., and Alkasrawi, M. (2021). Environmental impacts of solar photovoltaic systems: A critical review of recent progress and future outlook. Sci. Total Environ. 759, 143528. doi:10.1016/j.scitotenv.2020.143528

Tian, J., Yu, L., Xue, R., Zhuang, S., and Shan, Y. (2022). Global low-carbon energy transition in the post-COVID-19 era. Appl. energy 307, 118205. doi:10.1016/j.apenergy.2021.118205

Tirkolaee, E. B., Goli, A., and Mirjalili, S. (2022). Circular economy application in designing sustainable medical waste management systems. Environ. Sci. Pollut. Res. 29, 79667–79668. doi:10.1007/s11356-022-20740-x

Tirkolaee, E. B., Goli, A., and Weber, G.-W. (2020b). Fuzzy mathematical programming and self-adaptive artificial fish swarm algorithm for just-in-time energy-aware flow shop scheduling problem with outsourcing option. IEEE Trans. fuzzy Syst. 28, 2772–2783. doi:10.1109/tfuzz.2020.2998174

Tirkolaee, E. B., Goli, A., Weber, G.-W., and Szwedzka, K. (2020a). A novel formulation for the sustainable periodic waste collection arc-routing problem: A hybrid multi-objective optimization algorithm. Logist. Operations Manag. Recycl. Reuse , 77–98.

Tong, D., Sun, Y., Tang, J., Luo, Z., Lu, J., and Liu, X. (2023). Modeling the interaction of internal and external systems of rural settlements: the case of guangdong, China. Land Use Policy 132, 106830. doi:10.1016/j.landusepol.2023.106830

Truby, J. (2018). Decarbonizing bitcoin: law and policy choices for reducing the energy consumption of blockchain technologies and digital currencies. Energy Res. Soc. Sci. 44, 399–410. doi:10.1016/j.erss.2018.06.009

Usman, M., Jahanger, A., Makhdum, M. S. A., Balsalobre-Lorente, D., and Bashir, A. (2022). How do financial development, energy consumption, natural resources, and globalization affect arctic countries' economic growth and environmental quality? An advanced panel data simulation. Energy 241, 122515. doi:10.1016/j.energy.2021.122515

Van Der Schoor, T., and Scholtens, B. (2015). Power to the people: local community initiatives and the transition to sustainable energy. Renew. Sustain. energy Rev. 43, 666–675. doi:10.1016/j.rser.2014.10.089

Verbruggen, A., Fischedick, M., Moomaw, W., Weir, T., Nadaï, A., Nilsson, L. J., et al. (2010). Renewable energy costs, potentials, barriers: conceptual issues. Energy policy 38, 850–861. doi:10.1016/j.enpol.2009.10.036

Vidadili, N., Suleymanov, E., Bulut, C., and Mahmudlu, C. (2017). Transition to renewable energy and sustainable energy development in Azerbaijan. Renew. Sustain. Energy Rev. 80, 1153–1161. doi:10.1016/j.rser.2017.05.168

Vujanović, M., Wang, Q., Mohsen, M., Duić, N., and Yan, J. (2021). Recent progress in sustainable energy-efficient technologies and environmental impacts on energy systems. Appl. Energy 283, 116280. doi:10.1016/j.apenergy.2020.116280

Wang, X., Chen, G., Afshan, S., Awosusi, A. A., and Abbas, S. (2023b). Transition towards sustainable energy: the role of economic complexity, financial liberalization and natural resources management in China. Resour. Policy 83, 103631. doi:10.1016/j.resourpol.2023.103631

Wang, H., Ghasemi, M., Ghadiri Nejad, M., and Khandan, A. (2023a). Assessing the potential growth of Iran’s hospitals with regard to the sustainable management of medical tourism. Health & Soc. Care Community 2023, 1–12. doi:10.1155/2023/8734482

Wang, Q., Li, R., and Zhan, L. (2021). Blockchain technology in the energy sector: from basic research to real world applications. Comput. Sci. Rev. 39, 100362. doi:10.1016/j.cosrev.2021.100362

Wang, Q., and Su, M. (2020). Integrating blockchain technology into the energy sector—From theory of blockchain to research and application of energy blockchain. Comput. Sci. Rev. 37, 100275. doi:10.1016/j.cosrev.2020.100275

Wang, S., and Wang, S. (2015). Impacts of wind energy on environment: A review. Renew. Sustain. Energy Rev. 49, 437–443. doi:10.1016/j.rser.2015.04.137

Wang, Z., and Razzaq, A. (2022). Natural resources, energy efficiency transition and sustainable development: evidence from BRICS economies. Resour. Policy 79, 103118. doi:10.1016/j.resourpol.2022.103118

Wang, Z., Yen-Ku, K., Li, Z., An, N. B., and Abdul-Samad, Z. (2022). The transition of renewable energy and ecological sustainability through environmental policy stringency: estimations from advance panel estimators. Renew. Energy 188, 70–80. doi:10.1016/j.renene.2022.01.075

Wu, B., Gu, Q., Liu, Z., and Liu, J. (2023). Clustered institutional investors, shared ESG preferences and low-carbon innovation in family firm. Technol. Forecast. Soc. Change 194, 122676. doi:10.1016/j.techfore.2023.122676

Wu, J., and Tran, N. K. (2018). Application of blockchain technology in sustainable energy systems: an overview. Sustainability 10, 3067. doi:10.3390/su10093067

Wu, Y., Wu, Y., Cimen, H., Vasquez, J. C., and Guerrero, J. M. (2022). Towards collective energy community: potential roles of microgrid and blockchain to go beyond P2P energy trading. Appl. Energy 314, 119003. doi:10.1016/j.apenergy.2022.119003

Wünsche, J. F., and Fernqvist, F. (2022). The potential of blockchain technology in the transition towards sustainable food systems. Sustainability 14, 7739. doi:10.3390/su14137739

Xie, X., Jin, X., Wei, G., and Chang, C.-T. (2023). Monitoring and early warning of SMEs’ shutdown risk under the impact of global pandemic shock. Systems 11, 260. doi:10.3390/systems11050260

Xu, X., Lin, Z., Li, X., Shang, C., and Shen, Q. (2022). Multi-objective robust optimisation model for MDVRPLS in refined oil distribution. Int. J. Prod. Res. 60, 6772–6792. doi:10.1080/00207543.2021.1887534

Xu, X., Wang, C., and Zhou, P. (2021). GVRP considered oil-gas recovery in refined oil distribution: from an environmental perspective. Int. J. Prod. Econ. 235, 108078. doi:10.1016/j.ijpe.2021.108078

Yasmeen, R., Tao, R., Jie, W., Padda, I. U. H., and Shah, W. U. H. (2022). The repercussions of business cycles on renewable & non-renewable energy consumption structure: evidence from OECD countries. Renew. Energy 190, 572–583. doi:10.1016/j.renene.2022.03.138

Yavari, R., Zaliwciw, D., Cibin, R., and McPhillips, L. (2022). Minimizing environmental impacts of solar farms: A review of current science on landscape hydrology and guidance on stormwater management. Environ. Res. Infrastructure Sustain. 2, 032002. doi:10.1088/2634-4505/ac76dd

Yi, H., Meng, X., Linghu, Y., and Zhang, Z. (2023). Can financial capability improve entrepreneurial performance? Evidence from rural China. Econ. research-Ekonomska istraživanja 36, 1631–1650. doi:10.1080/1331677x.2022.2091631

Yildizbasi, A. (2021). Blockchain and renewable energy: integration challenges in circular economy era. Renew. Energy 176, 183–197. doi:10.1016/j.renene.2021.05.053

Yin, L., Wang, L., Li, T., Lu, S., Yin, Z., Liu, X., et al. (2023). U-Net-STN: A novel end-to-end lake boundary prediction model. Land 12, 1602. doi:10.3390/land12081602

Yu, B., and Zhou, X. (2023). Urban administrative hierarchy and urban land use efficiency: Evidence from Chinese cities . International Review of Economics & Finance .

Yu, C., Moslehpour, M., Tran, T. K., Trung, L. M., Ou, J. P., and Tien, N. H. (2023). Impact of non-renewable energy and natural resources on economic recovery: empirical evidence from selected developing economies. Resour. Policy 80, 103221. doi:10.1016/j.resourpol.2022.103221

Zakaria, Z., Kamarudin, S. K., Salehmin, M. N. I., Ahmad, N. N. R., Aminuddin, M. A., Hanapi, I. H., et al. (2023). Energy scenario in Malaysia: embarking on the potential use of hydrogen energy. Int. J. Hydrogen Energy . doi:10.1016/j.ijhydene.2023.05.358

Zeleňáková, M., Fijko, R., Diaconu, D. C., and Remeňáková, I. (2018). Environmental impact of small hydro power plant—A case study. Environments 5, 12. doi:10.3390/environments5010012

Zhang, J., Ullah, S., and Khan, K. (2023). The prominence of fossil energy resources in ecological sustainability of BRICS: the key role of institutional worth. Front. Environ. Sci. 10, 1084314. doi:10.3389/fenvs.2022.1084314

Zhou, X., Jia, M., Altuntaş, M., Kirikkaleli, D., and Hussain, M. (2022). Transition to renewable energy and environmental technologies: the role of economic policy uncertainty in top five polluted economies. J. Environ. Manag. 313, 115019. doi:10.1016/j.jenvman.2022.115019

Zhu, J., Lin, N., Zhu, H., and Liu, X. (2023). Role of sharing economy in energy transition and sustainable economic development in China. J. Innovation Knowl. 8, 100314. doi:10.1016/j.jik.2023.100314

Keywords: sustainable energy, blockchain technology, renewable energy technologies, economic benefits, policy changes

Citation: Lv Y (2023) Transitioning to sustainable energy: opportunities, challenges, and the potential of blockchain technology. Front. Energy Res. 11:1258044. doi: 10.3389/fenrg.2023.1258044

Received: 13 July 2023; Accepted: 28 August 2023; Published: 14 September 2023.

Reviewed by:

Copyright © 2023 Lv. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Yongjun Lv, [email protected]

This article is part of the Research Topic

The Role of Blockchain Technology Toward a Sustainable Energy Future

Energy management is the proactive and systematic monitoring, control, and optimization of an organization’s energy consumption to conserve use and decrease energy costs.

Energy management includes minor actions such as monitoring monthly energy bills and upgrading to energy-saving light bulbs. It can mean more extensive improvements like adding insulation, installing a reflective roof covering or improving HVAC (heating and cooling) equipment to optimize energy performance.

Energy management also includes more elaborate activities, such as creating financial projections for commissioning renewable energy services and making other improvements for clean energy consumption and reduced energy costs in coming years.

More sophisticated energy management programs take advantage of technology. For instance, utility tracking software predicts future energy usage and plans energy budgets. Which help a company’s strategic decision makers ensure its energy management strategy correlates with its objectives and financial planning. Enterprise management software uses IoT, advanced connectivity and big data, allowing a corporation to take advantage of energy data analytics for better facility management, and helps with energy consumption and energy management challenges.

With ESG disclosures starting as early as 2025 for some companies, make sure that you're prepared with our guide.

Register for the ebook on GHG emissions accounting

Around the globe, there is a great need to save energy , which impacts prices, emissions targets, and legislation that affects us all. Not only can energy management help reduce the carbon emissions that contribute to global warming, it also helps reduce our dependence on increasingly limited fossil fuels.

According to energystar.gov (link resides outside of IBM), energy use is a US commercial office building’s single largest operating expense. It represents about a third of an enterprise’s typical operating budget and accounts for almost 20% of the nation’s annual greenhouse gas emissions. Energy StarÒ says office buildings waste up to one-third of the energy they consume.

Energy management is even more important in Europe, where the energy supply (link resides outside of IBM) is especially vulnerable to cyberattacks. This is because, on average, EU corporations invest 41% less on information security than American companies. Therefore, European companies need more initiatives that implement energy security solutions and help them safeguard data, access, and networks.

In addition to helping mitigate global problems that result from carbon emissions, energy management programs also bring benefits to corporations.

Having energy management software in place helps control a corporation’s budget and reduce the risk that is associated with energy price increases that can impact a business’s ability to operate. Tracking utility costs and energy efficiency allows corporations to budget more efficiently and gain better insight into overall operational costs. According to Energy Star, decreasing energy use by 10% can lead to a 1.5% increase in net operating income.

Energy monitoring and management not only bring cost savings to a company’s bottom line through decreased usage and consumption but can also mean reduced reliance on sometimes volatile supply chains. Energy management programs can also help companies lower costs through competitive procurement.

Having a strong environmental, social and governance (ESG) foundation helps companies save energy, increase transparency and work toward better sustainability goals.

Energy management solutions that use a single system of record to reduce energy use, cost, time, and the burden of reporting allow clients to manage the impact of environmental risks . While also, identifying efficiency opportunities and assess sustainability risks, thus focusing on ESG strategic outcomes.

Besides saving energy costs and lowering carbon emissions, reducing your company’s carbon footprint also shows the company’s commitment to the environment, which promotes an image of greater sustainability and advocating for green energy. Reducing greenhouse gas emissions leads to having, and being recognized for, greater corporate social responsibility.

A strategic approach to consulting with sustainability experts on your sustainability strategy and roadmap leads to the most effective energy and ESG management . In addition to other benefits, consulting on efforts that can include decarbonization and transition to renewables can also help your business attract new and often younger employees who value the optimization of sustainable energy and renewable energy use and take corporate social responsibility seriously.

Intelligent asset management can create energy efficiency for several industry use cases. Some of these include:

• Buildings:  Managing energy in your offices, factories and other facilities helps save energy and reduce carbon output in various ways.  Intelligent asset management uses technology such as AI, IoT, and analytics to help you inspect and monitor a building’s efficiency, calculate potential impacts to the grid, anticipate failure, and better plan maintenance procedures. Companies that use this technology can increase their productivity and make their facilities more energy-efficient, reducing emissions, mitigating climate risk and extending asset lifecycles. They gain operational insights into clean energy sources, efficient waste management and decarbonization.
• Sustainable supply chains:  Using AI and blockchain, intelligent supply chain automation can help reduce the impact that current supply chain weaknesses are having on your business. A more resilient, sustainable supply chain allows clients to act quickly and confidently and mitigate disruptions. Measuring Scope 3 emissions—indirect emissions that are not caused by a company directly but occurring within its supply chain, from warehousing, transportation and waste operations, among other areas—gives companies a competitive advantage in terms of sustainability. While Scope 3 emissions are out of a company’s direct control, measuring them identifies emission problems in their supply chain and allows them to perhaps affect change. Compared to Scope 1 (direct emissions) and Scope 2 (indirect), Scope 3 emissions generally represent the highest levels of greenhouse gases.
• Manufacturing:  Manufacturing facilities burn numerous fossil fuels and are some of the largest energy consumers. Creating an energy management program to sustainably reduce energy use for manufacturing includes collecting and analyzing energy-efficiency data (from various meters, databases and multiple plant sites) and creating a project management plan. A more IT-based factory floor that uses the Industrial Internet of Things (IIoT) and analytics means better predictive maintenance and quality, which leads to smarter manufacturing. Case studies show that changing energy consumption patterns in manufacturing requires management personnel that are committed to reducing energy use because it requires change, infrastructure investment and possibly retraining.

Energy management also comes with its own set of challenges. Some of these include:

• Not enough data integrity, analysis, or clear benchmarks:  Traditional building management systems and meters that collect data through manual energy audits don’t provide data that lets you see wasteful energy usage patterns. Using an energy management system makes it easier and more convenient to access and use more data about energy consumption. A strong energy management system automatically generates regular, reliable, and customized energy reports.
• Faulty systems, incorrect settings, and poorly maintained equipment:  Scheduled checks that are conducted too infrequently mean wasted time and money. Equipment that breaks down unexpectedly thrusts you into reactive maintenance, which can create challenges and unexpected expenses. In contrast, intelligent energy systems alert you to equipment breakdown and energy wastage immediately. They provide real-time information on energy consumption, and you can set energy KPIs for consistent results. Having a proactive maintenance strategy, with routine and preventive maintenance schedules, means that equipment is serviced regularly and has longer lifespans.
• Failure to plan for energy upgrades:  In-depth energy data lets you make smart decisions about energy retrofits or upgrade initiatives that bring cost savings and a good ROI.

Save energy and decarbonize with intelligent asset management.

Reduce energy and carbon emissions with efficient data centers and more sustainable, secure IT operations.

Accelerate sustainability by managing all your environmental, social, and governance (ESG) indicators in a single platform.

Optimize your real estate and facilities management operations for higher efficiency and sustainability.

Boost your sustainability journey and energy management efficiency by charting a sustainable and profitable path forward with open, AI-powered solutions and platforms plus deep industry expertise from IBM.

Automating application resource management should be your first step in the sustainability journey.

Learn how to minimize energy use and embed responsible computing across your IT environment.

Enterprises that want to reduce their carbon footprint should expand their sustainability goals to include green IT and responsible computing.

Unlock the full potential of your enterprise assets with IBM Maximo Application Suite by unifying maintenance, inspection and reliability systems into one platform. It’s an integrated cloud-based solution that harnesses the power of AI, IoT and advanced analytics to maximize asset performance, extend asset lifecycles, minimize operational costs and reduce downtime.

MPPCS Exam Preparation

MPPSC Prelims and Mains Notes, MPPCS Test Series

Energy Management: Issues and challenges

The Government of India has set up Bureau of Energy Efficiency (BEE) on 1st March 2002 under the provision of the Energy Conservation Act, 2001. The mission of  Bureau of Energy Efficiency is to assist in developing policies and strategies with a thrust on self-regulation and market principles, within the overall framework of the Energy Conservation Act, 2001 along with the primary objective of reducing energy intensity of the Indian economy.

The National Mission for Enhanced Energy Efficiency (NMEEE) is one of the eight national missions under the National Action Plan on Climate Change (NAPCC). NMEEE consist of four initiatives to enhance energy efficiency in energy intensive industries which are as follows:

The primary energy consumption in India is the third biggest after China and USA with 5.5% global share in 2016.The electricity generation target of conventional sources for the year 2017-18 has been fixed as 1229.400 Billion Unit (BU). i.e. growth of around 5.97% over actual conventional generation of 1160.141 BU for the previous year (2016-17). The conventional generation during 2016-17 was 1160.141 BU as compared to 1107.822 BU generated during 2015-16, representing a growth of about 4.72 %.

India has become power surplus from chronic power shortage. Record capacity additionof around one-fifth of current conventional power capacity and solar power capacity addition of 157% in the last two years led to a boost in power generation. The highest-ever increase in transmission lines and sub-stations improved the transmission scenario resulting in energy deficit falling to lowest ever of 2.1% in 2015-16.

Energy Crisis can be described as a situation in which a country suffers from frequent disruptions in energy supplies because of large and increasing gaps between availability and demand of electricity accompanied by rapidly increasing energy prices that threaten economic and social development of the nation.

• Our over-dependence on limited and exhaustible sources of energy such as our coal and oil deposits.
• Increasing gap in the demand and supply of the energy.
• Ever increasing prices of the energy and fuel from other countries.
• Reluctance in using alternative and renewable sources of energy, such as solar,wind, bio-energy, etc..
• Overuse and misuse of the available sources of energy.
• MPPCS  Mains 2024 Tests and Notes Program
• MPPCS  Prelims Exam 2024- Test Series and Notes Program
• MPPCS  Prelims and Mains 2024 Tests Series and Notes Program
• MPPCS  Detailed Complete Prelims Notes

Online Trade Magazine Alternative Energy from Solar, Wind, Biomass, Fuel Cells and more…

We conclude that a National Energy Experts Committee (NEEC) should be established under ministry of energy consisting of Energy experts from Academia, Industry, Business, Associations/NGOs and International Experts.

Energy Management Issues, Challenges and Way Forward in Pakistan

Dr. Faiz M Bhutta | Techfa Consulting

Energy is the most important resource for progress and growth of the country. Pakistan is energy deficient country trying its best to come out of energy crisis. The energy issues facing the country are as under,

• Lack of integrated energy planning and demand forecasting seriously worsening gap between energy supply and demand
• Circular debt, amount of cash shortfall within Central Power Purchasing Agency (CPPA) that it is unable to pay to power supply companies
• Imbalanced energy Mix with heavy dependence on oil and gas ( 72% imported)
• Non-utilization of vast indigenous resources of thar coal, solar , wind and hydro potential
• Heavy Transmission /distribution losses and theft
• In adequate revenue collection by DISCOs.

In Pakistan there are many institutions, departments, Councils, Authorities and donors working to resolve the above issues.

WAPDA (Water and Power Development Authority) is working on adding power through deployment of Hydro power plants in Pakistan. The name of Ministry of Water and Power has been changed to Ministry of Energy and Ministry of Energy is working toward adding thermal power plants. NEECA and provincial Energy Efficiency and conservation departments like PEECA are trying to create awareness and promote energy efficiency and conservation in Pakistan. NEPRA (National Electric Power regulation Authority) is trying its best to develop grid codes, standards and power regulations in Pakistan. AEDB (Alternate Energy development Board) is trying to develop alternate and renewable energy policies and regulations to enhance alternate and renewable energy in energy mix in the country. NTDC (National Transmission and Dispatch Company) is working toward enhancing transmission and distribution network in the country for power evacuation from non-renewable and Renewable Energy Power Generation plants in the country. PPIB (Private Power and Infrastructure Board) is trying best to increase facilitation to private investors in Power and provide one window operation.

Industry Associations like REAP, PSA and SQF are trying to promote EE and RE in Pakistan and putting pressure on Government to provide incentives, investor friendly policies and tax exemptions, RE standards development and effective implementation of regulations. International donor agencies like GIZ Pakistan, KFW, World Bank, IFC, European Union and UNIDO  are making efforts for provision of funds and technical assistance for enhancing EE and RE share in energy mix of the country. CPEC (China Pakistan Economic Corridor) is US $50 Billion project having more than 60% energy related projects. Recently UNIDO has conducted six EE and RE trainings under their program of Sustainable Energy Imitative for Industries in Pakistan on various segments of energy management targeted toward academia, vendors, policy makers, Top management and Bankers and all trainings have been delivered by world reputed consultants and trainers. These trainings provided awareness to vendors, consultants, Academia and Policy makers on EE and RE so that best energy initiatives should be launched and best energy policies should be developed.

USAID has set up two US Pakistan Center for Advance Studies in Energy at NUST, UET Peshawar where research labs are being established, Research is being undertaken in solar, biomass/Bio-fuel, wind and Hydro sectors, Energy Graduates and PhDs are being produced through these centers and first batch of Energy Graduates have been produced by NUST in April 2018 and these energy graduates can play role in setting up energy policies and regulations in Pakistan.

German NGO “ The Heinrich Boll Stiftung” has recently conducted Energy dialogue on Governance in RE in all provinces and the report reflected the major issue as lack of coordination of provinces with the Federal Government in view of 18Th amendment in constitution, lack of awareness in RE sector and lack of standards for supply and installation of RE systems.

Energy Management is not picking up much because of lack of regulations, laws, financing and incentives and there is no law binding the Industrial, commercial and business sector to implement energy management systems or get ISO 50001certifications. Industries, commercial and domestic sectors are taking no cost and low cost measures like replacement of conventional lights with energy efficiency lighting, preventing leakages and wastes, undertaking preventive and corrective maintenance plans, undertaking calibrations and doing retrofits but there is a need to adopt Energy management Systems ( EnMS) taking into accounts continuous improvements in organization, technology and people.

Despite the above tremendous efforts, still energy issues exist and there is still load shedding of four to six hours in cities and eight to 12 hours in rural areas. There is a need to think that why the energy crisis still exists and where is a gap and what strategies and policies should be developed to overcome energy crisis and making Pakistan a Green and Energy Efficient Pakistan?

Energy and Power policies in practice lacks timely review of policies including review of targets, review strategies, review of implementation, review of funding sources, review of regulations etc. All policies including Renewable Energy policy 2006, Power Policy 2015 etc and there is need of integrated energy policy and Ministry of Energy should take up this matter and establish National Energy Experts Committee (NEEC) and this committee should design and develop one integrated energy policy keeping in view of Energy Situation, Energy sources, Energy regulations, Energy targets, Energy incentives, Energy Financing and most importantly implementation of existing laws and regulations. Pakistan Engineering Council (PEC) have developed Building Code of Pakistan 2011, PETSAC (Pakistan Electric and Telecommunication safety code of Pakistan 2014 and Fire code of Pakistan 2015. NEECA has issued national Energy Efficiency and Conservation Act 2016. Implementation of all these policies and regulations is weak and there is a need to set up Implementation Unit at federal and provincial levels to ensure implementation of policies, laws and acts or each department, ministry and authority should set up its implementation units for effective implementation of existing policies, regulations and acts.

What improvements are required in energy sector? Some of the improvements needing action are as under,

• Promote domestic alternate sources of energy including Hydro, solar, wind, local coal, agriculture, biomass/biodiesel etc
• Energy conservation and demand management programs
• Coping with circular debt and better management of power sector financial flows
• Existing power plants to be overhauled to achieve maximum efficiency.
• Undertake policies and programs to improve governance, performance of energy sector entities
• Decrease cost and increase cash flows.
• Ensure operational/financial integrity of the sector
• Implement international best practices undertaking smart metering/automated meter reading (AMR) systems and time of use (TOU) tariff
• Resolve tariff and subsidy disputes between provincial governments and CPPA/DISCOs.
• Penalties for electric thefts
• Fuel allocation policies be introduced
• Reallocation of imported furnace oil with gas into power production

We conclude that a National Energy Experts Committee (NEEC) should be established under ministry of energy consisting of Energy experts from Academia, Industry, Business, Associations/NGOs and International Experts. The committee should be given TOR for review of existing energy situation and development of Integrated Energy Policy and Implementation plan addressing the issues and improvements highlighted in this article.

BRIEF INTRODUCTION OF ENGR. FAIZ MUHAMMAD BHUTTA

He has contributed as member PEC Task force on Building Energy Code 2011, Pakistan Electric and Telecommunication Safety Code 2014 and Fire Code of Pakistan 2016.

He was First Chairman of Pakistan Solar Association (PSA) and First Chapter Chairman of REAP.  He is a master trainer at his own Pakistan Renewable Academy (PRENAC). He has so far trained more than 2000 persons in Pakistan on Solar Energy and desinged various solar system ( off-grid, Hybrid and On-Grid) varying from 1 KW to 2 MW.

He is member of International Solar Society Germany (ISES), Life member of PEC, Life Member of IEP, Life member of IEEEP, Member of HVACR Society, Member of PGBC, Member of MAP and member of ASHRAE USA.

He is a master trainer from GIZ Pakistan on solar. He got TOT training on Solar from RENAC Berlin. He got TOT on energy Management from UNIDO Pakistan. He is also a resoruce person on CPD program on Solar from Pakistan Engineering Council. He also got 20 days training from China –HNAC on Planning, construction and Management of Small and Medium Hydro Power Plants.

He is a writer & Trainer on Renwable Energy and his articles are published in Daily DAWN, Altenergy Mag USA, EIR, HVACR Journal, TechnoBiz, Energy Update, Engineering Horizon and Business Mag etc.

He has attended lot of national and international EXPO and Conferences on Alternate Energy in China and Germany. He can be approacehd at [email protected]

The content & opinions in this article are the author’s and do not necessarily represent the views of AltEnergyMag

Comments (0).

This post does not have any comments. Be the first to leave a comment below.

Post A Comment

You must be logged in before you can post a comment. Login now .

Featured Product

Introducing RockIt Smart Slide

More alternative energy resources.

© 2010 - 2024 AltEnergyMag - All Rights Reserved Powered by BTH Management

• Open access
• Published: 13 March 2023

A comprehensive overview on demand side energy management towards smart grids: challenges, solutions, and future direction

• Mutiu Shola Bakare 1 ,
• Abubakar Abdulkarim 2 ,
• Mohammad Zeeshan 1 &
• Aliyu Nuhu Shuaibu 1  

Energy Informatics volume  6 , Article number:  4 ( 2023 ) Cite this article

15k Accesses

18 Citations

6 Altmetric

Metrics details

Demand-side management, a new development in smart grid technology, has enabled communication between energy suppliers and consumers. Demand side energy management (DSM) reduces the cost of energy acquisition and the associated penalties by continuously monitoring energy use and managing appliance schedules. Demand response (DR), distributed energy resources (DER), and energy efficiency (EE) are three categories of DSM activities that are growing in popularity as a result of technological advancements in smart grids. During the last century, the energy demand has grown significantly in tandem with the increase in the global population. This is related to the expansion of business, industry, agriculture, and the increasing use of electric vehicles. Because of the sharp increase in global energy consumption, it is currently extremely difficult to manage problems such as the characterization of home appliances, integration of intermittent renewable energy sources, load categorization, various constraints, dynamic pricing, and consumer categorization. To address these issues, it is critical to examine demand-side management (DSM), which has the potential to be a practical solution in all energy demand sectors, including residential, commercial, industrial, and agricultural. This paper has provided a detailed analysis of the different challenges associated with DSM, including technical, economic, and regulatory challenges, and has proposed a range of potential solutions to overcome these challenges. The PRISMA reviewing methodology is adopted based on relevant literature to focus on the issues identified as barriers to improving DSM functioning. The optimization techniques used in the literature to address the problem of energy management were discussed, and the hybrid techniques have shown a better performance due to their faster convergence speed. Gaps in future research and prospective paths have been briefly discussed to provide a comprehensive understanding of the current DSM implementation and the potential benefits it can offer for an energy management system. This comprehensive review of DSM will assist all researchers in this field in improving energy management strategies and reducing the effects of system uncertainties, variances, and restrictions.

Introduction

The mechanism that allows electricity to be transmitted from power plants to energy customers is known as the “power grid”. This electricity goes from the power plant through the substations in one direction before it reaches the energy user when the voltage is changed via the transmission and distribution line (Piette et al. 2004 ).

The need for energy has expanded significantly along with the increase in the global population during the last century. The International Energy Agency predicted that by 2030, global electricity consumption will have increased by more than 50% (Freeman 2005 ). This is related to the growth of business, industry, agriculture, and the increasing use of electric vehicles (Martínez-Lao et al. 2017 ).

Due to the sharp increase in global energy consumption, it is currently extremely challenging to manage problems such as controlling power loss, dependability, efficiency, and security challenges. A “smart grid,” which combines self-monitoring, self-healing, pervasive control, adaptive, and islanding mode mechanisms, has been suggested to allow for energy transit from the point of production to the site of consumption to solve these problems (Fang et al. 2011 ; Xu et al. 2016b ).

The hardware and software components of smart grids provide the utilities the capacity to immediately identify and address any problems that could develop between the customers and the producing plants and endanger the consistency and quality of the power supply. The smart grid component is classified as shown in Table 1 .

Electrical energy management is used to reduce energy expenses and alter the load profile on both the supply and demand sides. The goal of supply side management (SSM) is to make energy generation, transmission, and distribution more operationally effective. SSM has many advantages, such as maximizing customer value by ensuring efficient energy production at the lowest practical cost, satisfying demand for electricity without the need for new infrastructure, and limiting environmental impact. However, supply-side management is affected by fuel price volatility because of its techniques for managing thermal generators (Haffaf et al. 2021 ).

Demand side energy management (DSM) reduces the cost of energy acquisition and the associated penalties by continuously monitoring energy use and managing appliance schedules (Dranka and Ferreira 2019 ). In order to lower peak loads, control time of use (TOU) levels of power demand, evaluate user profiles for electricity loads, lower carbon emissions, and provide consumers a choice of preferred energy source, the electrical industry originally developed the DSM in 1970 (Gellings 2017 ; Maharjan et al. 2014 ).

Several nations, including the UK (Warren 2014 ), China (Ming et al. 2013 ), North America (Wang et al. 2015 ), and Turkey (Alasseri et al. 2017 ), have adopted the Energy Management System (EMS), which is the most effective way to save energy costs while preserving system stability. However, there are still several constraints that prevent EMS from being fully implemented in underdeveloped nations. These components might be related to:

Adopting an EMS comes at a significant expense, and the long-term rate of return on investment is low.

Time-varying electricity tariffs are ideal. Making the switch from an older model to a newer one is tough for electrical companies and merchants.

Not all stakeholders benefit equally from the transformation;

Population knowledge has a significant impact on implementation speed.

Upgrading the network infrastructure could be very expensive for the system, and bidirectional power flow is still in the research stage, which could delay the idea of EMS.

Cappers et al. examined the prospective benefits of DSM to the electrical power system as illustrated in Fig.  1 . These enhancements have the potential to provide considerable secondary advantages, such as decreased losses and premature aging (Cappers et al. 2010 ).

Benefit achieved by the DSM program (Cappers et al. 2010 )

To effectively reduce costs without the involvement of operators, a control system that selects the energy sources to power different loads according to the period of the energy demand is required. The most frequently used controllers in the literature to accomplish the aforementioned goal are programmable logic controllers (PLC), supervisory control and data acquisition (SCADA), building management systems (BMS), energy management systems (EMS), and automation systems (home automation systems, etc.) (Jabir et al. 2018 ).

Numerous studies have focused on the load control strategies used by DSM (Jabir et al. 2018 ), the roles played by DSM in the electricity market (Morgan and Talukdar 1979 ), the economic benefits of DS (Conchado and Linares 2012 ), the impacts of DSM on the commercial and residential sectors (Esther and Kumar 2016 ; Shoreh et al. 2016 ), the interactions between DSM and other smart grid technologies (Khan et al. 2015b ), the business strategies used by DSM (Behrangrad 2015 ), the impacts of DSM on the reliability of the power system (Kirby 2006 ), the optimization strategies used by DSM (Hussain et al. 2015 ; Vardakas et al. 2014 ), and the load control strategies (Khan et al. 2016 ).

The electrical market has just entered a phase of transformation where one of the primary objectives is to lower peak demand while making the greatest use of all resources available. Over the world, incentives have been created to motivate consumers by offering them a range of monetary benefits and different power rates at different load-dependent intervals. Dynamic pricing is an inherent aspect of the home energy scheduling problem in this situation since it encourages consumers to move their load from the on-peak to the off-peak period. Marginal cost, load pattern, social considerations, and the power utility’s capacity are the main variables utilized to define the energy tariff structure (Phuangpornpitak and Tia 2013 ).

All consumers must benefit from greater DSM effectiveness, which requires detailed consumer consumption data. With the advent of advanced metering infrastructure (AMI), utilities may collect all consumer consumption data, and various DSM programs may be developed depending on the data attributes. The scale, complexity, and unpredictability of smart meter data are addressed for use in load forecasting and DSM systems. When implementing DSM, it is important to consider some important factors, including the load profile of an appliance, the integration of renewable energy, load categorization, constraints, dynamic pricing, consumer categorization, optimization techniques, consumer behaviors, problems with electricity data, enough knowledge, a solid framework, and smart grid technology with its intelligent applications (Khan and Jayaweera 2019 ).

As the load profile of appliances heavily depends on the stochastic behavioral patterns of consumers and the surrounding environment, developing a universal DSM optimization method that works for all types of consumers is quite challenging. It is also difficult to develop a generic forecasting system that can accurately predict the power consumption of various appliances for different users. Thus, the load profile of the consumers’ appliances plays a crucial role in the development of a consumer-specific optimization algorithm that takes into consideration their preferences for comfort (Sharda et al. 2021 ). Different appliances have different characteristics, power requirements, and operating styles. For DSM optimization, the right grouping of home appliances based on consumer preferences or behavior is essential. Survey techniques, bottom-up models, top-down models, and hybrid methods have all been explored to do accurate appliance forecasting. Nonetheless, it is believed that utilizing smart appliances and meters is the best option (Proedrou 2021 ).

The effectiveness of demand scheduling optimization depends critically on customer classification. Customers should be made active DR participants by ensuring their comfort which is done by arranging various appliances within their own time and temperature ranges. likewise, customers may be grouped according to their behavior and demand (Liu et al. 2015 ). It is necessary to overcome consumers’ resistance to adopting and taking part in DSM programs, and this may be done by creating consumer awareness initiatives that will urge customers to use the DSM system. Increased expenses for installing and maintaining control devices must also be taken into account. It is necessary to address the impact of the accelerated development of storage systems brought on by the availability of cheap local storage. The majority of the increasing energy consumption is caused by thermostatically regulated equipment. Hence, there is a lot of room for energy savings via effective management of these devices. The following suggestions, which were emphasized in Ming et al. ( 2015 ) may truly aid in overcoming the difficulties associated with DSM.

The planning for the power sector and regional economic growth should all use DSM as a resource. To be properly implemented, rules, laws, and regulations need to be created by the governments and electricity grid businesses.

It is important to gradually establish the DSM’s assessment and monitoring methods. It might be put into practice by constructing a post evaluation system for DSM, an expert committee and oversight mechanisms for DSM, an energy efficiency evaluation system for performing energy inspections, and an analysis of the energy efficiency criteria for electrical equipment. It is also necessary to promote the creation and improvement of relevant supporting policies for DSM.

To fulfill the expanding energy demand and reduce the rising CO 2 emissions, energy generation from renewable energy sources has become more crucial. Several DSM methodologies are utilized to govern distributed energy resources, renewable energy resources, and storage devices to ensure the overall system operates as effectively as feasible. It is difficult to plan for optimal energy requirements since renewable energy sources and power costs are unpredictable. Each operating location must be thoroughly analyzed to pinpoint the areas where natural capital provides notable advantages for certain types of renewable energy consumption. Several optimization techniques, such as mixed-integer linear programming (MILP) (Erdinc et al. 2014 ), two-stage robust optimization (Liu and Hsu 2018 ), and heuristic optimization, have been proposed to enhance the scheduling of distributed energy sources (Luo et al. 2018 ). The ability of the electric vehicle to function as a battery energy storage system has also been researched for applications like vehicle-to-home (V2H) and vehicle-to-grid (V2G) (Erdinc et al. 2014 ).

An effective management system for scheduling various smart appliances and integrating renewable energy (RES) like solar, wind, distributed micro-generators, and energy storage devices, including plug-in electric automobiles and batteries, may be offered to DSM to provide an optimal management system (Qureshi et al. 2021 ; Wang et al. 2019 ; Wu et al. 2019 ). Electricity prices have a big impact on how much energy people use (Rahman and Miah 2017 ; Zhang and Peng 2017 ). But both the analysis and reshaping of the load profiles as well as the load market’s load patterns in SG may be handled by the DSM. This method lowers energy prices, carbon emissions, and grid running costs by lowering customer peak load demands. It also increases the system’s sustainability, security, and stability (Awais et al. 2015 ).

Numerous studies have been written about the DSM of SG, with the majority of them concentrating on distributed generation with renewable energy integration, optimal load scheduling of demand response (DR), and innovative enabling technologies and systems (Kakran and Chanana 2018 ; Lu et al. 2018 ). This paper reviews and examines carefully the DSM methods as well as the effects of distributed renewable energy generation and storage systems on SG. These strategies, seek to lessen peak load demands and uphold a highly developed synchronization between network operators and customers. This paper major contributions is shown below:

Challenges related to the full implementation of DSM in SG and their accompanying solution.

DSM policy, techniques, and their applications to lessen peak demands and price of electricity.

Recent trends of optimization techniques in the DSM.

The paper’s remaining section is shown as follows: The methodology used for this systematic DSM process and the existing work from the literature are also covered in depth in section “ Methodology ”. In section “ The demand side energy management policies ”, the DSM policy and related work done on these policies are examined. Section “ Demand side management techniques ” reviewed the DSM techniques extensively. The challenges related to the full implementation of DSM in SG are carefully examined in section “ Challenges of DSM ”. The future study is highlighted in chapter “ Future work ” with the concluding part shown in chapter “ Conclusion ”.

Methodology

PRISMA stands for Preferred Reporting Items for Systematic Reviews and Meta-Analyses. It is an evidence-based minimum set of guidelines meant to help scientific writers publish different kinds of systematic reviews and meta-analyses. PRISMA focuses on the methods through which authors may ensure accurate and comprehensive reporting of this type of research (Cortese et al. 2022 ). The PRISMA standard superseded the previous QUOROM standard by demonstrating the high review’s quality, allowing review process replication, and allowing readers to assess the review’s benefits and drawbacks. It offers the replication of a systematic literature review that will completely examine all papers published on the issue to identify the answers to a clearly defined research question. To do this, it will choose the reports to be included in the review using a range of inclusion and exclusion criteria, and it will then summarize the findings (Sarkis-Onofre et al. 2021 ).

Any research project’s main emphasis is centered on three crucial elements: the purpose, the research technique, and the output with potential future application. The planning, executing stage, and reporting are the three stages of the evaluation stage that are used. What are potential solutions to the problems encountered when implementing DSM in the smart grid? was one of the research questions that were developed in the initial step of planning the literature study. Which optimization method has recently become popular in DSM? How do DSM’s policies and methods affect peak demands and power costs in their use? The goal of the present research is to address these issues using the examined literature.

The second step of a systematic review, known as the “executing stage,” comprises the inclusive and exclusive criteria. Inclusive criteria give a full and in-depth assessment of current research papers, and an academic database is employed for this study, which comprises IEEE Explore, MDPI, ACM Digital Library, Springer, Science Direct, Google Scholar, and Taylor and Francis. These databases include reputable, excellent peer-reviewed materials including journal articles, conference papers, and review articles. To incorporate relevant terms in a single search, boolean operators are utilized. For instance, keywords and synonyms are combined using Boolean operators like “AND” and “OR.”. Hence, any article matching the keywords “Demand Side” Management,” “Demand Response,” “Load categorization,” “Optimization methods,” “Customer classification,” and “Distributed Energy Sources integration.” will show up in the search results. An organized approach based on PRISMA is used to cover the published material from the last 10 years. Which provides a guideline with features in the form of a checklist to improve openness and clarity in reviews (Page and Moher 2017 ) as shown in Fig.  2 . Based on keyword searches of published articles during the last 10 years, we found 95,736 review papers in the chosen database that were all authored in English.

Overview of an articles search strategy

The Selection procedure was carried out based on the paper’s title, abstract, and English-written content. The publication should be published in an English journal or conference paper, feature a prominent DSM name, and make a significant contribution to the DSM’s practical application. Articles are not excluded based on their citation records, as is the case with traditional reviewing processes, and publications found in a general database like Google Scholar were tracked down to the relevant publishing journal and counted there rather than under Google Scholar to avoid duplicate entries. Parents or unpublished manuscripts are also excluded.

The final collection of papers is summarized, stored in Microsoft Word and Excel files, and then utilized in the R-Classify online tools, which help readers find the manuscript’s most important idea. In this last phase, the results are described together with any possible limits and prospective future study areas. The findings of earlier research on energy management systems are summarized in Table 2 . The total number of works considered and cited in the final analysis is 255. Of the 255 articles, 24 are peer-reviewed papers while the others are technical papers. The following details were obtained from each article included in this study: The DSM, demand response techniques, implementation challenges, customer-driven adoption, methodology, approaches, and upcoming optimization work. Table 3 indicated the relationship between the existing and current studies.

Table 3 shows that most review works focused on DSM policy, DSM techniques, and optimization techniques, with little or no consideration for the remaining work. As a result, this paper thoroughly analyzes optimization techniques while also providing future directions to bridge these existing gaps.

Demand side management (DSM) is the concept of allowing users to monitor their energy consumption while taking peak energy demand into account. This continuous monitoring and management of energy consumption aim to improve system reliability while lowering energy costs. Many studies have been conducted on demand side energy management due to its enormous complexity (Li et al. 2018 ). The following is a discussion of the principles, techniques, issues, optimization techniques, and future developments used in literature.

The demand side energy management policies

Energy Efficiency (EE), Demand Response (DR), and Distributed Energy Resources (DER) are three categories into which the strategies used to manage energy on the demand side are divided (Sharifi et al. 2017 ; Wu and Xia 2017 ).

Energy efficiency

Energy efficiency provides energy consumers with a comparable and superior service to lower the quantity of energy needed in an economically effective manner since these methods eliminate excessive power loss in the power network (Bukoski et al. 2016 ). Among the energy-efficient tactics are shown by (Jabir et al. 2018 ).

Using energy-efficient equipment and buildings, as well as promoting consumers’ energy-conscious behavior, to reduce energy usage. Typical instances are switching to energy-saving lights from incandescent bulbs and switching to variable-speed air conditioning from standard air conditioning.

Enhancing and performing routine maintenance on electrical equipment by recovering heat from waste, improving maintenance techniques, using contemporary equipment with optimum designs, and implementing cogeneration.

Increasing the efficiency of power transmission and distribution networks by utilizing distributed generation, advanced control systems for voltage regulation, three-phase balancing, power factor correction, data acquisition and analysis in supervisory control and data acquisition systems, and modern technologies such as low-loss transformers, gas installation substations, smart meters, fiber-optics for data acquisition, and high transmission voltages.

• Demand response

Customers’ energy expenses are reduced through demand response, an optional alteration to the load pattern in response to a change in the electricity tariff (Aghaei and Alizadeh 2013 ). However, it may create inconvenience during appliance waiting periods. Price-based and incentive-based DR policies are the two categories. The split and subdivision of the incentive-based DR are shown in Fig.  3 . The emergency demand response (EDR) program, which pays users for voluntarily decreasing power during crises, and the direct load control (DLC) program, which enables the utility to remotely regulate customers’ appliances to fulfill demand, are both components of the voluntary program. It should be emphasized that under the voluntary initiative, consumers who decide not to participate in energy adjustment will not suffer sanctions (Chen et al. 2014 ; Imani et al. 2018 ).

Incentive based Demand Response (Aalami et al. 2019 )

Energy consumers who violate utility company rules under the mandatory program, which consists of the Interruptible Curtailable Service (ICS) and the Capacity Market Program (CMP), are fined (ICS). Another scenario is where the utilities set a predetermined load reduction that the capacity market participants must strictly adhere to maintain a balance between supply, demand, and system dependability. Interruptible/curtailabe uses the emergency response paradigm to stabilize the system, but this paradigm is different from the latter in that users are still required to participate despite the inconvenience involved (Aalami et al. 2010 ; Conteh et al. 2019 ).

The last component of the incentive basis for DR is the market clearing scheme, in which users that participate are compensated with load reduction profits. When attempting to balance energy output and consumption in a market clearing program, procedures like demand bidding/buyback (DBB) and auxiliary service market service (ASM) programs are utilized (Aalami and Khatibzadeh 2016 ). Large energy users, such as industrial and commercial customers, favored this strategy because it gave them a way to bargain for the cost of energy for the load they would be prepared to reduce during a system outage. A negotiated quantity of load reduction with the related rates serves as the electric grid’s reserve energy in an ancillary service market program (Elma and Selamoğullari 2017 ; Yan et al. 2018 ).

Price-based DR is used to persuade energy users to participate in different electricity pricing signals with the aim of lowering energy usage. The primary goals of these regulations are to reduce energy prices and shift demand away from peak times. Several signs related to power price are shown in Fig.  4 .

Price based Demand Response (Shewale et al. 2020 )

The cost of producing energy at a certain time of day depending on consumer demand is reflected in the time of use (TOU). The price signal of TOU, which is broken down into on-peak, mid-peak, and off-peak times, is determined by demand and cost. It has the excellent benefit of being simple for customers to follow, comprehend, and arrange for their schedule demands. Countries including China (Zeng et al. 2008 ), Ontario (Adepetu et al. 2013 ), Italy (Torriti 2012 ), USA (Faruqui and Sergici 2010 ) and Malaysia (Hussin et al. 2014 ) have implemented TOU after it was recommended in (Moon and Lee 2016 ; Vivekananthan et al. 2014 ) to minimize costs and energy consumption patterns in residential structures.

Critical peak pricing (CPP) is a price control signal that uses higher power charges to restrict energy usage at a peak time. It offers two time frames (the peak and off-peak). Customers were advised that CPP is granted on days that are predicted to have higher energy use in advance of this period. Since the system is not constantly subject to this constraint, CPP is not a daily DP, but it is also ineffectual at reducing energy costs and carbon emissions. Customers of energy have been urged to participate in DR via CPP, and significant energy and cost reductions have been noted (Kim et al. 2015 ; Yang et al. 2016 ). Most especially in countries like North America (Faruqui and Sergici 2010 ) and Sweden (Renner et al. 2011 ).

The real-time pricing (RTP) scheme is subject to frequent changes due to the utility price signal, which is made available to consumers an hour or day in advance. It is difficult for the consumers to actively participate in it due to its high level of intricacy and the fact that there are two lines of communication between the parties. This pricing strategy is recommended by (Yoon et al. 2014a , b ) as a way to increase system stability at a reduced cost and with favorable environmental impacts in a country like the USA (Yoon et al. 2014a , b ).

When Inclined Block Rate (IBR) is paired with RTP or TOU, both price signals may be utilized. Customers’ energy use and electricity prices are connected, thus if energy consumption falls below a certain point, so will the price. The RTP and TOU pricing scheme works well in terms of energy cost and stability when the IBR is utilized to boost its efficacy (Zhao et al. 2013 ).

A fixed price is a form of pricing indication that is consistent throughout the day or season and is not negotiable. Fixed power pricing in a nation like Nigeria makes it almost difficult to actively engage in any suggested fixed tariff to reduce the cost of energy (Faria et al. 2013 ; Pan et al. 2014 ).

Distributed renewable energy

An integrated decentralized power generating system that is connected to the electrical grid is known as a distributed energy resource (DER). With the increasing integration of DER into the grid, a variety of benefits and opportunities, including affordability, reliability, efficiency, power quality, and energy independence for the power system and its stakeholders emerge. The classification of DER into Distribution Generation (DG) and Electric Energy Storage is shown in Fig.  5 . The DER is powered by convection and renewable energy sources (RES). Conventional energy sources including diesel, gas, microturbines, and combustion turbines still make up the bulk of the energy market despite their limited availability. These sources, nevertheless, are constrained by high production costs, transmission loss, anthropogenic climate change, the greenhouse effect, and acid rain (Bongomin and Nziu 2022 ).

Classification of distributed energy resources (Oskouei et al. 2022 )

Despite being stochastic in nature, intermittent, unexpected, and uncontrolled, renewable energy sources (RES) including solar, biomass, wind, solar thermal, geothermal, and small hydro turbines have grown to be a popular source of energy (Platt et al. 2014 ). According to their storage concept, electrical energy may be transformed into mechanical, electrochemical, electromagnetic, thermodynamic, and chemical energy. The present energy storage methods, prices, guiding principles, benefits, and kinds of ESS applications can be found in Oskouei et al. ( 2022 ).

Demand side management techniques

As illustrated in Fig.  6 , Demand Side Management (DSM) techniques for load shaping include peak clipping, valley filling, load shifting, strategy conservation, strategic load growth, and variable load shape (Macedo et al. 2015 ).

Peak clipping is a concept used in poor countries to decrease the effect of peak demand during peak hours when the installation costs of additional power units are prohibitive. This strategy simultaneously reduced demand and the peak time by directly reducing user appliance loads (Al-enezi 2010 ).

Load shifting involves changing the demand for loads from peak hours to off-peak hours by applying filling and clipping strategies. The TOU and storage devices are used in this method with a constant level of total energy consumption (Chokpanyasuwan et al. 2015 ).

To preserve system balance, valley filling requires a structure during off-peak times, especially when the average cost is lower than the load cost. This often occurs when a plant’s energy production is not fully used and its running expenses are minimal. Even if the peak demand is unaltered, this leads to an increase in total energy usage. By using thermal storage to apply this technology, system efficiency is greatly raised at a reduced energy cost.

Strategic conservation reduces energy loss and consumption efficiency of seasonal energy consumption through technological change incentives. This technique is quite comprehensive and less considered as a technique in load management because it involves a reduction in sales that is not necessarily accompanied by peak reduction.

Strategic load growth increases peak demand in a particular season by managing the seasonal energy usage and a drastic rise in both effect of the energy usage and peak demand is recorded. However, the utilities make use of a more intelligent system to meet their target, especially in the electrification of industrial and commercial heating processes.

Flexible load shape uses load limiting devices to reduce energy consumption at the user’s end without affecting the actual system conditions, the utility interrupts the loads when necessary to reduce the peak demand and change the total energy consumption.

Demand side management techniques (Macedo et al. 2015 )

This paper reports some of the work on demand side energy management strategies and takes into account the three main categories of energy consumers, namely residential (R), commercial (C), and industrial (I) energy users. As indicated in Table 4 , certain authors in some of the examined works took into account all (A) energy users at once.

Challenges of DSM

Planning and managing decision parameters and operating constraints are necessary for the implementation of DSM and depend on several important factors, including the load profile of an appliance, the integration of renewable energy, load categorization, constraints, dynamic pricing, consumer categorization, optimization techniques, consumer behaviors, issues with electricity data, adequate knowledge, a reliable framework, technology-smart, and grid-intelligent appliances, appropriate control strategies, and these challenges encountered during the DSM’s deployment are briefly mentioned below:

Load profile of appliance

Smart appliances are an essential part of creating an accurate and efficient load management system since they come with built-in communication sensors that can link with the smart meter to analyze their energy usage. This is accomplished by collecting ambient data and operating in accordance with the power and tariff parameters provided to them. To create a more precise and trustworthy system, the energy profiles of smart appliances must be taken into consideration during the deployment phase. A normal survey load profile may take the role of smart meters, although it is less accurate. If you are aware of every piece of equipment your clients use, setting up a DR program is easy. To assess load profile management, a survey of various energy consumers is conducted, with an emphasis on quality of service (QoS) (Pilloni et al. 2016 ). Similar in approach, the authors (Vivekananthan et al. 2014 ) urge users to discuss their preferences for using controlled appliances and place greater emphasis on scheduling appliances according to time and preferences. According to a study published in (Yilmaz et al. 2019 ), the variables used to construct the experimental load profiles for 60 residential structures were consumer availability, occupant population, and age. The deployment of smart meters with specific devices, as well as the methodology for monitoring and analysis, are presented in Issi and Kaplan ( 2018 ), Teng and Yamazaki ( 2018 ). The writers in Yilmaz et al. ( 2020 ) investigate the major appliances that are responsible for this high energy consumption at the designated time of day to lower peak demand to 38% by implementing energy-efficient equipment. The stochastic ambient environment and user behavior, according to the currently available literature, make it challenging to develop a generalized load profile optimization algorithm that can accurately predict the energy consumption of various electrical appliances for various consumers.

In conclusion, compared to the usage of smart appliances and smart meters, load profiling assessment techniques like surveys, questionnaires, bottom-up, and top-down approaches are less technically complex, accurate, and time-consuming. However, performing this assessment comes at a far higher cost. By using the data produced by these smart devices, stakeholders may have a better knowledge of how they consume electricity. This is a crucial tactic to raise the power grids’ dependability and effectiveness.

Renewable energy integration

Since the use of renewable energy sources (RES) in the current power system seems to have a bright future, it is one of the factors considered while using DSM. Integration is very difficult, although encouraging, it may sometimes be irregular and intermittent (Elma et al. 2017 ). But in order to deal with the problems of power instability, power quality, and reliability brought on by RES’s intermittent nature, battery energy storage systems (BESS) are especially helpful (Elma et al. 2017 ). To address these difficulties, four battery consumption management techniques using centralized, decentralized, and distributed control structures have been investigated (Worthmann et al. 2015 ). The authors in (Yao et al. 2015 ) suggested an autonomous energy scheduling strategy to solve the problem of voltage escalation in HEMS. The DSM has recommended the optimal charging methods for plug-in electric cars (PHEV) and BESS to reduce the peak load demand (Mou et al. 2014 ). To assess how well the system uses its batteries, two metrics of battery efficiency factor and utilization factor have been created. It has been shown that system operating costs may decrease as battery efficiency increases (Nguyen et al. 2014 ). Since RES is rapidly evolving into one of the fundamental elements of DSM, it is imperative to develop cutting-edge optimization solutions for efficient load scheduling with the lowest cost while maintaining customer satisfaction.

By reducing system strain, which lowers the likelihood of power outages, diversifying the generation mix, and possibly improving power quality, it can be deduced from the literature that the integration of renewable energy can increase power network reliability. Moreover, it may help countries with climate change mitigation, energy cost reduction, and improving resistance to price volatility. Decentralized energy production, less environmental impact, and improved energy security are advantages of RES in DSM (Dincer and Bicer 2020 ). Yet, because the efficiency is lower than that of the conventional energy grid, synchronizing energy production and consumption is a significant issue for the energy sector. Nonetheless, the development of batteries has positively impacted the aforementioned constraint. The cost of production and the quantity of space needed for the use of this various energy are further barriers to the full integration of RES (Basit et al. 2020 ).

Load categorization

Electrical appliance classification is vital for efficient load management. These electrical loads may be categorized according to three standards:

Based on the appliances’ time of operation (Puente et al. 2020 ).

Based on power rating of appliances (Kim and Lee 2019 ).

Based on appliances’ total energy consumption (Ibrahim et al. 2023 ).

Deferrable and nondeferrable operated appliances make up the first standard’s loads, adjustable and nonadjustable operated appliances make up the second standard’s load, and basic and heavy operated appliances make up the third standard’s loads. It is important to note that there is presently no approved worldwide classification system for home appliances (Leitao et al. 2020 ). It should be noted that despite writers using the categorization suggested in Beaudin and Zareipour ( 2015 ), there is still no agreement on the appliances that belong to each group.

The literature classifies various smart home appliances based on user comfort and classification clarity. For scheduling home appliances, authors in the literature have used their own classification. Faisal et al. classified fifteen appliances as interruptible, non-interruptible, or base appliances. Among the interruptible appliances are the vacuum cleaner, sensors, PHEV, dishwasher, stove, microwave, and other intermittent loads. The clothes washer and spin dryer are non-interruptible appliances, while the oven, TV, PC, laptop, radio, and coffee maker are basic appliances (Faisal et al. 2019 ).

Shuja et al. classified fifteen appliances as shiftable, non-shiftable, or fixed. Water pumps, water heaters, vacuum cleaners, dishwashers, steam irons, air conditioners, and refrigerators are all shiftable appliances. Washing machines and tumble dryers are non-shiftable appliances, while TV, oven, desktops PC, blender, laptops, and ceiling fans are fixed appliances (Shuja et al. 2019 ). Thirteen smart home appliances were utilized (Rahim et al. 2016b ), including eight shiftable and five non-shiftable items. Shiftable appliances include an air conditioner, clothes dryer, washing machine, dishwasher, refrigerator, coffee maker, water heater, and space heater, whereas non-shiftable appliances include a fan, lamp, iron, toaster, and microwave oven. Abbasi et al. utilized eleven items divided into three categories: fixed appliances, shiftable appliances, and interruptible appliances. Fixed appliances include a lamp, oven, blender, and coffee maker. Shiftable appliances include the clothes dryer, washing machine, and dishwasher, whereas interruptible appliances include the water heater, iron, vacuum cleaner, and space heater (Abbasi et al. 2019 ). Eight shiftable appliances (dishwasher, refrigerator, air conditioner, clothes dryer, water heater, coffee maker, space heater, dishwasher) and six non-shiftable appliances (fan, light, blender, clothes iron, oven, and vacuum cleaner) were utilized (Rahim et al. 2018 ).

Deferrable and nondeferrable operated appliances

The time of operation of a deferrable appliance can be stopped, and restarted at other time slots. This is simply subdivided into interruptible and non-interruptible operated appliances (Abideen et al. 2017 ; Li et al. 2017 ).

Interruptible operated appliances may be stopped, interrupted, and resumed for a brief time without affecting the quality of the energy services provided, provided that it is completed before the deadline. Air conditioners, electric heaters, cold appliances, and hybrid electric automobiles are a few examples of interruptible operated equipment (PHEV). These appliances are also referred to as adjustable, shiftable, thermostatically controlled, and limitable operated equipment. These loads may be scheduled using a demand response system. Depending on the cost of the power or a financial incentive, they might be shifted from peak to off-peak hours, which will reduce the demand for peak load.

Non-interruptible operated appliances must finish their scheduled operation within a certain time frame. Non-interruptible appliances, also known as regular, fixed, non-adjustable, and non-controllable operated appliances, include lighting and kitchen systems. These loads are unsuitable for DR programs since they do not permit a time shift or interruption.

Adjustable and nonadjustable operated appliances

Most thermal loads are examples of adjustable operated appliances since they may be set to a lower level. These kinds of loads may actively take part in DR programs by reducing their total energy usage in line with energy pricing and financial incentives. However, it’s crucial to be informed that the DR software employed for these sorts of devices might make you uncomfortable while you wait. The overall consumption for non-adjustable loads is fixed (e.g., TVs and computers). An algorithm for demand response cannot plan for non-deferrable or non-adjustable loads (Li et al. 2017 ).

Basic and heavy operated appliances

An electrical appliance’s rating decides which categories it will fall under. Appliances with simple operating systems are those that use less energy. Lighting systems, televisions, laptops, and other basic operated appliances are just a few examples, and they hardly ever take part in DR programs. In contrast, appliances that require a lot of power consumption are more likely to be included in DR programs. The heavily operated appliances include things like air conditioners, electric cookers, and washing machines. The control of various appliances, particularly thermostatically controlled loads like air conditioning systems and electric water heaters, has already been the subject of several studies created by various authors (Du and Lu 2011 ; Goh and Apt 2004 ; Ibrahim et al. 2023 ; Ilic et al. 2002 ; Pedrasa et al. 2010 ).

The scheduling optimization problem involves many constraints. These restrictions apply to the system level as well as the appliance level. The restrictions listed below are addressed:

Electrical demand supply balance (Tasdighi et al. 2013 ):

The balance between the need for and supply of electricity at any given hour is shown in the equation below, which also accounts for power from batteries and the grid, load shifting, and both shiftable and non-shiftable load demands. Without considering load shifting

Considering Load Shifting

Temperature constraints (Tasdighi et al. 2013 ):

In this case, it is necessary to schedule thermostatically controllable loads (TCLs) with the understanding that the water and room temperatures must be maintained within a certain range.

The water temperature at the outlet is given as:

The HVAC room temperature is given as:

Battery constraints (Huang et al. 2016 ):

The manufacturer’s recommended range for battery level maintenance should be followed. As a result, the following constraints are put in place

Battery maximum charging and discharging power limit can be represented as:

Charge and discharge rate constraints for Electric vehicles (Zhao et al. 2012 )

Electric vehicles (EVs) are supposed to be charged and discharged at residential locations in this scenario. When parked at homes, EVs are typically wired into the residential metering systems.

During the charge cycle:

During the discharge cycle:

Grid constraints (Wong 1991 ):

Each time slot’s energy import from the grid must be upper bound by a predetermined limit to avoid overloading the utility.

User comfort-enabling constraints (Tamilarasu et al. 2021 ):

The wants and satisfaction of the users are given precedence in various circumstances. Certain limitations must be met to guarantee that the optimization process moves forward without significantly sacrificing comfort

Total daily load requirement:

Instantaneous power demand:

Idle constraint:

Phase wise energy requirement of appliances (Sou et al. 2011 ):

Since controllable appliances such as washing machines, and dishwashers have different power requirements at each operation cycle. This limitation guarantees each appliance’s operational cycle gets adequate energy for its functioning

Power safety (Sou et al. 2011 ):

This constraint places a maximum on the total energy allotted during any period, requiring that it always be less than the maximum energy from the grid.

Prioritization of appliance constraints (El-Metwally et al. 2006 ):

In this instance, the DSM optimization places a focus on the appliance priority. A priority index (PI), which is inversely proportional to the appliance’s load factor and proportionate to the peak demand of the appliance, is used to classify the loads

Up time required to finish a task (Paudyal and Ni 2019 ; Tasdighi et al. 2013 ):

When an appliance is switched on, it shouldn’t be shut off until the associated task is finished, for example, a dishwasher

where \(W_{n} (t)\) is the operation state of n th shiftable load at a time (t) 1: on, 0: off and \(TOP_{n}\) is the number of n th shiftable load’s time of operation.

Operation ordering of appliances (Paudyal and Ni 2019 ; Tasdighi et al. 2013 ):

The maintenance of the appliance’s operational ordering should be ensured. For instance, it is best to use the dryer after the washing machine has done its work. If shiftable load m is activated after shiftable load in such a scenario:

Dynamic pricing

Another element that exacerbates DSM challenges is dynamic pricing. One of the main goals of the reform of the energy market is to lower peak demand while increasing the use of all resources. Through various incentives provided by the utilities, customers are encouraged to participate in different dynamic pricing schemes. Since dynamic pricing encourages consumers to transfer their load from peak to off-peak periods, the scheduling issue for home energy usage must be addressed in this situation. The key elements influencing the structure of the electricity tariff are marginal cost, load pattern, societal considerations, and the profitability of the power company (Phuangpornpitak and Tia 2013 ). Numerous pricing strategies have been used, as can be shown in Fig.  6 to balance the supply and demand for energy. To preserve customer happiness and boost the system’s overall cost efficiency, advanced optimization algorithms must be developed to allow efficient energy consumption scheduling in addition to the reduction of dynamic tariffs (Panda et al. 2022 ).

Customer categorization

A thorough examination of numerous consumer categories may aid in a better understanding and design of DR. The customers are divided into four categories including the residential, commercial, industrial, and transportation sectors. In any of these categories, transportation is not a key problem for DR.

The residential sector is more challenging because of the diverse appliance consumption patterns, consumer dispersion, and individual user preferences. This suggests that rather than treating customers equally, each one is treated differently. Because the load profile and appliance use data are not readily available, DR adoption for industrial clients is quite challenging. Even with access to this data, the activities’ dependency on time makes it difficult to change energy use. Commercial users’ energy profiles may be modified with ease if they are identical. The most commonly used equipment, including air conditioners, heaters, ventilators, and lights, may be managed in line with the established specifications. It is crucial to remember that the DR is simple to deploy in the commercial and industrial sectors, allowing the system to react to DR fast.

Consumer behaviors

Some customers don’t respond well to price changes and it is unclear how people will respond to these programs. Customers have a variety of reactions to the price of electricity, and these reactions can be categorized as extremely flexible and unassuming behavior (Sharifi et al. 2017 ). Although there are many ways to implement DR and it offers many advantages, if the end user encounters any kind of difficulties, they may become disillusioned and leave the program or demand more money or incentives (Duncan and Hiskens 2011 ). The motivations behind these difficulties posed by each consumer’s decision to install microgeneration in their home are examined by the authors (Balcombe et al. 2014 ). They assert that inconveniencing people can prevent them from adopting technology.

The study by (Balcombe et al. 2014 ) does highlight an important aspect of end-use customers, namely that financial considerations are frequently more important than a desire to contribute to environmental change, even though micro-generation is a distinct but related problem. It is important to emphasize the importance of financial motivations, particularly in light of the high level of uncertainty previously mentioned regarding the potential financial benefits of enrolling in a DR program. The possibility is raised in (Boisvert and Neenan 2003 ), and raises a related financial concern, that the electricity bill savings from customers may not be sufficient to support equipment investment and make up for the inconvenience of continuously monitoring electricity prices when they may only need to react in exceptional circumstances. Naturally, this will depend on the type of software being used and the required level of customer interaction.

There will be little interest in DR if financial considerations are the primary factors influencing the adoption of DR programs and it is demonstrated that consumers will not be able to save money on their future power bills or recover their initial investment in DR technology. This dissuades people from using DR programs extensively. Despite receiving feedback on their energy use from in-home displays, most study participants continued with their regular routines and habits, according to research published in (Herrando et al. 2014 ). This is a great example of unanticipated or possibly irrational customer behavior, a challenge that needs to be taken into account when evaluating the DR implementation.

This study also emphasizes the importance of promoting greater DR knowledge and giving consumers the right information about DR programs for them to make informed decisions. As a result, utility companies won’t frequently send the DR resource (Cutter et al. 2012 ). This is a crucial factor to take into account when estimating the resource’s worth. It is crucial to take into account when estimating DR resources because it is connected to the traits and physical composition of electrical loads.

The main challenges are recognizing and properly accounting for the DR resource’s limitations as a result of end-user behavior and preferences in DR deployment. Understanding the variables that affect customers’ choices to accept or reject a DR program, as well as how these restrictions are reflected in the assessment study, is essential. Recognizing the potential effects that unanticipated consumer behavior may have on the DR features is essential as it successfully manages it throughout the evaluation process (Nolan and O’Malley 2015 ). Overall, different lifestyles and household activities have a significant influence on how much energy is used since it is predictable. Both long- and short-term trends are easily predicted. Participants reduce their electricity bills and Non-participating users may also save money since the programs shift power consumption from times when demand is highest to times when energy is least expensive.

• Optimization techniques

Numerous optimization strategies have been used to address the problems related to energy management. However, demand-side optimization methods are further divided into deterministic, stochastic, and hybrid approaches as illustrated in Fig.  7 .

The goal of this method of optimization is to find a universally optimal solution by using the analytic properties of the problem. It is also important to note that as the problem constraint shrinks, the likelihood of discovering global solutions increases, as well as the assurance of the quality of the optimal solutions attained. Linear programming (LP) (Erol-Kantarci and Mouftah 2011 ; Zhu et al. 2012 ), nonlinear programming (NLP) (Althaher et al. 2015 ), gradient base (GB) (Huang et al. 2015 ), Lagrangian algorithms (Boyd; Gatsis and Giannakis 2011 ), Lagrange–Newton (Dong et al. 2012 ), interior point method (Samadi et al. 2012 ) and Lyapunov techniques (Guo et al. 2012 ), and mixed integer nonlinear programming (MINP) (Behrangrad et al. 2010 ) are few examples of deterministic methods used in energy management to reduce the amount of electricity used.

Zhu et al. ( 2012 ) proposed an integer LP system to schedule electrical appliances, together with power sources and operating time, in accordance with user preferences to decrease peak loads. Similarly to this, Wang et al. developed the ideal dispatching model for a smart HEMS with distributed energy resources and smart home appliances using the MINLP methodology (Wang et al. 2012 ). The cost of electricity and total energy used are both decreased. Due to consumers’ unexpected, impulsive, non-linear, and complex energy usage behaviors, the MINLP was unable to regulate some appliances. Existing work on Deterministic Optimization Techniques is shown in Table 5 .

Stochastic approach

The stochastic method is an iterative algorithm that makes use of the unpredictable nature to identify the optimal solution from the parent solution. It employs a variety of techniques to the problem in an attempt to identify the best answer conceivable because of the high dimensional nonlinear objectives issue; however, unlike the deterministic method, the optimal solution is not guaranteed. Even though the problem where determinism methods have several local solutions, its singularity makes it a powerful tool in engineering. This approach is broken down into heuristic, meta-heuristic, and artificial intelligence categories in Fig.  7 .

Every strategy has advantages and disadvantages that vary depending on the optimization problems. Because of this, there isn’t a perfect answer to every optimization problem. The fundamental weaknesses and advantages of each random method examined in this work are summarized in Table 6 . A fuzzy inference system (FIS) is recommended by Hasaranga et al. ( 2017 ) for the management of an energy storage system that utilizes renewable energy sources and a storage unit. Comparison with a rule-based control method demonstrated the recommended system’s efficiency in lowering fluctuation and prolonging the lifetime of energy storage devices (ESS).

Ambreen et al. published a heuristic technique for cost, PAR, and the load reduction in the smart grid in 2017. The recommended algorithms provide the appliances in a home with the best schedule possible, Cost savings, reduced PAR, and user comfort are all obtained when appliances are designed. Costs are cut by 52% using GA scheduling, while PAR is cut by 23% (Ambreen et al. 2017 ). Hsu et al. developed a DPbased optimization strategy to reduce the system’s energy-producing costs for the DLC dispatch. As a consequence, the dispatch DLC approaches and the unit commitment issue were integrated, and a DP strategy was developed to address both issues (Hsu and Su 1991 ).

A model predictive control strategy based on weather forecasts is offered to reduce the amount of energy required and improve the utilization of renewable energy sources for energy management in residential microgrids. The established MPC control approach is based on a constrained optimal control problem for a certain time horizon. The proposed approach was contrasted with conventional rule-based control logic. Primary fossil energy usage has dropped by 14.5% on average while home comfort levels have increased (Bruni et al. 2015 ).

Noor et al. proposed a GTA technique for a demand-side management model that includes storage components in distinct research. In addition to reducing the peak to average ratio for the benefit of the electric grid, the suggested model can smooth out dips in the demand profile caused by supply restrictions. This was decided by every player who took part, their strategies, and the awards they received. Customers are the participants in this strategy, and the reward is determined by the lowest cost (Noor et al. 2018 ).

For a variety of consumer loads, BFO was used to reduce peak load and energy expenditures by 7% and 10%, respectively. This method outperforms earlier evolutionary algorithms for controlling controlled devices (Priya Esther et al. 2016 ). Similarly to this, Bharathi et al. recommend combining GA with an appropriate load shifting technique to reduce and reconfigure the load needs of all sorts of energy consumers (Bharathi et al. 2017 ). Based on TOU and IBR, Rahim et al. employed ACO to decrease energy usage at the residential load. The recommended approach may dramatically lower peak load, PAR, and energy expenditures without affecting customer satisfaction (Rahim et al. 2016a ).

Mahmood et al. recommended a HEMC model to control the scheduling of appliances, lowering user comfort, PAR, and electricity costs. However, energy is wasted significantly when appliances are used unnecessarily, and environmental concerns are also disregarded (Mahmood et al. 2016 ).

Another study advises evaluating a HEMS’s ability to control its energy expenses using GWO and BFO. These proposed techniques resulted in 45% and 55% energy reductions respectively (Barolli et al. 2020 ). Furthermore, (Elmouatamid et al. 2020 ) evaluated the performance of a HEMS by using three meta-heuristic optimization techniques and the HS, BFO, and EDE algorithms. Existing work on Stochastic Optimization Techniques is shown in Table 7 .

Another sub-category of stochastic optimization techniques worth discussing due to its constantly evolving field is machine language. Machine learning (ML) is an evolving branch of computational algorithms that are designed to emulate human intelligence by learning from the surrounding environment. They are considered the working horse in the new era of the so-called big data, which has been used to address different issues in DSM as shown in Table 8 (Antonopoulos et al. 2020 ). The main types of machine learning are supervised learning, unsupervised learning, and reinforcement learning as stated (Murphy 2012 ). Figure  8 shows the subtypes of machine learning used in DR.

Machine language used in DSM (Antonopoulos et al. 2020 )

Supervised machine learning (SML) is the task of generating meaning from labeled training data that includes a set of training examples. In supervised learning, each example is a mainstay that contains an input object (typically a vector quantity) and an enforced output value (may also be referred to as a supervisory signal) (Praveena and Jaiganesh 2017 ). The authors in Giovanelli et al. ( 2017 ), Pal and Kumar ( 2016 ), Yang et al. ( 2018 ) proposed Support Vector Regression (SVR) to forecast the price of energy. This technique is also used for short time load forecasting for non-aggregated loads (Zhou et al. 2016 ).

Unsupervised machine learning (UML) approaches are very beneficial in description tasks because they try to discover links in a data structure without requiring a quantifiable output. Because there is no response variable to oversee the study, this kind of machine learning is referred to as unsupervised (Gareth et al. 2013 ). Cao et al. examine the clustering of 4000 households from the Irish CER dataset over 18 months using K-means, SOM, and hierarchical clustering algorithms with various distance calculations based on the 17 most significant PCA components (Cao et al. 2013 ).

Reinforcement learning (RL) is the task of determining how agents should perform actions in a given environment to maximize cumulative rewards. Q-learning is commonly used at the HEMS level to optimize appliance scheduling by using cost and user comfort as reward functions (O’Neill et al. 2010 ; Wen et al. 2015 ). O’Neill et al. consider pre-specified disutility functions for customers’ dissatisfaction with job scheduling (O’Neill et al. 2010 ), but Wen et al. address this limitation (Wen et al. 2015 ). A state in this context is made up of a price sequence from the retailer or aggregator, a vector that reflects the user’s consumption of specific appliances over time, and sometimes the priority of the considered device.

Hybrid approach

The hybrid approaches have been used in numerous engineering applications to get beyond the drawbacks of each optimization strategy and enhance their efficacy and accuracy to give a greater performance of the system (Tsipianitis & Tsompanakis). Several of the hybrid approaches used in DSM are briefly described below:

First, the teacher and learning-based optimization (TLBO) and the shuffling frog leap (SFL) methods of optimization are recommended. In this concept, the load is separated into three categories: shiftable, sheddable, and non-sheddable loads. The recommended strategy aimed to bring down the cost of electricity. This research employs ToU, RTP, and CPP as three alternative pricing models. The findings demonstrated that the recommended approach was successful in reducing consumption costs (Derakhshan et al. 2016 ).

Rahim et al. ( 2016a , b ) investigated the efficacy of binary particle swarm optimization (BPSO), ant colony optimization (ACO), and genetic algorithm (GA). Lowering power prices and the peak-to-average ratio (PAR) while taking into consideration RESs and storage systems is the main objective of the proposed effort (Rahim et al. 2016b ).

However, the validation results showed that GAPSO performed better than GA and BPSO in terms of cost and discomfort, lowering peak power use by 7.8532% and 27.7794%, respectively. While GA and BSPO reduced the cost of energy consumption by 24.0470% and 29.9702%, respectively, while GAPSO decreased peak power consumption (PAR) by 36.39%. While needing the least amount of waiting time, GAPSO was able to reduce consumption expenses by up to 25.2923% (Javaid et al. 2017a ).

In Küçüker et al. ( 2017 ), a hybrid energy management strategy is proposed by using a hierarchical genetic algorithm (HGA) to alter the fuzzy inference system’s rule base. The fuzzy-HGA method seems to be more effective than the conventional fuzzy-GA approach, even with just 47% of the total rules in the rule base. By purchasing a more basic fuzzy logic controller, the entire control system can be implemented in real time on low-cost embedded electronic devices. A fuzzy logic-based EMS is presented in Panwar et al. ( 2017 ) to lower the fluctuations and peak powers of a grid-tied microgrid. In a similar line, the study (Pascual et al. 2015 ) proposes the conventional fuzzy-genetic algorithm approach.

A hybrid power system for residential structures was the subject of an energy management strategy developed by Zenned et al. ( 2017 ). When compared to buying electricity from the grid, this plan’s results show a decrease in energy use, however, the modeling fails to take energy costs into account (Zenned et al. 2017 ).

A nonlinear MPC approach is recommended (Merabet et al. 2016 ). Using a synthetic NN, the loading trough was estimated. Voltage stability may be maintained by regulating the battery state of charge (SOC) and planning the load. Grid Connected based MPC EMS is used to reduce energy expenses (Arcos-Aviles et al. 2017 ).

Javaid et al. developed a hybrid genetic wind-driven (HGWD) technique to build a DSM controller for a residential area in an SG. The result shows that the HGWD algorithm performed the best. By lowering the cost of power use by 33% and 10%, respectively, when compared to the WDO algorithm and GA. To get the best results, the HGWD reduced user comfort by 40%, PAR by 17%, and electricity costs by 30% (Javaid et al. 2017b ). A hybrid method that combines PSO and Gray wolf optimization (GWO) is suggested using day-ahead scheduling (Hussain et al. 2016 ).

The hybrid GA/PSO method (HGPSO) was introduced by Ahmad et al. who also showed that it outperformed the GA, BPSO, BFO, and WDO algorithms. For the GA, BPSO, BFO, and WDO algorithms, the percentage of power bill decrease was 9.80%, 19.50%, 15.40%, and 15.80%, respectively. Each algorithm’s percentage of PAR reduction was 14.09%, 3.30%, 22.10%, and 33.54%. The PAR and the electric cost were reduced by 25.12% and 24.88% respectively by the HGPSO (Ahmad et al. 2017 ). In another investigation, the GA was put up against a more advanced PSO algorithm (IPSO). The peak load was reduced with the IPSO by about 30.26% while it was reduced with the GA by 25.78% (Yang et al. 2015 ).

The simulation results show how efficiently the proposed algorithm GHSA minimizes user discomfort while decreasing PAR and power costs. The GHSA reduces the peak load at 3.73 kWh in contrast to the present heuristic methods (13.84 kWh). According to the findings, smart home (SH) expenses have been decreased by WDO, HSA, GA, and GHSA to 2.61, 1.72, 1.12, and 1.34 cents/h, respectively (Javaid et al. 2017b ).

Manzoor et al. introduced the teacher learning genetic optimization (TLGO) method and compared it to the teacher learning-based optimization (TLBO) and GA for residential load scheduling with a day-ahead pricing scheme. Cost reductions of 31%, 31.5%, and 33% were produced by the GA, TLBO, and TLGO, respectively. User discomfort was lowest with TLGO when compared to GA and TLBO. User discomfort with the GA, TLBO, and TLGO had corresponding values of 2.37, 2.14, and 1.83 (Manzoor et al. 2017 ).

The hybrid algorithm known as the bat-crow search algorithm (BCSA) was developed by Javaid et al. by combining a meta-heuristic bat algorithm (BA) and a crow search algorithm (CSA). Using the critical peak pricing (CPP) system for HEMS, they compared the outcomes of BCSA with BA and CSA in terms of the amount of power cost reduction. According to the findings of optimization, the BCSA algorithm lowered power expenses by 31.19%, while the BA and CSA cut costs by 28.32% and 26.70%, respectively. The description above suggests that hybrid algorithms perform better than single algorithms because they are more adaptable and effective (Javaid et al. 2018 ). Existing work on Hybrid Optimization Techniques is shown in Table 9 .

Future work

The majority of the review focused on thermal comfort and appliance waiting time to address customer satisfaction. The user’s experience at a DR event, their social comfort, and other social variables should be taken into consideration as they can boost user satisfaction. It’s crucial to model EVs as both a load and a generator to make the most out of the system. Peer-to-peer trade between prosumers may result in flexible assets with lower costs. Most of the work that was examined represented EVs as interruptible or storage systems.

Fairness between users, standardization, and SG interoperability must be guaranteed while developing a DSM program. For the real-time synchronization and integration of security, safety, smart appliances, and monitoring, extensive research is needed to secure the security and privacy of customers’ data. In addition to this, the agencies, shareholders, and policymakers need to step up and enact new rules and policies to increase the trust of the public. A thorough evaluation of the technical, economic, and environmental performance of current and upcoming DSM systems is required. This is needed to compare DSM and conventional treatments fairly.

The convergence and computation times of DSM optimization problems are improved by the hybrid algorithms-based optimization models. However, while choosing an algorithm to solve DSM optimization issues, other factors such as problem types (such as single- or multi-objective), optimization types (such as local or global), robustness, and accuracy should be taken into account.

As DSM, as previously said, enables both system operation and system development, it offers versatile advantages and value. However, the business case for DSM has not been well established since there are no tools for weighing costs and advantages. There is still a lot of work to be done in this area.

The primary system operating variables will often determine the DSM value’s size (i.e., the value of demand controllability). The system stress, or how close the system is to being loaded to its full capacity and hence needing reinforcement, should be taken into account in this situation. Even though it is often low in systems with significant spare capacity, the value of DSM will be high in system components that need reinforcement.

This paper provides a comprehensive analysis of the different technologies, approaches used in DSM as well as the impact of distributed renewable energy generation and storage technologies in SG. The main goal of these methods is to decrease peak load demands and achieve advanced synchronization between network operators and customers via the development and application of power-saving technologies, financial incentives, the price of energy, and government rules. This research thoroughly investigated DSM implementation issues that must be overcome for DSM to be effectively integrated into the SG with some proposed solutions, DSM optimization methodologies, and their related solutions, which were not included in the earlier review article. As a consequence, a comprehensive comparison of many algorithms used in DSM optimization problems is provided in terms of a variety of factors such as energy cost reduction, PAR, waiting time, power scheduling, Voltage limitations, DR, risk management, client privacy, and carbon emission. We determined, after examining multiple DSM-based research, that a single strategy is not the best solution to handle the high complexity of the DSM optimization problem due to its poor performance and low convergence rate. As a consequence, hybrid algorithms may outperform single algorithms in terms of convergence rate, complexity, noisy environment, imprecision, uncertainty, and ambiguity. Furthermore, these tactics may be improved in the future to improve SG’s efficiency by balancing supply and demand. Even though these current breakthroughs in the use of optimization techniques in DSM are widely known, extra research is undoubtedly necessary to discover the optimal solutions in many real-world scenarios.

The power system’s functioning will become more difficult if corrective control is used. This is just another obstacle to the adoption of DSM. Yet, given that adaptability is increasingly seen as a key tool for coping with the unpredictability of future developments, together with the ongoing cost reductions of DSM technologies, it is anticipated that DSM will become noticeably more competitive in the near future. Increasing trust in the employment of DSM schemes for the provision of system security will benefit from the establishment of targeted trial schemes. This comprehensive review of DSM will assist all researchers in this field in improving energy management strategies and reducing the effects of system uncertainties, variances, and restrictions.

Availability of data and materials

Data sharing is not applicable to this articles as no datasets were generated or analysed during the current study.

Abbreviations

Software-Defined Network

Interdependent Networks

Field Area Networks

Wireless Sensor Networks

Neighborhood Area Networks

Advanced metering infrastructure

Supply side management

• Demand side management

Time of use

Critical peak pricing

Real time pricing

Renewable energy sources

Priority index

Thermal energy storage

Home energy management system

Direct load control

Capacity Market Program

Auxiliary service market

Battery energy storage system

Nonlinear programming

Mixed integer linear programming

Convex nonlinear programming

Peak to average ratio

Dynamic programming

Game theory algorithms

Particle swarm optimization

Grey wolf optimization

Harmony search algorithm

Binary particle swarm optimization

Satin bowerbird optimizer

Sine cosine algorithm

Crow search algorithm

Moth Fly Optimization

Cuckoo optimization algorithm

Firefly algorithm

Cat search algorithm

Differential evolution

Cultural algorithm

Artificial immune system

Earth Worm Algorithm

Shuffling frog leap

Adaptive neuro fuzzy logic

Improved particle swarm optimization

Genetic harmony search algorithms

Bat-crow search algorithm

Karush–Kuhn–Tucker

Gravitational Search Algorithm

Backtracking Search Optimization

Effective Differential Evolution

Hybrid genetic algorithm

Hybrid Effective Differential Evolution

Modified clonal selection algorithm

Hybrid genetic particle wind driven optimization

Hybrid genetic particle swarm optimization

Deep Reinforcement Learning

Elephant herding optimization

Unmanned aerial vehicle neural-fuzzy classification

Elephant herding optimization neuro fuzzy

Distributed automation

Distributed energy resources

Teleprotection

Anomaly detection

Substation automation

Privacy preserving

Inclined block rate

Energy management system

Programmable logic controller

Supervisory control and data acquisition

Building management system

Emergency demand response

Interruptible Curtailable Service

Demand bidding/buyback

Electric energy storage

Linear programming

Mixed integer nonlinear programming

Quadratic programming

Fuzzy logic interfere

Genetic algorithms

Model predictive control

Bat algorithm

Ant colony optimization

Artificial neural network

Reinforcement learning

Polar bear optimization

Whale optimization algorithm

Mosquito Host Seeking

Colliding body optimization

Social spider optimization

Biogeography based optimization

Imperialist competitive algorithm

Artificial bee colony

Bacterial foraging optimization

Candidate solution updation algorithm

Jaya Optimization Algorithm

Fuzzy logic

Teacher and learning-based optimization

Genetic algorithm particle swarm optimization

Hybrid genetic wind-driven

Teacher learning genetic optimization

Mixed grey wolf optimization

Runner Updation Optimization Algorithm

Enhanced leader particle swarm optimization

Expert advisors

Flower pollination algorithm

Math Kernel Library

Bacterial foraging optimization algorithm

Levy Whale Optimization Algorithm

Levy Whale Modified Crow Search Optimizer

Hybrid beamforming particle swarm optimization

Wind driven genetic algorithms

Wind driven grey wolf optimization

Wind driven binary particle swarm optimization

Power transferred from the grid at time (t) in kW

Power transferred from the battery at time (t) in kW

Total power consumption from non-shiftable loads at time (t)

Total power consumption from shiftable loads at time (t)

Shiftable loads

Minimum and maximum water outlet temperature in tank respectively

Mixed water temperature in the tank at interval i

Minimum and maximum room temperature respectively

Room temperature at interval i.

Minimum and maximum temperature

Minimum and maximum state of charge of battery at time (t)

Capacity of battery and the energy of battery at any time (t) in (kWh)

Battery’s charging and discharging power respectively at time (t)

Maximum battery’s charging and discharging power respectively

Battery’s charge efficiency

Charging and discharging power of EV at time (t) respectively

Maximum power level of EV at time (t).

Instantaneous and maximum instantaneous power demand (kW) respectively

Customer satisfaction

Energy requirement for energy phase j in appliance i.

Energy assigned to energy phase j of appliance i during the whole period of time slot

Total energy required by all running appliances at time (t)

Maximum energy from grid at that time (t).

Operation state of shift able load at time (t)

Number of shiftable load’s time of operation

Aalami HA, Khatibzadeh A (2016) Regulation of market clearing price based on nonlinear models of demand bidding and emergency demand response programs. Int Trans Electr Energy Syst 26(11):2463–2478

Article   Google Scholar  

Aalami H, Moghaddam MP, Yousefi G (2010) Demand response modeling considering interruptible/curtailable loads and capacity market programs. Appl Energy 87(1):243–250

Aalami HA, Pashaei-Didani H, Nojavan S (2019) Deriving nonlinear models for incentive-based demand response programs. Int J Electr Power Energy Syst 106:223–231

Abbasi RA, Javaid N, Khan S, Asif RM, Ahmad W (2019) Minimizing daily cost and maximizing user comfort using a new metaheuristic technique. Paper presented at the workshops of the international conference on advanced information networking and applications

Abideen ZU, Jamshaid F, Zahra A, Rehman AU, Razzaq S, Javaid N (2017) Meta-heuristic and nature inspired approaches for home energy management. Paper presented at the international conference on network-based information systems

Abualigah L, Diabat A (2021) Advances in sine cosine algorithm: a comprehensive survey. Artif Intell Rev 54(4):2567–2608

Adepetu A, Rezaei E, Lizotte D, Keshav S (2013) Critiquing time-of-use pricing in Ontario. Paper presented at the 2013 IEEE international conference on smart grid communications (SmartGridComm)

Aghaei J, Alizadeh M-I (2013) Demand response in smart electricity grids equipped with renewable energy sources: a review. Renew Sustain Energy Rev 18:64–72

Aghajani G, Shayanfar H, Shayeghi H (2015) Presenting a multi-objective generation scheduling model for pricing demand response rate in micro-grid energy management. Energy Convers Manag 106:308–321

Ahmad A, Khan A, Javaid N, Hussain HM, Abdul W, Almogren A, Alamri A, Azim Niaz I (2017) An optimized home energy management system with integrated renewable energy and storage resources. Energies 10(4):549

Ahmad MF, Isa NAM, Lim WH, Ang KM (2021) Differential evolution: a recent review based on state-of-the-art works. Alex Eng J 61:3831–3872

Ahmed MS, Mohamed A, Khatib T, Shareef H, Homod RZ, Abd Ali J (2017) Real time optimal schedule controller for home energy management system using new binary backtracking search algorithm. Energy Build 138:215–227

Ahmed AM, Rashid TA, Saeed SAM (2020) Cat swarm optimization algorithm: a survey and performance evaluation. Comput Intell Neurosci. https://doi.org/10.1155/2020/4854895

Al Essa MJM (2019) Home energy management of thermostatically controlled loads and photovoltaic-battery systems. Energy 176:742–752

Al Hasib A, Nikitin N, Natvig L (2014) Load scheduling in smart buildings with bidirectional energy trading. Paper presented at the 2014 IEEE 33rd international performance computing and communications conference (IPCCC)

Alasseri R, Tripathi A, Rao TJ, Sreekanth K (2017) A review on implementation strategies for demand side management (DSM) in Kuwait through incentive-based demand response programs. Renew Sustain Energy Rev 77:617–635

Al-enezi AN (2010) Demand side management (DSM) for efficient use of energy in the residential sector in Kuwait: analysis of options and priorities

Alipour M, Zare K, Abapour M (2017) MINLP probabilistic scheduling model for demand response programs integrated energy hubs. IEEE Trans Ind Inform 14(1):79–88

Althaher S, Mancarella P, Mutale J (2015) Automated demand response from home energy management system under dynamic pricing and power and comfort constraints. IEEE Trans Smart Grid 6(4):1874–1883

Amatullah A, Agung A, Arif A (2021) Minimizing power peaking factor of BEAVRS-based reactor using polar bear optimization algorithms. Paper presented at the IOP Conference Series: Earth and Environmental Science

Ambreen K, Khalid R, Maroof R, Khan HN, Asif S, Iftikhar H (2017) Implementing critical peak pricing in home energy management using biography based optimization and genetic algorithm in smart grid. Paper presented at the international conference on broadband and wireless computing, communication and applications

Amini M, Frye J, Ilić MD, Karabasoglu O (2015) Smart residential energy scheduling utilizing two stage mixed integer linear programming. Paper presented at the 2015 North American Power Symposium (NAPS)

Amrollahi MH, Bathaee SMT (2017) Techno-economic optimization of hybrid photovoltaic/wind generation together with energy storage system in a stand-alone micro-grid subjected to demand response. Appl Energy 202:66–77

Antonopoulos I, Robu V, Couraud B, Kirli D, Norbu S, Kiprakis A, Flynn D, Elizondo-Gonzalez S, Wattam S (2020) Artificial intelligence and machine learning approaches to energy demand-side response: a systematic review. Renew Sustain Energy Rev 130:109899

Anvari-Moghaddam A, Monsef H, Rahimi-Kian A (2014) Optimal smart home energy management considering energy saving and a comfortable lifestyle. IEEE Trans Smart Grid 6(1):324–332

Anvari-Moghaddam A, Rahimi-Kian A, Mirian MS, Guerrero JM (2017) A multi-agent based energy management solution for integrated buildings and microgrid system. Appl Energy 203:41–56

Arcos-Aviles D, Pascual J, Guinjoan F, Marroyo L, Sanchis P, Marietta MP (2017) Low complexity energy management strategy for grid profile smoothing of a residential grid-connected microgrid using generation and demand forecasting. Appl Energy 205:69–84

Arteconi A, Hewitt NJ, Polonara F (2012) State of the art of thermal storage for demand-side management. Appl Energy 93:371–389

Atia R, Yamada N (2016) Sizing and analysis of renewable energy and battery systems in residential microgrids. IEEE Trans Smart Grid 7(3):1204–1213

Awais M, Javaid N, Shaheen N, Iqbal Z, Rehman G, Muhammad K, Ahmad I (2015) An efficient genetic algorithm based demand side management scheme for smart grid. Paper presented at the 2015 18th international conference on network-based information systems

Awais M, Javaid N, Aurangzeb K, Haider SI, Khan ZA, Mahmood D (2018) Towards effective and efficient energy management of single home and a smart community exploiting heuristic optimization algorithms with critical peak and real-time pricing tariffs in smart grids. Energies 11(11):3125

Babaei Keshteli H, Rostamy-Malkhalifeh M, Hosseinzadeh Lotfi F (2021) Ranking of decision making units using the imperialist competitive algorithm in DEA. Meas Control 54(9–10):1326–1335

Bahamish HA, Al-Aidroos NM, Boraik AN (2021) Modified crow search algorithm for protein structure prediction

Balcombe P, Rigby D, Azapagic A (2014) Investigating the importance of motivations and barriers related to microgeneration uptake in the UK. Appl Energy 130:403–418

Barbato A, Capone A, Chen L, Martignon F, Paris S (2013) A power scheduling game for reducing the peak demand of residential users. Paper presented at the 2013 IEEE online conference on green communications (OnlineGreenComm)

Barolli L, Amato F, Moscato F, Enokido T, Takizawa M (2020) Web, artificial intelligence and network applications: proceedings of the workshops of the 34th international conference on advanced information networking and applications (WAINA-2020), vol 1150. Springer Nature

Basit MA, Dilshad S, Badar R, Sami ur Rehman SM (2020) Limitations, challenges, and solution approaches in grid-connected renewable energy systems. Int J Energy Res 44(6):4132–4162

Beaudin M, Zareipour H (2015) Home energy management systems: a review of modelling and complexity. Renew Sustain Energy Rev 45:318–335

Behrangrad M (2015) A review of demand side management business models in the electricity market. Renew Sustain Energy Rev 47:270–283

Behrangrad M, Sugihara H, Funaki T (2010) Analyzing the system effects of optimal demand response utilization for reserve procurement and peak clipping. Paper presented at the IEEE PES general meeting

Bharathi C, Rekha D, Vijayakumar V (2017) Genetic algorithm based demand side management for smart grid. Wirel Pers Commun 93(2):481–502

Bina MT, Ahmadi D (2015a) Stochastic modeling for the next day domestic demand response applications. IEEE Trans Power Syst 30(6):2880–2893

Bina VT, Ahmadi D (2015b) Stochastic modeling for scheduling the charging demand of EV in distribution systems using copulas. Int J Electr Power Energy Syst 71:15–25

Blake ST, O’Sullivan DT (2018) Optimization of distributed energy resources in an industrial microgrid. Procedia CIRP 67:104–109

Boisvert RN, Neenan BF (2003) Social welfare implications of demand response programs in competitive electricity markets. Lawrence Berkeley National Lab. (LBNL), Berkeley

Book   Google Scholar  

Bongomin O, Nziu P (2022) A critical review on the development and utilization of energy systems in Uganda

Boyd S. Dynamic network energy management via proximal message passing

Bruni G, Cordiner S, Mulone V, Rocco V, Spagnolo F (2015) A study on the energy management in domestic micro-grids based on model predictive control strategies. Energy Convers Manag 102:50–58

Bukoski JJ, Chaiwiwatworakul P, Gheewala SH (2016) Energy savings versus costs of implementation for demand side management strategies within an energy-efficient tropical residence. Energy Effic 9(2):473–485

Cao H-Â, Beckel C, Staake T (2013) Are domestic load profiles stable over time? An attempt to identify target households for demand side management campaigns. Paper presented at the IECON 2013—39th annual conference of the IEEE industrial electronics society

Cappers P, Goldman C, Kathan D (2010) Demand response in US electricity markets: empirical evidence. Energy 35(4):1526–1535

Chanal PM, Kakkasageri MS, Manvi SKS (2021) Security and privacy in the internet of things: computational intelligent techniques-based approaches. In: Recent trends in computational intelligence enabled research. Elsevier, Amsyerdam, pp 111–127

Chatziioannou K, Guštinčič J, Tjernberg LB (2013) On experience of smart grid projects in Europe and the Swedish demonstration projects. Chalmers University of Technology, Gothenburg

Google Scholar  

Chen C, Wang J, Kishore S (2014) A distributed direct load control approach for large-scale residential demand response. IEEE Trans Power Syst 29(5):2219–2228

Chen H, Wang L, Di J, Ping S (2020) Bacterial foraging optimization based on self-adaptive chemotaxis strategy. Comput Intell Neurosci. https://doi.org/10.1155/2020/2630104

Chintam JR, Daniel M (2018) Real-power rescheduling of generators for congestion management using a novel satin bowerbird optimization algorithm. Energies 11(1):183

Chokpanyasuwan C, Bunnag T, Prommas R (2015) Ant colony optimization for load management based on load shifting in the textile industry. Am J Appl Sci 12(2):142

Conchado A, Linares P (2012) The economic impact of demand-response programs on power systems. A survey of the state of the art. Handb Netw Power Syst I:281–301

Conteh A, Lotfy ME, Kipngetich KM, Senjyu T, Mandal P, Chakraborty S (2019) An economic analysis of demand side management considering interruptible load and renewable energy integration: a case study of Freetown Sierra Leone. Sustainability 11(10):2828

Cortese T, Almeida J, Batista G, Storopoli J, Liu A, Yigitcanlar T (2022) Understanding sustainable energy in the context of smart cities: a PRISMA review. Energies 15:2382; s Note: MDPI stays neu-tral with regard to jurisdictional claims in ….

Cutter E, Woo CK, Kahrl F, Taylor A (2012) Maximizing the value of responsive load. Electr J 25(7):6–16

Derakhshan G, Shayanfar HA, Kazemi A (2016) The optimization of demand response programs in smart grids. Energy Policy 94:295–306

Dincer I, Bicer Y (2020) Integration of renewable energy systems for multigeneration. In: Integrated energy systems for multigeneration. pp 287–402

Dong Q, Yu L, Song W-Z, Tong L, Tang S (2012) Distributed demand and response algorithm for optimizing social-welfare in smart grid. Paper presented at the 2012 IEEE 26th international parallel and distributed processing symposium

Dranka GG, Ferreira P (2019) Review and assessment of the different categories of demand response potentials. Energy 179:280–294

Du P, Lu N (2011) Appliance commitment for household load scheduling. IEEE Trans Smart Grid 2(2):411–419

Duncan SC, Hiskens I (2011) Achieving controllability of electric loads’. Paper presented at the IEEE proceedings

Durairasan M, Ramprakash S, Balasubramanian D (2021) System modeling of micro-grid with hybrid energy sources for optimal energy management—a hybrid elephant herding optimization algorithm-adaptive neuro fuzzy inference system approach. Int J Numer Model Electron Netw Devices Fields 34(6):e2915

Elma O, Selamoğullari US (2017) Paper presented at the 2017 4th international conference on electrical and electronic engineering (ICEEE)

Elma O, Taşcıkaraoğlu A, Ince AT, Selamoğulları US (2017) Implementation of a dynamic energy management system using real time pricing and local renewable energy generation forecasts. Energy 134:206–220

El-Metwally M, El-Sobki M, Attia H, Wahdan S (2006) Paper presented at the 2006 eleventh international Middle East power systems conference

Elmouatamid A, Ouladsine R, Bakhouya M, El Kamoun N, Khaidar M, Zine-Dine K (2020) Review of control and energy management approaches in micro-grid systems. Energies 14(1):168

Eltaeib T, Mahmood A (2018) Differential evolution: a survey and analysis. Appl Sci 8(10):1945

Erdinc O, Paterakis NG, Mendes TD, Bakirtzis AG, Catalão JP (2014) Smart household operation considering bi-directional EV and ESS utilization by real-time pricing-based DR. IEEE Trans Smart Grid 6(3):1281–1291

Erol-Kantarci M, Mouftah HT (2011) Wireless sensor networks for cost-efficient residential energy management in the smart grid. IEEE Trans Smart Grid 2(2):314–325

Esther BP, Kumar KS (2016) A survey on residential demand side management architecture, approaches, optimization models and methods. Renew Sustain Energy Rev 59:342–351

Eusuff M, Lansey K, Pasha F (2006) Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization. Eng Optim 38(2):129–154

Article   MathSciNet   Google Scholar  

Faisal HM, Javaid N, Qasim U, Habib S, Iqbal Z, Mubarak H (2019) An efficient scheduling of user appliances using multi objective optimization in smart grid. Paper presented at the workshops of the international conference on advanced information networking and applications

Fang X, Misra S, Xue G, Yang D (2011) Smart grid—the new and improved power grid: a survey. IEEE Commun Surv Tutor 14(4):944–980

Fang G-H, Wu C-J, Liao T, Huang X-F, Qu B (2020) A two-layer improved invasive weed optimization algorithm for optimal operation of cascade reservoirs. Water Supply 20(6):2311–2323

Fanti MP, Mangini AM, Roccotelli M (2018) A simulation and control model for building energy management. Control Eng Pract 72:192–205

Faria P, Soares J, Vale Z, Morais H, Sousa T (2013) Modified particle swarm optimization applied to integrated demand response and DG resources scheduling. IEEE Trans Smart Grid 4(1):606–616

Faruqui A, Sergici S (2010) Household response to dynamic pricing of electricity: a survey of 15 experiments. J Regul Econ 38(2):193–225

Feng X, Liu X, Yu H (2016) Group mosquito host-seeking algorithm. Appl Intell 44(3):665–686

Freeman R (2005) Managing energy: reducing peak load and managing risk with demand response and demand side management. Refocus 6(5):53–55

Gaber M, El-Banna S, El-Dabah M, Hamad M (2021) Designing and implementation of an intelligent energy management system for electric ship power system based on adaptive neuro-fuzzy inference system (ANFIS). Adv Sci Technol Eng Syst J 6(2):195–203

Gareth J, Daniela W, Trevor H, Robert T (2013) An introduction to statistical learning: with applications in R. Spinger, Cham

MATH   Google Scholar  

Gatsis N, Giannakis GB (2011) Residential demand response with interruptible tasks: duality and algorithms. Paper presented at the 2011 50th IEEE conference on decision and control and European control conference

Gelazanskas L, Gamage KA (2014) Demand side management in smart grid: a review and proposals for future direction. Sustain Cities Soc 11:22–30

Gellings CW (2017) Evolving practice of demand-side management. J Mod Power Syst Clean Energy 5(1):1–9

Giovanelli C, Liu X, Sierla S, Vyatkin V, Ichise R (2017) Towards an aggregator that exploits big data to bid on frequency containment reserve market. Paper presented at the IECON 2017—43rd annual conference of the IEEE Industrial Electronics Society

GK JS (2020) MANFIS based SMART home energy management system to support SMART grid. Peer Peer Netw Appl 13(6):2177–2188

Goh CHK, Apt J (2004) Consumer strategies for controlling electric water heaters under dynamic pricing. Paper presented at the Carnegie Mellon Electricity Industry Center working paper

Goubko MV, Kuznetsov SO, Neznanov AA, Ignatov DI (2016) Bayesian learning of consumer preferences for residential demand response. IFAC-PapersOnLine 49(32):24–29

Guo Y, Pan M, Fang Y (2012) Optimal power management of residential customers in the smart grid. IEEE Trans Parallel Distrib Syst 23(9):1593–1606

Guo W, Liu T, Dai F, Xu P (2020) An improved whale optimization algorithm for feature selection. Comput Mater Contin 62:337–354

Gyamfi S, Krumdieck S, Urmee T (2013) Residential peak electricity demand response—highlights of some behavioural issues. Renew Sustain Energy Rev 25:71–77

Hafeez G, Javaid N, Iqbal S, Khan FA (2018) Optimal residential load scheduling under utility and rooftop photovoltaic units. Energies 11(3):611

Haffaf A, Lakdja F, Meziane R, Abdeslam DO (2021) Study of economic and sustainable energy supply for water irrigation system (WIS). Sustain Energy Grids Netw 25:100412

Haider HT, See OH, Elmenreich W (2016) Residential demand response scheme based on adaptive consumption level pricing. Energy 113:301–308

Hajibandeh N, Shafie-Khah M, Osório GJ, Aghaei J, Catalão JP (2018) A heuristic multi-objective multi-criteria demand response planning in a system with high penetration of wind power generators. Appl Energy 212:721–732

Harish V, Kumar A (2014) Demand side management in India: action plan, policies and regulations. Renew Sustain Energy Rev 33:613–624

Hasaranga W, Hemarathne R, Mahawithana M, Sandanuwan M, Hettiarachchi H, Hemapala K (2017) A fuzzy logic based battery SOC level control strategy for smart micro grid. Paper presented at the 2017 third international conference on advances in electrical, electronics, information, communication and bio-informatics (AEEICB)

Hernández-Ocana B, Mezura-Montes E, Pozos-Parra P (2013) A review of the bacterial foraging algorithm in constrained numerical optimization. Paper presented at the 2013 IEEE congress on evolutionary computation

Herrando M, Markides CN, Hellgardt K (2014) A UK-based assessment of hybrid PV and solar-thermal systems for domestic heating and power: system performance. Appl Energy 122:288–309

Hsu Y-Y, Su C-C (1991) Dispatch of direct load control using dynamic programming. IEEE Trans Power Syst 6(3):1056–1061

Huang Y, Tian H, Wang L (2015) Demand response for home energy management system. Int J Electr Power Energy Syst 73:448–455

Huang Y, Wang L, Guo W, Kang Q, Wu Q (2016) Chance constrained optimization in a home energy management system. IEEE Trans Smart Grid 9(1):252–260

Hussain I, Mohsin S, Basit A, Khan ZA, Qasim U, Javaid N (2015) A review on demand response: pricing, optimization, and appliance scheduling. Procedia Comput Sci 52:843–850

Hussain A, Bui V-H, Kim H-M (2016) A resilient and privacy-preserving energy management strategy for networked microgrids. IEEE Trans Smart Grid 9(3):2127–2139

Hussain HM, Javaid N, Iqbal S, Hasan QU, Aurangzeb K, Alhussein M (2018) An efficient demand side management system with a new optimized home energy management controller in smart grid. Energies 11(1):190

Hussin N, Abdullah M, Ali A, Hassan M, Hussin F (2014) Residential electricity time of use (ToU) pricing for Malaysia. Paper presented at the 2014 IEEE conference on energy conversion (CENCON)

Ibrahim O, Bakare MS, Amosa TI, Otuoze AO, Owonikoko WO, Ali EM, Adesina LM, Ogunbiyi O (2023) Development of fuzzy logic-based demand-side energy management system for hybrid energy sources. Energy Convers Manag 10:100354

Ilic M, Black JW, Watz JL (2002) Potential benefits of implementing load control. Paper presented at the 2002 IEEE power engineering society winter meeting. Conference proceedings (Cat. No. 02CH37309)

Imani MH, Niknejad P, Barzegaran M (2018) The impact of customers’ participation level and various incentive values on implementing emergency demand response program in microgrid operation. Int J Electr Power Energy Syst 96:114–125

Iqbal Z, Javaid N, Iqbal S, Aslam S, Khan ZA, Abdul W, Almogren A, Alamri A (2018) A domestic microgrid with optimized home energy management system. Energies 11(4):1002

Issi F, Kaplan O (2018) The determination of load profiles and power consumptions of home appliances. Energies 11(3):607

Jabir HJ, Teh J, Ishak D, Abunima H (2018) Impacts of demand-side management on electrical power systems: a review. Energies 11(5):1050

Jaiswal U, Aggarwal S (2011) Ant colony optimization. Int J Sci Eng Res 2(7):1–7

Javaid N, Ahmed F, Ullah I, Abid S, Abdul W, Alamri A, Almogren AS (2017a) Towards cost and comfort based hybrid optimization for residential load scheduling in a smart grid. Energies 10(10):1546

Javaid N, Javaid S, Abdul W, Ahmed I, Almogren A, Alamri A, Niaz I (2017b) A hybrid genetic wind driven heuristic optimization algorithm for demand side management in smart grid. Energies 10(3):319

Javaid N, Ullah I, Akbar M, Iqbal Z, Khan FA, Alrajeh N, Alabed MS (2017c) An intelligent load management system with renewable energy integration for smart homes. IEEE Access 5:13587–13600

Javaid N, Mohsin SM, Iqbal A, Yasmeen A, Ali I (2018) A hybrid bat-crow search algorithm based home energy management in smart grid. Paper presented at the conference on complex, intelligent, and software intensive systems

Jindal A, Bhambhu BS, Singh M, Kumar N, Naik K (2019) A heuristic-based appliance scheduling scheme for smart homes. IEEE Trans Ind Inform 16(5):3242–3255

Kakran S, Chanana S (2018) Smart operations of smart grids integrated with distributed generation: a review. Renew Sustain Energy Rev 81:524–535

Kannayeram G, Prakash N, Muniraj R (2020) Intelligent hybrid controller for power flow management of PV/battery/FC/SC system in smart grid applications. Int J Hydrogen Energy 45(41):21779–21795

Karaboga N, Cetinkaya B (2004) Performance comparison of genetic and differential evolution algorithms for digital FIR filter design. Paper presented at the International conference on advances in information systems

Keshtkar A, Arzanpour S, Keshtkar F, Ahmadi P (2015) Smart residential load reduction via fuzzy logic, wireless sensors, and smart grid incentives. Energy Build 104:165–180

Khalid A, Javaid N, Mateen A, Khalid B, Khan ZA, Qasim U (2016) Demand side management using hybrid bacterial foraging and genetic algorithm optimization techniques. Paper presented at the 2016 10th international conference on complex, intelligent, and software intensive systems (CISIS)

Khalid A, Javaid N, Guizani M, Alhussein M, Aurangzeb K, Ilahi M (2018) Towards dynamic coordination among home appliances using multi-objective energy optimization for demand side management in smart buildings. IEEE Access 6:19509–19529

Khan ZA, Jayaweera D (2019) Smart meter data based load forecasting and demand side management in distribution networks with embedded PV systems. IEEE Access 8:2631–2644

Khan AA, Razzaq S, Khan A, Khursheed F (2015a) HEMSs and enabled demand response in electricity market: an overview. Renew Sustain Energy Rev 42:773–785

Khan ZA, Ahmed S, Nawaz R, Mahmood A, Razzaq S (2015b) Optimization based individual and cooperative DSM in smart grids: a review. In: 2015b power generation system and renewable energy technologies (PGSRET). pp 1–6

Khan AR, Mahmood A, Safdar A, Khan ZA, Khan NA (2016) Load forecasting, dynamic pricing and DSM in smart grid: a review. Renew Sustain Energy Rev 54:1311–1322

Khan ZA, Zafar A, Javaid S, Aslam S, Rahim MH, Javaid N (2019) Hybrid meta-heuristic optimization based home energy management system in smart grid. J Ambient Intell Humaniz Comput 10(12):4837–4853

Kim J-G, Lee B (2019) Appliance classification by power signal analysis based on multi-feature combination multi-layer LSTM. Energies 12(14):2804

Kim B-G, Zhang Y, Van Der Schaar M, Lee J-W (2015) Dynamic pricing and energy consumption scheduling with reinforcement learning. IEEE Trans Smart Grid 7(5):2187–2198

Kirby BJ (2006) Demand response for power system reliability: FAQ. Citeseer

Küçüker A, Kamal T, Hassan SZ, Li H, Mufti GM, Waseem M (2017) Design and control of photovoltaic/wind/battery based microgrid system. Paper presented at the 2017 international conference on electrical engineering (ICEE)

Kumar A, Rizwan M, Nangia U (2022) A hybrid optimization technique for proficient energy management in smart grid environment. Int J Hydrogen Energy 47(8):5564–5576

Kuo H, Lin C (2013) Cultural evolution algorithm for global optimizations and its applications. J Appl Res Technol 11(4):510–522

Kwon S, Ntaimo L, Gautam N (2018) Demand response in data centers: integration of server provisioning and power procurement. IEEE Trans Smart Grid 10(5):4928–4938

Lee KY, Park J-B (2006) Application of particle swarm optimization to economic dispatch problem: advantages and disadvantages. Paper presented at the 2006 IEEE PES power systems conference and exposition

Leitao J, Gil P, Ribeiro B, Cardoso A (2020) A survey on home energy management. IEEE Access 8:5699–5722

Li Y, Ng BL, Trayer M, Liu L (2012) Automated residential demand response: algorithmic implications of pricing models. IEEE Trans Smart Grid 3(4):1712–1721

Li D, Chiu W-Y, Sun H (2017) Demand side management in microgrid control systems. In: Microgrid. Elsevier, pp 203–230

Chapter   Google Scholar  

Li W, Ng C, Logenthiran T, Phan V-T, Woo WL (2018) Smart Grid Distribution Management System (SGDMS) for optimised electricity bills. J Power Energy Eng 6(08):49

Li Y, Han T, Han B, Zhao H, Wei Z (2019) Whale optimization algorithm with chaos strategy and weight factor. Paper presented at the Journal of Physics: Conference Series

Li Y, Zhu X, Liu J (2020) An improved moth-flame optimization algorithm for engineering problems. Symmetry 12(8):1234

Li L, Qian S, Li Z, Li S (2022) Application of improved satin bowerbird optimizer in image segmentation. Front Plant Sci 1519

Lin Y-H (2018) Design and implementation of an IoT-oriented energy management system based on non-intrusive and self-organizing neuro-fuzzy classification as an electrical energy audit in smart homes. Appl Sci 8(12):2337

Liping L, Ning W, Peijun Z (2018) Modified cuckoo search algorithm with variational parameters and logistic map. MDPI J.

Liu R-S, Hsu Y-F (2018) A scalable and robust approach to demand side management for smart grids with uncertain renewable power generation and bi-directional energy trading. Int J Electr Power Energy Syst 97:396–407

Liu Y, Yuen C, Yu R, Zhang Y, Xie S (2015) Queuing-based energy consumption management for heterogeneous residential demands in smart grid. IEEE Trans Smart Grid 7(3):1650–1659

Liu D, Xu Y, Wei Q, Liu X (2017) Residential energy scheduling for variable weather solar energy based on adaptive dynamic programming. IEEE/CAA J Autom Sin 5(1):36–46

Liu H, Liu B, Li Y, Cui S (2021a) A rotation learning-based colliding bodies optimization algorithm. Paper presented at the Journal of Physics: Conference Series

Liu J, Ji H, Liu Q, Li Y (2021b) A bat optimization algorithm with moderate orientation and perturbation of trend. J Algorithms Comput Technol. https://doi.org/10.1177/17483026211008410

Liu J, Wei X, Huang H (2021c) An improved grey wolf optimization algorithm and its application in path planning. IEEE Access 9:121944–121956

Logenthiran T, Srinivasan D, Shun TZ (2012) Demand side management in smart grid using heuristic optimization. IEEE Trans Smart Grid 3(3):1244–1252

Logenthiran T, Srinivasan D, Phyu E (2015) Particle swarm optimization for demand side management in smart grid. Paper presented at the 2015 IEEE Innovative Smart Grid Technologies-Asia (ISGT ASIA)

Lokeshgupta B, Sivasubramani S (2019) Multi-objective home energy management with battery energy storage systems. Sustain Cities Soc 47:101458

Lowell J, Yoshimura H (2011) ISO New England: results of ancillary service pilot programs, alternative technology regulation pilot program and demand response reserves pilot program. ISO New England, Holyoke

Lu X, Zhou K, Chan FT, Yang S (2017) Optimal scheduling of household appliances for smart home energy management considering demand response. Nat Hazards 88(3):1639–1653

Lu X, Zhou K, Zhang X, Yang S (2018) A systematic review of supply and demand side optimal load scheduling in a smart grid environment. J Clean Prod 203:757–768

Luo F, Dong ZY, Xu Z, Kong W, Wang F (2018) Distributed residential energy resource scheduling with renewable uncertainties. IET Gener Transm Distrib 12(11):2770–2777

Luque-Chang A, Cuevas E, Fausto F, Zaldívar D, Pérez M (2018) Social spider optimization algorithm: modifications, applications, and perspectives. Math Probl Eng. https://doi.org/10.1155/2018/6843923

Macedo M, Galo J, De Almeida L, de C. Lima A (2015) Demand side management using artificial neural networks in a smart grid environment. Renew Sustain Energy Rev 41:128–133

Maharjan S, Zhang Y, Gjessing S, Tsang DH (2014) User-centric demand response management in the smart grid with multiple providers. IEEE Trans Emerg Top Comput 5(4):494–505

Mahmood D, Javaid N, Alrajeh N, Khan Z, Qasim U, Ahmed I, Ilahi M (2016) Realistic scheduling mechanism for smart homes. Energies 9(3):202

Makhadmeh SN, Khader AT, Al-Betar MA, Naim S (2018) An optimal power scheduling for smart home appliances with smart battery using grey wolf optimizer. Paper presented at the 2018 8th IEEE international conference on control system, computing and engineering (ICCSCE)

Manzoor A, Javaid N, Ullah I, Abdul W, Almogren A, Alamri A (2017) An intelligent hybrid heuristic scheme for smart metering based demand side management in smart homes. Energies 10(9):1258

Martínez-Lao J, Montoya FG, Montoya MG, Manzano-Agugliaro F (2017) Electric vehicles in Spain: an overview of charging systems. Renew Sustain Energy Rev 77:970–983

Martirano L, Parise G, Greco G, Manganelli M, Massarella F, Cianfrini M, Parise L, di Laura FP, Habib E (2018) Aggregation of users in a residential/commercial building managed by a building energy management system (BEMS). IEEE Trans Ind Appl 55(1):26–34

Mekhilef S, Saidur R, Kamalisarvestani M (2012) Effect of dust, humidity and air velocity on efficiency of photovoltaic cells. Renew Sustain Energy Rev 16(5):2920–2925

Menos-Aikateriniadis C, Lamprinos I, Georgilakis PS (2022) Particle swarm optimization in residential demand-side management: a review on scheduling and control algorithms for demand response provision. Energies 15(6):2211

Merabet A, Ahmed KT, Ibrahim H, Beguenane R, Ghias AM (2016) Energy management and control system for laboratory scale microgrid based wind-PV-battery. IEEE Trans Sustain Energy 8(1):145–154

Meyabadi AF, Deihimi MH (2017) A review of demand-side management: reconsidering theoretical framework. Renew Sustain Energy Rev 80:367–379

Ming Z, Song X, Mingjuan M, Lingyun L, Min C, Yuejin W (2013) Historical review of demand side management in China: management content, operation mode, results assessment and relative incentives. Renew Sustain Energy Rev 25:470–482

Ming Z, Li S, Yanying H (2015) Status, challenges and countermeasures of demand-side management development in China. Renew Sustain Energy Rev 47:284–294

Mirkhan A, Celebi N (2022) Binary representation of polar bear algorithm for feature selection. Comput Syst Sci Eng 43(2):767–783

Misaghi M, Yaghoobi M (2019) Improved invasive weed optimization algorithm (IWO) based on chaos theory for optimal design of PID controller. J Comput Des Eng 6(3):284–295

Mitras BA, Sultan JA (2013) A novel hybrid imperialist competitive algorithm for global optimization. Aust J Basic Appl Sci 7:330–341

Møller Andersen F, Grenaa Jensen S, Larsen HV, Meibom P, Ravn H, Skytte K, Togeby M (2006) Analyses of demand response in Denmark. Risoe National Lab.

Moon S, Lee J-W (2016) Multi-residential demand response scheduling with multi-class appliances in smart grid. IEEE Trans Smart Grid 9(4):2518–2528

Moreno Escobar JJ, Morales Matamoros O, Tejeida Padilla R, Lina Reyes I, Quintana Espinosa H (2021) A comprehensive review on smart grids: challenges and opportunities. Sensors 21(21):6978

Morgan MG, Talukdar SN (1979) Electric power load management: some technical, economic, regulatory and social issues. Proc IEEE 67(2):241–312

Mou Y, Xing H, Lin Z, Fu M (2014) Decentralized optimal demand-side management for PHEV charging in a smart grid. IEEE Trans Smart Grid 6(2):726–736

Muhamediyeva D (2020) Fuzzy cultural algorithm for solving optimization problems. Paper presented at the Journal of Physics: Conference Series

Muratori M, Schuelke-Leech B-A, Rizzoni G (2014) Role of residential demand response in modern electricity markets. Renew Sustain Energy Rev 33:546–553

Murphy KP (2012) Machine learning: a probabilistic perspective. MIT Press, Cambridge

Nadimi-Shahraki MH, Fatahi A, Zamani H, Mirjalili S, Abualigah L (2021) An improved moth-flame optimization algorithm with adaptation mechanism to solve numerical and mechanical engineering problems. Entropy 23(12):1637

Nawaz A, Hafeez G, Khan I, Jan KU, Li H, Khan SA, Wadud Z (2020) An intelligent integrated approach for efficient demand side management with forecaster and advanced metering infrastructure frameworks in smart grid. IEEE Access 8:132551–132581

Nguyen HT, Nguyen DT, Le LB (2014) Energy management for households with solar assisted thermal load considering renewable energy and price uncertainty. IEEE Trans Smart Grid 6(1):301–314

Nolan S, O’Malley M (2015) Challenges and barriers to demand response deployment and evaluation. Appl Energy 152:1–10

Noor S, Guo M, van Dam KH, Shah N, Wang X (2018) Energy demand side management with supply constraints: game theoretic approach. Energy Procedia 145:368–373

O’Neill D, Levorato M, Goldsmith A, Mitra U (2010) Residential demand response using reinforcement learning. Paper presented at the 2010 First IEEE international conference on smart grid communications

Oskouei MZ, Mohammadi-Ivatloo B, Abapour M, Ahmadian A, Piran MJ (2020) A novel economic structure to improve the energy label in smart residential buildings under energy efficiency programs. J Clean Prod 260:121059

Oskouei MZ, Şeker AA, Tunçel S, Demirbaş E, Gözel T, Hocaoğlu MH, Abapour M, Mohammadi-Ivatloo B (2022) A critical review on the impacts of energy storage systems and demand-side management strategies in the economic operation of renewable-based distribution network. Sustainability 14(4):2110

Page MJ, Moher D (2017) Evaluations of the uptake and impact of the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) Statement and extensions: a scoping review. Syst Rev 6(1):1–14

Pal S, Kumar R (2016) Price prediction techniques for residential demand response using support vector regression. Paper presented at the 2016 IEEE 7th power India international conference (PIICON)

Pan J, Jain R, Paul S (2014) A survey of energy efficiency in buildings and microgrids using networking technologies. IEEE Commun Surv Tutor 16(3):1709–1731

Panapakidis IP, Papadopoulos TA, Christoforidis GC, Papagiannis GK (2014) Pattern recognition algorithms for electricity load curve analysis of buildings. Energy Build 73:137–145

Panda S, Mohanty S, Rout PK, Sahu BK, Bajaj M, Zawbaa HM, Kamel S (2022) Residential Demand Side Management model, optimization and future perspective: a review. Energy Rep 8:3727–3766

Panwar LK, Konda SR, Verma A, Panigrahi BK, Kumar R (2017) Operation window constrained strategic energy management of microgrid with electric vehicle and distributed resources. IET Gener Transm Distrib 11(3):615–626

Pascual J, Barricarte J, Sanchis P, Marroyo L (2015) Energy management strategy for a renewable-based residential microgrid with generation and demand forecasting. Appl Energy 158:12–25

Paterakis NG, Erdinç O, Pappi IN, Bakirtzis AG, Catalão JP (2016) Coordinated operation of a neighborhood of smart households comprising electric vehicles, energy storage and distributed generation. IEEE Trans Smart Grid 7(6):2736–2747

Patyn C, Ruelens F, Deconinck G (2018) Comparing neural architectures for demand response through model-free reinforcement learning for heat pump control. Paper presented at the 2018 IEEE international energy conference (ENERGYCON)

Paudyal P, Ni Z (2019) Smart home energy optimization with incentives compensation from inconvenience for shifting electric appliances. Int J Electr Power Energy Syst 109:652–660

Pedrasa MAA, Spooner TD, MacGill IF (2010) Coordinated scheduling of residential distributed energy resources to optimize smart home energy services. IEEE Trans Smart Grid 1(2):134–143

Phuangpornpitak N, Tia S (2013) Opportunities and challenges of integrating renewable energy in smart grid system. Energy Procedia 34:282–290

Piette MA, Sezgen O, Watson DS, Motegi N, Shockman C, ten Hope L (2004) Development and evaluation of fully automated demand response in large facilities

Piette M, Sezgen O, Watson D, Motegi N, Shockman C, ten Hope L (2005) Development and evaluation of fully automated demand response in large facilities, California Energy Commission: CEC-500-2005-013, Jan

Pilloni V, Floris A, Meloni A, Atzori L (2016) Smart home energy management including renewable sources: a qoe-driven approach. IEEE Trans Smart Grid 9(3):2006–2018

Platt G, Paevere P, Higgins A, Grozev G (2014) Electric vehicles: new problem or distributed energy asset? In: Distributed generation and its implications for the utility industry. Elsevier, Amsterdam, pp 335–355

Praveena M, Jaiganesh V (2017) A literature review on supervised machine learning algorithms and boosting process. Int J Comput Appl 169(8):32–35

Priya Esther B, Shivarama Krishna K, Sathish Kumar K, Ravi K (2016) Demand side management using bacterial foraging optimization algorithm. In: Information systems design and intelligent applications. Springer, pp 657–666

Proedrou E (2021) A comprehensive review of residential electricity load profile models. IEEE Access 9:12114–12133

Puente C, Palacios R, González-Arechavala Y, Sánchez-Úbeda EF (2020) Non-intrusive load monitoring (NILM) for energy disaggregation using soft computing techniques. Energies 13(12):3117

Quiggin D, Cornell S, Tierney M, Buswell R (2012) A simulation and optimisation study: towards a decentralised microgrid, using real world fluctuation data. Energy 41(1):549–559

Qureshi WA, Nair N-KC, Farid MM (2021) Impact of energy storage in buildings on electricity demand side management. In: Thermal energy storage with phase change materials. CRC Press, Boca Raton, pp 176–197

Rahim S, Iqbal Z, Shaheen N, Khan ZA, Qasim U, Khan SA, Javaid N (2016a) Ant colony optimization based energy management controller for smart grid. Paper presented at the 2016a IEEE 30th international conference on advanced information networking and applications (AINA)

Rahim S, Javaid N, Ahmad A, Khan SA, Khan ZA, Alrajeh N, Qasim U (2016b) Exploiting heuristic algorithms to efficiently utilize energy management controllers with renewable energy sources. Energy Build 129:452–470

Rahim MH, Khalid A, Javaid N, Alhussein M, Aurangzeb K, Khan ZA (2018) Energy efficient smart buildings using coordination among appliances generating large data. IEEE Access 6:34670–34690

Rahman SM, Miah MD (2017) The impact of sources of energy production on globalization: evidence from panel data analysis. Renew Sustain Energy Rev 74:110–115

Rahman I, Vasant PM, Singh BSM, Abdullah-Al-Wadud M (2016) On the performance of accelerated particle swarm optimization for charging plug-in hybrid electric vehicles. Alex Eng J 55(1):419–426

Article   MATH   Google Scholar  

Rahman MM, Arefi A, Shafiullah G, Hettiwatte S (2018) A new approach to voltage management in unbalanced low voltage networks using demand response and OLTC considering consumer preference. Int J Electr Power Energy Syst 99:11–27

Rajendhar P, Jeyaraj BE (2019) Application of DR and co-simulation approach for renewable integrated HEMS: a review. IET Gener Transm Distrib 13(16):3501–3512

Rehman AU, Wadud Z, Elavarasan RM, Hafeez G, Khan I, Shafiq Z, Alhelou HH (2021) An optimal power usage scheduling in smart grid integrated with renewable energy sources for energy management. IEEE Access 9:84619–84638

Renner S, Albu M, van Elburg H, Heinemann C, Lazicki A, Penttinen L, Puente F, Saele H (2011) European smart metering landscape report. Imprint 2:1–168

Roy C, Das DK (2021) A hybrid genetic algorithm (GA)–particle swarm optimization (PSO) algorithm for demand side management in smart grid considering wind power for cost optimization. Sādhanā 46(2):1–12

Ruelens F, Claessens BJ, Vandael S, Iacovella S, Vingerhoets P, Belmans R (2014) Demand response of a heterogeneous cluster of electric water heaters using batch reinforcement learning. Paper presented at the 2014 power systems computation conference

Saad W, Han Z, Poor HV, Basar T (2012) Game-theoretic methods for the smart grid: an overview of microgrid systems, demand-side management, and smart grid communications. IEEE Signal Process Mag 29(5):86–105

Sæle H, Grande OS (2011) Demand response from household customers: experiences from a pilot study in Norway. IEEE Trans Smart Grid 2(1):102–109

Safdarian A, Fotuhi-Firuzabad M, Lehtonen M (2015) Optimal residential load management in smart grids: a decentralized framework. IEEE Trans Smart Grid 7(4):1836–1845

Sala-Cardoso E, Delgado-Prieto M, Kampouropoulos K, Romeral L (2018) Activity-aware HVAC power demand forecasting. Energy Build 170:15–24

Samad T, Koch E, Stluka P (2016) Automated demand response for smart buildings and microgrids: the state of the practice and research challenges. Proc IEEE 104(4):726–744

Samadi P, Mohsenian-Rad H, Schober R, Wong VW (2012) Advanced demand side management for the future smart grid using mechanism design. IEEE Trans Smart Grid 3(3):1170–1180

Samadi P, Mohsenian-Rad H, Wong VW, Schober R (2014) Real-time pricing for demand response based on stochastic approximation. IEEE Trans Smart Grid 5(2):789–798

Samsuddin S, Othman MS, Yusuf LM (2018) A review of single and population-based metaheuristic algorithms solving multi depot vehicle routing problem. Int J Softw Eng Comput Syst 4(2):80–93

Sarker MR, Ortega-Vazquez MA, Kirschen DS (2014) Optimal coordination and scheduling of demand response via monetary incentives. IEEE Trans Smart Grid 6(3):1341–1352

Sarker E, Halder P, Seyedmahmoudian M, Jamei E, Horan B, Mekhilef S, Stojcevski A (2021) Progress on the demand side management in smart grid and optimization approaches. Int J Energy Res 45(1):36–64

Sarkis-Onofre R, Catalá-López F, Aromataris E, Lockwood C (2021) How to properly use the PRISMA statement. Syst Rev 10(1):1–3

Schwartz J (2012) Salt River Project: the persistence of consumer choice. Association for Demand Response and Smart Grid, June 15

Sehar F, Pipattanasomporn M, Rahman S (2017) Integrated automation for optimal demand management in commercial buildings considering occupant comfort. Sustain Cities Soc 28:16–29

Shafie-Khah M, Siano P (2017) A stochastic home energy management system considering satisfaction cost and response fatigue. IEEE Trans Ind Inform 14(2):629–638

Shafie-Khah M, Siano P, Aghaei J, Masoum MA, Li F, Catalão JP (2019) Comprehensive review of the recent advances in industrial and commercial DR. IEEE Trans Ind Inform 15(7):3757–3771

Sharda S, Singh M, Sharma K (2021) Demand side management through load shifting in IoT based HEMS: overview, challenges and opportunities. Sustain Cities Soc 65:102517

Shareef H, Ahmed MS, Mohamed A, Al Hassan E (2018) Review on home energy management system considering demand responses, smart technologies, and intelligent controllers. IEEE Access 6:24498–24509

Sharifi R, Fathi S, Vahidinasab V (2017) A review on demand-side tools in electricity market. Renew Sustain Energy Rev 72:565–572

Shen J, Jiang C, Liu Y, Qian J (2016) A microgrid energy management system with demand response for providing grid peak shaving. Electr Power Compon Syst 44(8):843–852

Shewale A, Mokhade A, Funde N, Bokde ND (2020) An overview of demand response in smart grid and optimization techniques for efficient residential appliance scheduling problem. Energies 13(16):4266

Shoreh MH, Siano P, Shafie-khah M, Loia V, Catalão JP (2016) A survey of industrial applications of Demand Response. Electr Power Syst Res 141:31–49

Shuja SM, Javaid N, Khan S, Akmal H, Hanif M, Fazalullah Q, Khan ZA (2019) Efficient scheduling of smart home appliances for energy management by cost and PAR optimization algorithm in smart grid. Paper presented at the workshops of the international conference on advanced information networking and applications

Simmhan Y, Aman S, Kumbhare A, Liu R, Stevens S, Zhou Q, Prasanna V (2013) Cloud-based software platform for big data analytics in smart grids. Comput Sci Eng 15(4):38–47

Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702–713

Songyang L, Haipeng Y, Miao W (2022) Cat swarm optimization algorithm based on the information interaction of subgroup and the top-N learning strategy. J Intell Syst 31(1):489–500

Sou KC, Weimer J, Sandberg H, Johansson KH (2011) Scheduling smart home appliances using mixed integer linear programming. Paper presented at the 2011 50th IEEE conference on decision and control and European control conference

Suruli K, Ila V (2020) Social spider optimization algorithm-based optimized power management schemes. Electr Power Compon Syst 48(11):1111–1124

Tamilarasi K, Gogulkumar M, Velusamy K (2021) Enhancing the performance of social spider optimization with neighbourhood attraction algorithm. Paper presented at the Journal of Physics: Conference Series.

Tamilarasu K, Sathiasamuel CR, Joseph JDN, Madurai Elavarasan R, Mihet-Popa L (2021) Reinforced demand side management for educational institution with incorporation of user’s comfort. Energies 14(10):2855

Tang W-J, Wu Y-S, Yang H-T (2018) Adaptive segmentation and machine learning based potential DR capacity analysis. Paper presented at the 2018 18th international conference on harmonics and quality of power (ICHQP)

Tariq M, Khalid A, Ahmad I, Khan M, Zaheer B, Javaid N (2017) Load scheduling in home energy management system using meta-heuristic techniques and critical peak pricing tariff. Paper presented at the international conference on P2P, parallel, grid, cloud and internet computing

Tasdighi M, Ghasemi H, Rahimi-Kian A (2013) Residential microgrid scheduling based on smart meters data and temperature dependent thermal load modeling. IEEE Trans Smart Grid 5(1):349–357

Teng R, Yamazaki T (2018) Load profile-based coordination of appliances in a smart home. IEEE Trans Consum Electron 65(1):38–46

Tian Z, Zhang C (2018) An improved harmony search algorithm and its application in function optimization. J Inf Process Syst 14(5):1237–1253

Torriti J (2012) Price-based demand side management: assessing the impacts of time-of-use tariffs on residential electricity demand and peak shifting in Northern Italy. Energy 44(1):576–583

Tsipianitis AD, Tsompanakis Y. Solving engineering optimization problems with an efficient hybrid nature-inspired algorithm.

Tushar MHK, Assi C, Maier M, Uddin MF (2014) Smart microgrids: optimal joint scheduling for electric vehicles and home appliances. IEEE Trans Smart Grid 5(1):239–250

Vardakas JS, Zorba N, Verikoukis CV (2014) A survey on demand response programs in smart grids: pricing methods and optimization algorithms. IEEE Commun Surv Tutor 17(1):152–178

Vázquez-Canteli JR, Nagy Z (2019) Reinforcement learning for demand response: a review of algorithms and modeling techniques. Appl Energy 235:1072–1089

Vivekananthan C, Mishra Y, Ledwich G, Li F (2014) Demand response for residential appliances via customer reward scheme. IEEE Trans Smart Grid 5(2):809–820

Wang G, Lin Z (2017) Hybrid mosquito host-seeking algorithm for multi-depot vehicle routing problem. Paper presented at the 2017 9th international conference on measuring technology and mechatronics automation (ICMTMA)

Wang M, Lu G (2021) A modified sine cosine algorithm for solving optimization problems. IEEE Access 9:27434–27450

Wang Z, Paranjape R (2015) Optimal residential demand response for multiple heterogeneous homes with real-time price prediction in a multiagent framework. IEEE Trans Smart Grid 8(3):1173–1184

Wang J, Sun Z, Zhou Y, Dai J (2012) Optimal dispatching model of smart home energy management system. Paper presented at the IEEE PES innovative smart grid technologies

Wang Q, Zhang C, Ding Y, Xydis G, Wang J, Østergaard J (2015) Review of real-time electricity markets for integrating distributed energy resources and demand response. Appl Energy 138:695–706

Wang Y, Yang Z, Mourshed M, Guo Y, Niu Q, Zhu X (2019) Demand side management of plug-in electric vehicles and coordinated unit commitment: a novel parallel competitive swarm optimization method. Energy Convers Manag 196:935–949

Warren P (2014) A review of demand-side management policy in the UK. Renew Sustain Energy Rev 29:941–951

Wen Z, O’Neill D, Maei H (2015) Optimal demand response using device-based reinforcement learning. IEEE Trans Smart Grid 6(5):2312–2324

Weng Y, Rajagopal R (2015) Probabilistic baseline estimation via gaussian process. Paper presented at the 2015 IEEE power & energy society general meeting

Weng Y, Yu J, Rajagopal R (2018) Probabilistic baseline estimation based on load patterns for better residential customer rewards. Int J Electr Power Energy Syst 100:508–516

Wong MC-H (1991) Market-based systems of monetary control in developing countries: operating procedures and related issues

Worthmann K, Kellett CM, Braun P, Grüne L, Weller SR (2015) Distributed and decentralized control of residential energy systems incorporating battery storage. IEEE Trans Smart Grid 6(4):1914–1923

Wu Z, Xia X (2017) A portfolio approach of demand side management. IFAC-PapersOnLine 50(1):171–176

Wu Z, Tazvinga H, Xia X (2015) Demand side management of photovoltaic-battery hybrid system. Appl Energy 148:294–304

Wu Y, Ravey A, Chrenko D, Miraoui A (2019) Demand side energy management of EV charging stations by approximate dynamic programming. Energy Convers Manag 196:878–890

Xu B, Oudalov A, Ulbig A, Andersson G, Kirschen DS (2016a) Modeling of lithium-ion battery degradation for cell life assessment. IEEE Trans Smart Grid 9(2):1131–1140

Xu G, Yu W, Griffith D, Golmie N, Moulema P (2016b) Toward integrating distributed energy resources and storage devices in smart grid. IEEE Internet Things J 4(1):192–204

Yan X, Ozturk Y, Hu Z, Song Y (2018) A review on price-driven residential demand response. Renew Sustain Energy Rev 96:411–419

Yang X-S, He X (2013) Bat algorithm: literature review and applications. Int J Bio-Inspir Comput 5(3):141–149

Yang H-T, Yang C-T, Tsai C-C, Chen G-J, Chen S-Y (2015) Improved PSO based home energy management systems integrated with demand response in a smart grid. Paper presented at the 2015 IEEE congress on evolutionary computation (CEC)

Yang H, Zhang J, Qiu J, Zhang S, Lai M, Dong ZY (2016) A practical pricing approach to smart grid demand response based on load classification. IEEE Trans Smart Grid 9(1):179–190

Yang C, Cheng Q, Lai P, Liu J, Guo H (2018) Data-driven modeling for energy consumption estimation. In: Exergy for a better environment and improved sustainability, vol 2. Springer, pp 1057–1068

Yao E, Samadi P, Wong VW, Schober R (2015) Residential demand side management under high penetration of rooftop photovoltaic units. IEEE Trans Smart Grid 7(3):1597–1608

Yilmaz S, Weber S, Patel M (2019) Who is sensitive to DSM? Understanding the determinants of the shape of electricity load curves and demand shifting: socio-demographic characteristics, appliance use and attitudes. Energy Policy 133:110909

Yilmaz S, Rinaldi A, Patel MK (2020) DSM interactions: what is the impact of appliance energy efficiency measures on the demand response (peak load management)? Energy Policy 139:111323

Yoon JH, Baldick R, Novoselac A (2014a) Dynamic demand response controller based on real-time retail price for residential buildings. IEEE Trans Smart Grid 5(1):121–129

Yoon JH, Bladick R, Novoselac A (2014b) Demand response for residential buildings based on dynamic price of electricity. Energy Build 80:531–541

Yuce B, Packianather MS, Mastrocinque E, Pham DT, Lambiase A (2013) Honey bees inspired optimization method: the bees algorithm. Insects 4(4):646–662

Zeng S, Li J, Ren Y (2008) Research of time-of-use electricity pricing models in China: a survey. Paper presented at the 2008 IEEE international conference on industrial engineering and engineering management

Zenned S, Chaouali H, Mami A (2017) Fuzzy logic energy management strategy of a hybrid renewable energy system feeding a typical Tunisian house. Int J Adv Comput Sci Appl 8(12):206–212

Zhang Q, Grossmann IE (2016) Enterprise-wide optimization for industrial demand side management: fundamentals, advances, and perspectives. Chem Eng Res Des 116:114–131

Zhang Y-J, Peng H-R (2017) Exploring the direct rebound effect of residential electricity consumption: an empirical study in China. Appl Energy 196:132–141

Zhang L, Zhong Y, Li P (2004) Applications of artificial immune sysetms in remote sensing image classification.

Zhang D, Shah N, Papageorgiou LG (2013) Efficient energy consumption and operation management in a smart building with microgrid. Energy Convers Manag 74:209–222

Zhang L, Liu L, Yang X-S, Dai Y (2016) A novel hybrid firefly algorithm for global optimization. PLoS ONE 11(9):e0163230

Zhang X, Lu R, Jiang J, Hong SH, Song WS (2021) Testbed implementation of reinforcement learning-based demand response energy management system. Appl Energy 297:117131

Zhao J, Wen F, Dong ZY, Xue Y, Wong KP (2012) Optimal dispatch of electric vehicles and wind power using enhanced particle swarm optimization. IEEE Trans Ind Inform 8(4):889–899

Zhao Z, Lee WC, Shin Y, Song K-B (2013) An optimal power scheduling method for demand response in home energy management system. IEEE Trans Smart Grid 4(3):1391–1400

Zhao J, Lv L, Sun H (2015) Artificial bee colony using opposition-based learning. In: Genetic and evolutionary computing. Springer, pp 3–10

Zhou K, Yang S (2015) Demand side management in China: the context of China’s power industry reform. Renew Sustain Energy Rev 47:954–965

Zhou D, Balandat M, Tomlin C (2016) Residential demand response targeting using machine learning with observational data. Paper presented at the 2016 IEEE 55th conference on decision and control (CDC)

Zhu Z, Tang J, Lambotharan S, Chin WH, Fan Z (2012) An integer linear programming based optimization for home demand-side management in smart grid. Paper presented at the 2012 IEEE PES innovative smart grid technologies (ISGT)

Download references

Not applicable.

Author information

Authors and affiliations.

Department of Electrical Engineering, Kampala International University, 20000, Ishaka, Uganda

Mutiu Shola Bakare, Mohammad Zeeshan & Aliyu Nuhu Shuaibu

Department of Electrical Engineering, Faculty of Engineering, Ahmadu Bello University, Zaria, Nigeria

Abubakar Abdulkarim

You can also search for this author in PubMed   Google Scholar

Contributions

All authors listed have significantly contributed to the development and writing of this manuscript. MSB conceptualized the idea, AA, MZ, and ANS supervise the research, MSB, AA and MZ revised and edited the final manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Mutiu Shola Bakare .

Ethics declarations

Ethics approval and consent to participate, consent for publication, competing interests.

The authors declared that they have no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Bakare, M.S., Abdulkarim, A., Zeeshan, M. et al. A comprehensive overview on demand side energy management towards smart grids: challenges, solutions, and future direction. Energy Inform 6 , 4 (2023). https://doi.org/10.1186/s42162-023-00262-7

Download citation

Received : 06 February 2023

Accepted : 05 March 2023

Published : 13 March 2023

DOI : https://doi.org/10.1186/s42162-023-00262-7

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Machine language

Future Prospects and Challenges of Renewable Energy: A Case Study of Nepal

Ieee account.

• Change Username/Password
• Update Address

Purchase Details

• Payment Options
• Order History
• View Purchased Documents

Profile Information

• Communications Preferences
• Profession and Education
• Technical Interests
• US & Canada: +1 800 678 4333
• Worldwide: +1 732 981 0060
• Contact & Support
• About IEEE Xplore
• Accessibility
• Terms of Use
• Nondiscrimination Policy
• Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2024 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• v.45(Suppl 1); 2016 Jan

Nuclear power in the 21st century: Challenges and possibilities

Akos horvath.

MTA Centre for Energy Research, KFKI Campus, P.O.B. 49, Budapest 114, 1525 Hungary

Elisabeth Rachlew

Department of Physics, Royal Institute of Technology, KTH, 10691 Stockholm, Sweden

The current situation and possible future developments for nuclear power—including fission and fusion processes—is presented. The fission nuclear power continues to be an essential part of the low-carbon electricity generation in the world for decades to come. There are breakthrough possibilities in the development of new generation nuclear reactors where the life-time of the nuclear waste can be reduced to some hundreds of years instead of the present time-scales of hundred thousand of years. Research on the fourth generation reactors is needed for the realisation of this development. For the fast nuclear reactors, a substantial research and development effort is required in many fields—from material sciences to safety demonstration—to attain the envisaged goals. Fusion provides a long-term vision for an efficient energy production. The fusion option for a nuclear reactor for efficient production of electricity has been set out in a focussed European programme including the international project of ITER after which a fusion electricity DEMO reactor is envisaged.

Introduction

All countries have a common interest in securing sustainable, low-cost energy supplies with minimal impact on the environment; therefore, many consider nuclear energy as part of their energy mix in fulfilling policy objectives. The discussion of the role of nuclear energy is especially topical for industrialised countries wishing to reduce carbon emissions below the current levels. The latest report from IPCC WGIII ( 2014 ) (see Box 1 for explanations of all acronyms in the article) says: “Nuclear energy is a mature low-GHG emission source of base load power, but its share of global electricity has been declining since 1993. Nuclear energy could make an increasing contribution to low-carbon energy supply, but a variety of barriers and risks exist ”.

Demand for electricity is likely to increase significantly in the future, as current fossil fuel uses are being substituted by processes using electricity. For example, the transport sector is likely to rely increasingly on electricity, whether in the form of fully electric or hybrid vehicles, either using battery power or synthetic hydrocarbon fuels. Here, nuclear power can also contribute, via generation of either electricity or process heat for the production of hydrogen or other fuels.

In Europe, in particular, the public opinion about safety and regulations with nuclear power has introduced much critical discussions about the continuation of nuclear power, and Germany has introduced the “Energiewende” with the goal to close all their nuclear power by 2022. The contribution of nuclear power to the electricity production in the different countries in Europe differs widely with some countries having zero contribution (e.g. Italy, Lithuania) and some with the major part comprising nuclear power (e.g. France, Hungary, Belgium, Slovakia, Sweden).

Current status

The use of nuclear energy for commercial electricity production began in the mid-1950s. In 2013, the world’s 392 GW of installed nuclear capacity accounted for 11 % of electricity generation produced by around 440 nuclear power plants situated in 30 countries (Fig.  1 ). This share has declined gradually since 1996, when it reached almost 18 %, as the rate of new nuclear additions (and generation) has been outpaced by the expansion of other technologies. After hydropower, nuclear is the world’s second-largest source of low-carbon electricity generation (IEA 2014 1 ).

Total number of operating nuclear reactors worldwide. The total number of reactors also include six in Taiwan (source: IAEA 2015) ( https://www.iaea.org/newscenter/focus/nuclear-power )

The Country Nuclear Power Profiles (CNPP 2 ) compiles background information on the status and development of nuclear power programmes in member states. The CNPP’s main objectives are to consolidate information about the nuclear power infrastructures in participating countries, and to present factors related to the effective planning, decision-making and implementation of nuclear power programmes that together lead to safe and economical operations of nuclear power plants.

Within the European Union, 27 % of electricity production (13 % of primary energy) is obtained from 132 nuclear power plants in January 2015 (Fig.  1 ). Across the world, 65 new reactors are under construction, mainly in Asia (China, South Korea, India), and also in Russia, Slovakia, France and Finland. Many other new reactors are in the planning stage, including for example, 12 in the UK.

Apart from one first Generation “Magnox” reactor still operating in the UK, the remainder of the operating fleet is of the second or third Generation type (Fig.  2 ). The predominant technology is the Light Water Reactor (LWR) developed originally in the United States by Westinghouse and then exploited massively by France and others in the 1970s as a response to the 1973 oil crisis. The UK followed a different path and pursued the Advanced Gas-cooled Reactor (AGR). Some countries (France, UK, Russia, Japan) built demonstration scale fast neutron reactors in the 1960s and 70s, but the only commercial reactor of this type currently operating is in Russia.

Nuclear reactor generations from the pioneering age to the next decade (reproduced with permission from Ricotti 2013 )

Future evolution

The fourth Generation reactors, offering the potential of much higher energy recovery and reduced volumes of radioactive waste, are under study in the framework of the “Generation IV International Forum” (GIF) 3 and the “International Project on Innovative Nuclear Reactors and Fuel Cycles” (INPRO). The European Commission in 2010 launched the European Sustainable Nuclear Industrial Initiative (ESNII), which will support three Generation IV fast reactor projects as part of the EU’s plan to promote low-carbon energy technologies. Other initiatives supporting biomass, wind, solar, electricity grids and carbon sequestration are in parallel. ESNII will take forward: the Astrid sodium-cooled fast reactor (SFR) proposed by France, the Allegro gas-cooled fast reactor (GFR) supported by central and eastern Europe and the MYRRHA lead- cooled fast reactor (LFR) technology pilot proposed by Belgium.

The generation of nuclear energy from uranium produces not only electricity but also spent fuel and high-level radioactive waste (HLW) as a by-product. For this HLW, a technical and socially acceptable solution is necessary. The time scale needed for the radiotoxicity of the spent fuel to drop to the level of natural uranium is very long (i.e. of the order of 200 000–300 000 years). The preferred solution for disposing of spent fuel or the HLW resulting from classical reprocessing is deep geological storage. Whilst there are no such geological repositories operating yet in the world, Sweden, Finland and France are on track to have such facilities ready by 2025 (Kautsky et al. 2013 ). In this context it should also be mentioned that it is only for a minor fraction of the HLW that recycling and transmutation is required since adequate separation techniques of the fuel can be recycled and again fed through the LWR system.

The “Strategic Energy Technology Plan” (SET-Plan) identifies fission energy as one of the contributors to the 2050 objectives of a low-carbon energy mix, relying on the Generation-3 reactors, closed fuel cycle and the start of implementation of Generation IV reactors making nuclear energy more sustainable. The EU Energy Roadmap 2050 provides decarbonisation scenarios with different assumptions from the nuclear perspective: two scenarios contemplate a nuclear phase-out by 2050, whilst three others consider that 15–20 % of electricity will be produced by nuclear energy. If by 2050 a generation capacity of 20 % nuclear electricity (140 GWe) is to be secured, 100–120 nuclear power units will have to be built between now and 2050, the precise number depending on the power rating (Garbil and Goethem 2013 ).

Despite the regional differences in the development plans, the main questions are of common interest to all countries, and require solutions in order to maintain nuclear power in the power mix of contributing to sustainable economic growth. The questions include (i) maintaining safe operation of the nuclear plants, (ii) securing the fuel supplies, (iii) a strategy for the management of radioactive waste and spent nuclear fuel.

Safety and non-proliferation risks are managed in accordance with the international rules issued both by IAEA and EURATOM in the EU. The nuclear countries have signed the corresponding agreements and the majority of them have created the necessary legal and regulatory structure (Nuclear Safety Authority). As regards radioactive wastes, particularly high-level wastes (HLW) and spent fuel (SF) most of the countries have long-term policies. The establishment of new nuclear units and the associated nuclear technology developments offer new perspectives, which may need reconsideration of fuel cycle policies and more active regional and global co-operation.

Open and closed fuel cycle

In the frame of the open fuel cycle, the spent fuel will be taken to final disposal without recycling. Deep geological repositories are the only available option for isolating the highly radioactive materials for a very long time from the biosphere. Long-term (80–100 years) near soil intermediate storages are realised in e.g. France and the Netherlands which will allow for permanent access and inspection. The main advantage of the open fuel cycle is its simplicity. The spent fuel assemblies are first stored in interim storage for several years or decades, then they will be placed in special containers and moved into deep underground storage facilities. The technology for producing such containers and for excavation of the underground system of tunnels exists today (Hózer et al. 2010 ; Kautsky et al. 2013 ).

The European Academies Science Advisory Board recently released the report on “Management of spent nuclear fuel and its waste” (EASAC 2014 ). The report discusses the challenges associated with different strategies to manage spent nuclear fuel, in respect of both open cycles and steps towards closing the nuclear fuel cycle. It integrates the conclusions on the issues raised on sustainability, safety, non-proliferation and security, economics, public involvement and on the decision-making process. Recently Vandenbosch et al. ( 2015 ) critically discussed the issue of confidence in the indefinite storage of nuclear waste. One complication of the nuclear waste storage problem is that the minor actinides represent a high activity (see Fig.  3 ) and pose non-proliferation issues to be handled safely in a civil used plant. This might be a difficult challenge if the storage is to be operated economically together with the fuel fabrication.

Radiotoxicity of radioactive waste

The open (or ‘once through’) cycle only uses part of the energy stored in the fuel, whilst effectively wasting substantial amounts of energy that could be recovered through recycling. The conventional closed fuel cycle strategy uses the reprocessing of the spent fuel following interim storage. The main components which can be further utilised (U and Pu) are recycled to fuel manufacturing (MOX (Mixed Oxide) fuel fabrication), whilst the smaller volume of residual waste in appropriately conditioned form—e.g. vitrified and encapsulated—is disposed of in deep geological repositories.

The advanced closed fuel cycle strategy is similar to the conventional one, but within this strategy the minor actinides are also removed during reprocessing. The separated isotopes are transmuted in combination with power generation and only the net reprocessing wastes and those conditioned wastes generated during transmutation will be, following appropriate encapsulation, disposed of in deep geological repositories. The main factor that determines the overall storage capacity of a long-term repository is the heat content of nuclear waste, not its volume. During the anticipated repository time, the specific heat generated during the decay of the stored HLW must always stay below a dedicated value prescribed by the storage concept and the geological host information. The waste that results from reprocessing spent fuel from thermal reactors has a lower heat content (after a period of cooling) than does the spent fuel itself. Thus, it can be stored more densely.

A modern light water reactor of 1 GWe capacity will typically discharge about 20–25 tonnes of irradiated fuel per year of operation. About 93–94 % of the mass of typical uranium oxide irradiated fuel comprises uranium (mostly 238 U), with about 4–5 % fission products and ~1 % plutonium. About 0.1–0.2 % of the mass comprises minor actinides (neptunium, americium and curium). These latter elements accumulate in nuclear fuel because of neutron capture, and they contribute significantly to decay heat loading and neutron output, as well as to the overall radiotoxic hazard of spent fuel. Although the total minor actinide mass is relatively small—20 to 25 kg per year from a 1 GWe LWR—it has a disproportionate impact on spent fuel disposal because of its long radioactive decay times (OECD Nuclear Energy Agency 2013 ).

Generation IV development

To address the issue of sustainability of nuclear energy, in particular the use of natural resources, fast neutron reactors (FNRs) must be developed, since they can typically multiply by over a factor 50 the energy production from a given amount of uranium fuel compared to current reactors. FNRs, just as today’s fleet, will be primarily dedicated to the generation of fossil-free base-load electricity. In the FNR the fuel conversion ratio (FCR) is optimised. Through hardening the spectrum a fast reactor can be designed to burn minor actinides giving a FCR larger than unity which allows breeding of fissile materials. FNRs have been operated in the past (especially the Sodium-cooled Fast Reactor in Europe), but today’s safety, operational and competitiveness standards require the design of a new generation of fast reactors. Important research and development is currently being coordinated at the international level through initiatives such as GIF.

In 2002, six reactor technologies were selected which GIF believe represent the future of nuclear energy. These were selected from the many various approaches being studied on the basis of being clean, safe and cost-effective means of meeting increased energy demands on a sustainable basis. Furthermore, they are considered being resistant to diversion of materials for weapons proliferation and secure from terrorist attacks. The continued research and development will focus on the chosen six reactor approaches. Most of the six systems employ a closed fuel cycle to maximise the resource base and minimise high-level wastes to be sent to a repository. Three of the six are fast neutron reactors (FNR) and one can be built as a fast reactor, one is described as epithermal, and only two operate with slow neutrons like today’s plants. Only one is cooled by light water, two are helium-cooled and the others have lead–bismuth, sodium or fluoride salt coolant. The latter three operate at low pressure, with significant safety advantage. The last has the uranium fuel dissolved in the circulating coolant. Temperatures range from 510 to 1000 °C, compared with less than 330 °C for today’s light water reactors, and this means that four of them can be used for thermochemical hydrogen production.

The sizes range from 150 to 1500 MWe, with the lead-cooled one optionally available as a 50–150 MWe “battery” with long core life (15–20 years without refuelling) as replaceable cassette or entire reactor module. This is designed for distributed generation or desalination. At least four of the systems have significant operating experience already in most respects of their design, which provides a good basis for further research and development and is likely to mean that they can be in commercial operation well before 2030. However, when addressing non-proliferation concerns it is significant that fast neutron reactors are not conventional fast breeders, i.e. they do not have a blanket assembly where plutonium-239 is produced. Instead, plutonium production happens to take place in the core, where burn-up is high and the proportion of plutonium isotopes other than Pu-239 remains high. In addition, new reprocessing technologies will enable the fuel to be recycled without separating the plutonium.

In January 2014, a new GIF Technology Roadmap Update was published. 4 It confirmed the choice of the six systems and focused on the most relevant developments of them so as to define the research and development goals for the next decade. It suggested that the Generation IV technologies most likely to be deployed first are the SFR, the lead-cooled fast reactor (LFR) and the very high temperature reactor technologies. The molten salt reactor and the GFR were shown as furthest from demonstration phase.

Europe, through sustainable nuclear energy technology platform (SNETP) and ESNII, has defined its own strategy and priorities for FNRs with the goal to demonstrate Generation IV reactor technologies that can close the nuclear fuel cycle, provide long-term waste management solutions and expand the applications of nuclear fission beyond electricity production to hydrogen production, industrial heat and desalination; The SFR as a proven concept, as well as the LFR as a short-medium term alternative and the GFR as a longer-term alternative technology. The French Commissariat à l’Energie Atomique (CEA) has chosen the development of the SFR technology. Astrid (Advanced Sodium Technological Reactor for Industrial Demonstration) is based on about 45 reactor-years of operational experience in France and will be rated 250 to 600 MWe. It is expected to be built at Marcoule from 2017, with the unit being connected to the grid in 2022.

Other countries like Belgium, Italy, Sweden and Romania are focussing their research and development effort on the LFR whereas Hungary, Czech Republic and Slovakia are investing in the research and development on GFR building upon the work initiated in France on GFR as an alternative technology to SFR. Allegro GFR is to be built in eastern Europe, and is more innovative. It is rated at 100 MWt and would lead to a larger industrial demonstration unit called GoFastR. The Czech Republic, Hungary and Slovakia are making a joint proposal to host the project, with French CEA support. Allegro is expected to begin construction in 2018 operate from 2025. The industrial demonstrator would follow it.

In mid-2013, four nuclear research institutes and engineering companies from central Europe’s Visegrád Group of Nations (V4) agreed to establish a centre for joint research, development and innovation in Generation IV nuclear reactors (the Czech Republic, Hungary, Poland and Slovakia) which is focused on gas-cooled fast reactors such as Allegro.

The MYRRHA (Multi-purpose hYbrid Research Reactor for High-tech Applications) 5 project proposed in Belgium by SCK•CEN could be an Experimental Technological Pilot Plant (ETPP) for the LFR technology. Later, it could become a European fast neutron technology pilot plant for lead and a multi-purpose research reactor. The unit is rated at 100 thermal MW and has started construction at SCK-CEN’s Mol site in 2014 planned to begin operation in 2023. A reduced-power model of Myrrha called Guinevere started up at Mol in March 2010. ESNII also includes an LFR technology demonstrator known as Alfred, also about 100 MWt, seen as a prelude to an industrial demonstration unit of about 600 MWe. Construction on Alfred could begin in 2017 and the unit could start operating in 2025.

Research and development topics to meet the top-level criteria established within the GIF forum in the context of simultaneously matching economics as well as stricter safety criteria set-up by the WENRA FNR demand substantial improvements with respect to the following issues:

• Primary system design simplification,
• Improved materials,
• Innovative heat exchangers and power conversion systems,
• Advanced instrumentation, in-service inspection systems,
• Enhanced safety,

and those for fuel cycle issues pertain to:

• Partitioning and transmutation,
• Innovative fuels (including minor actinide-bearing) and core performance,
• Advanced separation both via aqueous processes supplementing the PUREX process as well as pyroprocessing, which is mandatory for the reprocessing of the high MA-containing fuels,
• Develop a final depository.

Beyond the research and development, the demonstration projects mentioned above are planned in the frame of the SET-Plan ESNII for sustainable fission. In addition, supporting research infrastructures, irradiation facilities, experimental loops and fuel fabrication facilities, will need to be constructed.

Regarding transmutation, the accelerator-driven transmutation systems (ADS) technology must be compared to FNR technology from the point of view of feasibility, transmutation efficiency and cost efficiency. It is the objective of the MYRRHA project to be an experimental demonstrator of ADS technology. From the economical point of view, the ADS industrial solution should be assessed in terms of its contribution to closing the fuel cycle. One point of utmost importance for the ADS is its ability for burning larger amounts of minor actinides (the typical maximum in a critical FNR is about 2 %).

The concept of partitioning and transmutation (P&T) has three main goals: reduce the radiological hazard associated with spent fuel by reducing the inventory of minor actinides, reduce the time interval required to reach the radiotoxicity of natural uranium and reduce the heat load of the HLW packages to be stored in the geological disposal hence reducing the foot print of the geological disposal.

Advanced management of HLW through P&T consists in advanced separation of the minor actinides (americium, curium and neptunium) and some fission products with a long half-life present in the nuclear waste and their transmutation in dedicated burners to reduce the radiological and heat loads on the geological disposal. The time scale needed for the radiotoxicity of the waste to drop to the level of natural uranium will be reduced from a ‘geological’ value (300 000 years) to a value that is comparable to that of human activities (few hundreds of years) (OECD/NEA 2006 ; OECD 2012 ; PATEROS 2008 6 ). Transmutation of the minor actinides is achieved through fission reactions and therefore fast neutrons are preferred in dedicated burners.

At the European level, four building blocks strategy for Partitioning and Transmutation have been identified. Each block poses a serious challenge in terms of research & development to be done in order to reach industrial scale deployment. These blocks are:

• Demonstration of advanced reprocessing of spent nuclear fuel from LWRs, separating Uranium, Plutonium and Minor Actinides;
• Demonstration of the capability to fabricate at semi-industrial level dedicated transmuter fuel heavily loaded in minor actinides;
• Design and construct one or more dedicated transmuters;
• Fabrication of new transmuter fuel together with demonstration of advanced reprocessing of transmuter fuel.

MYRRHA will support this Roadmap by playing the role of an ADS prototype (at reasonable power level) and as a flexible irradiation facility providing fast neutrons for the qualification of materials and fuel for an industrial transmuter. MYRRHA will be not only capable of irradiating samples of such inert matrix fuels but also of housing fuel pins or even a limited number of fuel assemblies heavily loaded with MAs for irradiation and qualification purposes.

Options for nuclear fusion beyond 2050

Nuclear fusion research, on the basis of magnetic confinement, considered in this report, has been actively pursued in Europe from the mid-60s. Fusion research has the goal to achieve a clean and sustainable energy source for many generations to come. In parallel with basic high-temperature plasma research, the fusion technology programme is pursued as well as the economy of a future fusion reactor (Ward et al. 2005 ; Ward 2009 ; Bradshaw et al. 2011 ). The goal-oriented fusion research should be driven with an increased effort to be able to give the long searched answer to the open question, “will fusion energy be able to cover a major part of mankind’s electricity demand?”. ITER, the first fusion reactor to be built in France by the seven collaborating partners (Europe, USA, Russia, Japan, Korea, China, India) is hoped to answer most of the open physics and many of the remaining technology/material questions. ITER is expected to start operation of the first plasma around 2020 and D-T operation 2027.

The European fusion research has been successful through the organisation of EURATOM to which most countries in Europe belong (the fission programme is also included in EURATOM). EUROfusion, the European Consortium for the Development of Fusion Energy, manages European fusion research activities on behalf of EURATOM. The organisation of the research has resulted in a well-focused common fusion research programme. The members of the EUROfusion 7 consortium are 29 national fusion laboratories. EUROfusion funds all fusion research activities in accordance with the “EFDA Fusion electricity. Roadmap to the realisation of fusion energy” (EFDA 2012 , Fusion electricity). The Roadmap outlines the most efficient way to realise fusion electricity. It is the result of an analysis of the European Fusion Programme undertaken by all Research Units within EUROfusion’s predecessor agreement, the European Fusion Development Agreement, EFDA.

The most successful confinement concepts are toroidal ones like tokamaks and helical systems like stellarators (Wagner 2012 , 2013 ). To avoid drift losses, two magnetic field components are necessary for confinement and stability—the toroidal and the poloidal field component. Due to their superposition, the magnetic field winds helically around a system of nested toroids. In both cases, tokamak and stellarator, the toroidal field is produced by external coils; the poloidal field arises from a strong toroidal plasma current in tokamaks. In case of helical systems all necessary fields are produced externally by coils which have to be superconductive when steady-state operation is intended. Europe is constructing the most ambitious stellarator, Wendelstein 7-X in Germany. It is a fully optimised system with promising features. W7-X goes into operation in 2015. 8

Fusion research has now reached plasma parameters needed for a fusion reactor, even if not all parameters are reached simultaneously in a single plasma discharge (see Fig.  4 ). Plotted is the triple product n•τ E• T i composed of the density n, the confinement time τ E and the ion temperature T i . For ignition of a deuterium–tritium plasma, when the internal α-particle heating from the DT-reaction takes over and allows the external heating to be switched off, the triple product has to be about >6 × 10 21  m −3  s keV). The record parameters given as of today are shown together with the fusion experiment of its achievement in Fig.  4 . The achieved parameters and the missing factors to the ultimate goal of a fusion reactor are summarised below:

• Temperature: 40 keV achieved (JT-60U, Japan); the goal is surpassed by a factor of two
• Density n surpassed by factor 5 (C-mod,USA; LHD,Japan)
• Energy confinement time: a factor of 4 is missing (JET, Europe)
• Fusion triple product (see Fig.  4 : a factor of 6 is missing (JET, Europe)
• The first scientific goal is achieved: Q (fusion power/external heating power) ~1 (0,65) (JET, Europe)
• D-T operation without problems (TFTR (USA), JET, small tritium quantities have been used, however)
• Maximal fusion power for short pulse: 16 MW (JET)
• Divertor development (ASDEX, ASDEX-Upgrade, Germany)
• Design for the first experimental reactor complete (ITER, see below)
• The optimisation of stellarators (W7-AS, W7-X, Germany)

Progress in fusion parameters. Derived in 1955, the Lawson criterion specifies the conditions that must be met for fusion to produce a net energy output (1 keV × 12 million K). From this, a fusion “triple product” can be derived, which is defined as the product of the plasma ion density, ion temperature and energy confinement time. This product must be greater than about 6 × 10 21  keV m −3  s for a deuterium–tritium plasma to ignite. Due to the radioactivity associated with tritium, today’s research tokamaks generally operate with deuterium only ( solid dots ). The large tokamaks JET(EU) and TFTR(US), however, have used a deuterium–tritium mix ( open dots ). The rate of increase in tokamak performance has outstripped that of Moore’s law for the miniaturisation of silicon chips (Pitts et al. 2006 ). Many international projects (their names are given by acronyms in the figure) have contributed to the development of fusion plasma parameters and the progress in fusion research which serves as the basis for the ITER design

After 50 years of fusion research there is no evidence for a fundamental obstacle in the basic physics. But still many problems have to be overcome as detailed below:

Critical issues in fusion plasma physics based on magnetic confinement

• confine a plasma magnetically with 1000 m 3 volume,
• maintain the plasma stable at 2–4 bar pressure,
• achieve 15 MA current running in a fluid (in case of tokamaks, avoid instabilities leading to disruptions),
• find methods to maintain the plasma current in steady-state,
• tame plasma turbulence to get the necessary confinement time,
• develop an exhaust system (divertor) to control power and particle exhaust, specifically to remove the α-particle heat deposited into the plasma and to control He as the fusion ash.

Critical issues in fusion plasma technology

• build a system with 200 MKelvin in the plasma core and 4 Kelvin about 2 m away,
• build magnetic system at 6 Tesla (max field 12 Tesla) with 50 GJ energy,
• develop heating systems to heat the plasma to the fusion temperature and current drive systems to maintain steady-state conditions for the tokamak,
• handle neutron-fluxes of 2 MW/m 2 leading to 100 dpa in the surrounding material,
• develop low activation materials,
• develop tritium breeding technologies,
• provide high availability of a complex system using an appropriate remote handling system,
• develop the complete physics and engineering basis for system licensing.

The goals of ITER

The major goals of ITER (see Fig.  5 ) in physics are to confine a D-T plasma with α-particle self-heating dominating all other forms of plasma heating, to produce about ~500 MW of fusion power at a gain Q  = fusion power/external heating power, of about 10, to explore plasma stability in the presence of energetic α-particles, and to demonstrate ash-exhaust and burn control.

Schematic layout of the ITER reactor experiment (from www.iter.org )

In the field of technology, ITER will demonstrate fundamental aspects of fusion as the self-heating of the plasma by alpha-particles, show the essentials to a fusion reactor in an integrated system, give the first test a breeding blanket and assess the technology and its efficiency, breed tritium from lithium utilising the D-T fusion neutron, develop scenarios and materials with low T-inventories. Thus ITER will provide strong indications for vital research and development efforts necessary in the view of a demonstration reactor (DEMO). ITER will be based on conventional steel as structural material. Its inner wall will be covered with beryllium to surround the plasma with low-Z metal with low inventory properties. The divertor will be mostly from tungsten to sustain the high α-particle heat fluxes directed onto target plates situated inside a divertor chamber. An important step in fusion reactor development is the achievement of licensing of the complete system.

The rewards from fusion research and the realisation of a fusion reactor can be described in the following points:

• fusion has a tremendous potential thanks to the availability of deuterium and lithium as primary fuels. But as a recommendation, the fusion development has to be accelerated,

Fusion time strategy towards the fusion reactor on the net (EFDA 2012 , Fusion electricity. A roadmap to the realisation of fusion energy)

In addition, there is the fusion technology programme and its material branch, which ultimately need a neutron source to study the interaction with 14 MeV neutrons. For this purpose, a spallation source IFMIF is presently under design. As a recommendation, ways have to be found to accelerate the fusion development. In general, with ITER, IFMIF and the DEMO, the programme will move away from plasma science more towards technology orientation. After the ITER physics and technology programme—if successful—fusion can be placed into national energy supply strategies. With fusion, future generations can have access to a clean, safe and (at least expected of today) economic power source.

The fission nuclear power continues to be an essential part of the low-carbon electricity generation in the world for decades to come. There are breakthrough possibilities in the development of new generation nuclear reactors where the life-time of the nuclear waste can be reduced to some hundreds of years instead of the present time-scales of hundred thousand of years. Research on the fourth generation reactors is needed for the realisation of this development. For the fast nuclear reactors a substantial research and development effort is required in many fields—from material sciences to safety demonstration—to attain the envisaged goals. Fusion provides a long-term vision for an efficient energy production. The fusion option for a nuclear reactor for efficient production of electricity should be vigorously pursued on the international arena as well as within the European energy roadmap to reach a decision point which allows to critically assess this energy option.

Box 1 Explanations of abbreviations used in this article

Biographies.

is Professor in Energy Research and Director of MTA Center for Energy Research, Budapest, Hungary. His research interests are in the development of new fission reactors, new structural materials, high temperature irradiation resistance, mechanical deformation.

is Professor of Applied Atomic and Molecular Physics at Royal Institute of Technology, (KTH), Stockholm, Sweden. Her research interests are in basic atomic and molecular processes studied with synchrotron radiation, development of diagnostic techniques for analysing the performance of fusion experiments in particular development of photon spectroscopic diagnostics.

1 http://www.iea.org/ .

2 https://cnpp.iaea.org/pages/index.htm .

3 GenIV International forum: ( http://www.gen-4.org/index.html ).

4 https://www.gen-4.org/gif/jcms/c_60729/technology-roadmap-update-2013 .

5 http://myrrha.sckcen.be/ .

6 www.sckcen.be/pateros/ .

7 https://www.euro-fusion.org/ .

8 https://www.ipp.mpg.de/ippcms/de/pr/forschung/w7x/index.html .

Contributor Information

Akos Horvath, Email: [email protected] .

Elisabeth Rachlew, Email: es.htk@kre .

• Bradshaw AM, Hamacher T, Fischer U. Is nuclear fusion a sustainable energy form? Fusion Engineering and Design. 2011; 86 :2770–2773. doi: 10.1016/j.fusengdes.2010.11.040. [ CrossRef ] [ Google Scholar ]
• EASAC. 2014. EASAC Report 23—Management of spent nuclear fuel and its waste. http://www.easac.eu/energy/reports-and-statements/detail-view/article/management-o.html .
• EFDA. 2012. Fusion electricity. A roadmap to the realization of fusion energy. https://www.euro-fusion.org/wpcms/wp-content/uploads/2013/01/JG12.356-web.pdf .
• Garbil, R., and G. Van Goethem. (ed.). 2013. Symposium on the “Benefits and limitations of nuclear fission for a low carbon economy”, European Commission, Brussels, ISBN 978-92.79.29833.2.
• Hózer, Z. S. Borovitskiy, G. Buday, B. Boullis, G. Cognet, S. A. Delichatsios, J. Gadó, A. Grishin, et al. 2010. Regional strategies concerning nuclear fuel cycle and HLRW in Central and Eastern European Countries. International conference on management of spent fuel from Nuclear Power Reactors, Vienna, Conference ID:38089 (CN-178).
• IEA (International Energy Authority). 2014. World Energy Outlook 2014. http://www.iea.org/ .
• IPCC. 2014. Summary for policymakers WGIII AR5, SPM.4.2.2 Energy supply.
• Kautsky, U., T. Lindborg, and J. Valentin (ed.). 2013. Humans and ecosystems over the coming millenia: A biosphere assessment of radioactive waste disposal in Sweden. Ambio 42(4): 381–526. [ PMC free article ] [ PubMed ]
• OECD. 2011–2012. Fact book: Economic, environmental and social statistics. Retrieved from http://www.oecd-ilibrary.org/economics/oecd-factbook-2011-2012_factbook-2011-en .
• OECD/NEA. 2006. Potential benefits and impacts of advanced nuclear fuel cycles with actinide partitioning and transmutation. ISBN: 978-92-64-99165-1, http://www.oecd-nea.org/science/reports/2011/6894-benefits-impacts-advanced-fuel.pdf .
• OECD Nuclear Energy Agency. 2013. Minor actinide burning in thermal reactors. A report by the Working Party on Scientific Issues of Reactor Systems, NEA #6997. http://www.oecd-nea.org/science/pubs/2013/6997-minor-actinide.pdf .
• Pitts R, Buttery R, Pinches S. Fusion: The way ahead. Physics World. 2006; 19 :20–26. doi: 10.1088/2058-7058/19/3/35. [ CrossRef ] [ Google Scholar ]
• Ricotti ME. Nuclear energy: Basics, present, future. EPJ Web of Conferences. 2013; 54 :01005. doi: 10.1051/epjconf/20135401005. [ CrossRef ] [ Google Scholar ]
• Vandenbosch R, Vandenbosch SE. Nuclear waste confidence: Is indefinite storage safe? APS Physics and Society. 2015; 44 :5–7. [ Google Scholar ]
• Wagner, F. 2012. Fusion energy by magnetic confinement. IPP 18/3, http://hdl.handle.net/11858/00-001M-0000-0026-E767-A .
• Wagner F. Physics of magnetic confinement fusion. EPJ Web of Conferences. 2013; 54 :01007. doi: 10.1051/epjconf/20135401007. [ CrossRef ] [ Google Scholar ]
• Ward DJ. The contribution of fusion to sustainable development. Fusion Engineering and Design. 2009; 82 :528–533. doi: 10.1016/j.fusengdes.2007.02.028. [ CrossRef ] [ Google Scholar ]
• Ward DJ, Cook I, Lechon Y, Saez R. The economic viability of fusion power. Engineering and Design. 2005; 75–79 :1221–1227. doi: 10.1016/j.fusengdes.2005.06.160. [ CrossRef ] [ Google Scholar ]

• Wind Energy Technologies Office
• Wind Energy Career Map
• Key Activities in Wind Energy
• WETO Budget
• WETO Organization & Contacts
• Atmosphere to Electrons
• Distributed Wind
• Environmental Impacts & Siting
• Next-Generation Wind Technology
• Demonstration
• Floating Offshore Wind Shot
• Market Acceleration
• R&D Consortium
• Renewable Systems Integration
• Resource Assessment & Characterization
• Testing & Certification
• Drivetrains
• Infrastructure & Logistics
• Wind Turbine Radar Interference
• Wind Turbine Sustainability
• Workforce Development & Education
• History of Wind Energy
• How Distributed Wind Works
• How Wind Turbines Work
• WINDExchange
• Small Wind Systems FAQs
• WETO Peer Reviews
• Wind Energy FAQs
• Wind Energy Market Reports
• Wind Energy Projects Map
• Related Opportunities
• Wind Energy Technologies Office Updates
• Wind R&D Newsletter

Wind energy offers many advantages, which explains why it's one of the fastest-growing energy sources in the world. To further expand wind energy’s capabilities and community benefits, researchers are working to address technical and socio-economic challenges in support of a decarbonized electricity future.

Learn more about ongoing research to take advantage of these benefits and tackle wind energy challenges.

Advantages of Wind Power

• Wind power creates good-paying jobs.  There are over 125,000 people working in the U.S. wind industry across all 50 states, and that number continues to grow. According to the U.S. Bureau of Labor Statistics , wind turbine service technicians are the fastest growing U.S. job of the decade. Offering career opportunities ranging from blade fabricator to asset manager, the wind industry has the potential to support hundreds of thousands of more jobs by 2050.
• Wind power is a domestic resource that enables U.S. economic growth. In 2022, wind turbines operating in all 50 states generated more than 10% of the net total of the country’s energy . That same year, investments in new wind projects added $20 billion to the U.S. economy.
• Wind power is a clean and renewable energy source. Wind turbines harness energy from the wind using mechanical power to spin a generator and create electricity. Not only is wind an abundant and inexhaustible resource, but it also provides electricity without burning any fuel or polluting the air. Wind energy in the United States helps avoid 336 million metric tons of carbon dioxide emissions annually —equivalent to the emissions from 73 million cars.
• Wind power benefits local communities. Wind projects deliver an estimated $2 billion in state and local tax payments and land-lease payments each year. Communities that develop wind energy can use the extra revenue to put towards school budgets, reduce the tax burden on homeowners, and address local infrastructure projects.
• Wind power is cost-effective. Land-based, utility-scale wind turbines provide one of the lowest-priced energy sources available today. Furthermore, wind energy’s cost competitiveness continues to improve with advances in the science and technology of wind energy.
• Wind turbines work in different settings. Wind energy generation fits well in agricultural and multi-use working landscapes. Wind energy is easily integrated in rural or remote areas, such as farms and ranches or coastal and island communities, where high-quality wind resources are often found.

Challenges of Wind Power

• Wind power must compete with other low-cost energy sources. When comparing the cost of energy associated with new power plants , wind and solar projects are now more economically competitive than gas, geothermal, coal, or nuclear facilities. However, wind projects may not be cost-competitive in some locations that are not windy enough. Next-generation technology , manufacturing improvements , and a better understanding of wind plant physics can help bring costs down even more.
• Ideal wind sites are often in remote locations. Installation challenges must be overcome to bring electricity from wind farms to urban areas, where it is needed to meet demand. Upgrading the nation’s transmission network to connect areas with abundant wind resources to population centers could significantly reduce the costs of expanding land-based wind energy. In addition, offshore wind energy transmission and grid interconnection capabilities are improving.
• Turbines produce noise and alter visual aesthetics. Wind farms have different impacts on the environment compared to conventional power plants, but similar concerns exist over both the noise produced by the turbine blades and the  visual impacts on the landscape .
• Wind plants can impact local wildlife. Although wind projects rank lower than other energy developments in terms of wildlife impacts, research is still needed to minimize wind-wildlife interactions . Advancements in technologies,  properly siting wind plants, and ongoing environmental research are working to reduce the impact of wind turbines on wildlife.

Changing the Game: A Leader's Perspective on CD PROJEKT RED'S Transformation | D.I.C.E. Summit 2024

Gabe Amatangelo, Game Director of the acclaimed Cyberpunk 2077: Phantom Liberty expansion, shares his unique perspective on leading a team during the turbulent period that accompanies a studio-wide transformation.

As CD PROJEKT RED reconfigured its development policies and production framework, Gabe was tasked with juggling simultaneous tasks: reconfiguring teams to a new, Agile methodology while supporting Cyberpunk 2077, developing the expansion, and most critically, overhauling virtually all of the base game’s systems and releasing Update 2.0 ahead of Phantom Liberty.

Gabe highlights the most important focus areas and successes that allowed CD PROJEKT RED to literally change the game and inject both Cyberpunk 2077 and the studio with fresh energy and momentum.

Key takeaways include common pain points that can come up during such a comprehensive overhaul and how to successfully tackle them; how to create buy-in from employees; how studios and companies can leverage past challenges to create new paths and possibilities; and how to ensure a company retains its personality while also building new management and work styles from the ground up. To learn more about the Annual D.I.C.E. Summit, go to https://www.dicesummit.org/

Did you enjoy this video?

In this video.

• Bahasa Indonesia
• Eastern Europe
• Moscow Oblast

Elektrostal

Elektrostal Localisation : Country Russia , Oblast Moscow Oblast . Available Information : Geographical coordinates , Population, Area, Altitude, Weather and Hotel . Nearby cities and villages : Noginsk , Pavlovsky Posad and Staraya Kupavna .

Information

Find all the information of Elektrostal or click on the section of your choice in the left menu.

• Update data

Elektrostal Demography

Information on the people and the population of Elektrostal.

Elektrostal Geography

Geographic Information regarding City of Elektrostal .

Elektrostal Distance

Distance (in kilometers) between Elektrostal and the biggest cities of Russia.

Elektrostal Map

Locate simply the city of Elektrostal through the card, map and satellite image of the city.

Elektrostal Nearby cities and villages

Elektrostal weather.

Weather forecast for the next coming days and current time of Elektrostal.

Elektrostal Sunrise and sunset

Find below the times of sunrise and sunset calculated 7 days to Elektrostal.

Elektrostal Hotel

Our team has selected for you a list of hotel in Elektrostal classified by value for money. Book your hotel room at the best price.

Elektrostal Nearby

Below is a list of activities and point of interest in Elektrostal and its surroundings.

Elektrostal Page

• Information /Russian-Federation--Moscow-Oblast--Elektrostal#info
• Demography /Russian-Federation--Moscow-Oblast--Elektrostal#demo
• Geography /Russian-Federation--Moscow-Oblast--Elektrostal#geo
• Distance /Russian-Federation--Moscow-Oblast--Elektrostal#dist1
• Map /Russian-Federation--Moscow-Oblast--Elektrostal#map
• Nearby cities and villages /Russian-Federation--Moscow-Oblast--Elektrostal#dist2
• Weather /Russian-Federation--Moscow-Oblast--Elektrostal#weather
• Sunrise and sunset /Russian-Federation--Moscow-Oblast--Elektrostal#sun
• Hotel /Russian-Federation--Moscow-Oblast--Elektrostal#hotel
• Nearby /Russian-Federation--Moscow-Oblast--Elektrostal#around
• Page /Russian-Federation--Moscow-Oblast--Elektrostal#page
• Terms of Use
• Copyright © 2024 DB-City - All rights reserved
• Change Ad Consent Do not sell my data

Current time by city

For example, New York

Current time by country

For example, Japan

Time difference

For example, London

For example, Dubai

Coordinates

For example, Hong Kong

For example, Delhi

For example, Sydney

Geographic coordinates of Elektrostal, Moscow Oblast, Russia

City coordinates

Coordinates of Elektrostal in decimal degrees

Coordinates of elektrostal in degrees and decimal minutes, utm coordinates of elektrostal, geographic coordinate systems.

WGS 84 coordinate reference system is the latest revision of the World Geodetic System, which is used in mapping and navigation, including GPS satellite navigation system (the Global Positioning System).

Geographic coordinates (latitude and longitude) define a position on the Earth’s surface. Coordinates are angular units. The canonical form of latitude and longitude representation uses degrees (°), minutes (′), and seconds (″). GPS systems widely use coordinates in degrees and decimal minutes, or in decimal degrees.

Latitude varies from −90° to 90°. The latitude of the Equator is 0°; the latitude of the South Pole is −90°; the latitude of the North Pole is 90°. Positive latitude values correspond to the geographic locations north of the Equator (abbrev. N). Negative latitude values correspond to the geographic locations south of the Equator (abbrev. S).

Longitude is counted from the prime meridian ( IERS Reference Meridian for WGS 84) and varies from −180° to 180°. Positive longitude values correspond to the geographic locations east of the prime meridian (abbrev. E). Negative longitude values correspond to the geographic locations west of the prime meridian (abbrev. W).

UTM or Universal Transverse Mercator coordinate system divides the Earth’s surface into 60 longitudinal zones. The coordinates of a location within each zone are defined as a planar coordinate pair related to the intersection of the equator and the zone’s central meridian, and measured in meters.

Elevation above sea level is a measure of a geographic location’s height. We are using the global digital elevation model GTOPO30 .

Elektrostal , Moscow Oblast, Russia

• Popular Professionals
• Design & Planning
• Construction & Renovation
• Finishes & Fixtures
• Landscaping & Outdoor
• Systems & Appliances
• Interior Designers & Decorators
• Architects & Building Designers
• Design-Build Firms
• Kitchen & Bathroom Designers
• General Contractors
• Kitchen & Bathroom Remodelers
• Home Builders
• Roofing & Gutters
• Cabinets & Cabinetry
• Tile & Stone
• Hardwood Flooring Dealers
• Landscape Contractors
• Landscape Architects & Landscape Designers
• Home Stagers
• Swimming Pool Builders
• Lighting Designers and Suppliers
• 3D Rendering
• Sustainable Design
• Basement Design
• Architectural Design
• Universal Design
• Energy-Efficient Homes
• Multigenerational Homes
• House Plans
• Home Remodeling
• Home Additions
• Green Building
• Garage Building
• New Home Construction
• Basement Remodeling
• Stair & Railing Contractors
• Cabinetry & Cabinet Makers
• Roofing & Gutter Contractors
• Window Contractors
• Exterior & Siding Contractors
• Carpet Contractors
• Carpet Installation
• Flooring Contractors
• Wood Floor Refinishing
• Tile Installation
• Custom Countertops
• Quartz Countertops
• Cabinet Refinishing
• Custom Bathroom Vanities
• Finish Carpentry
• Cabinet Repair
• Custom Windows
• Window Treatment Services
• Window Repair
• Fireplace Contractors
• Paint & Wall Covering Dealers
• Door Contractors
• Glass & Shower Door Contractors
• Landscape Construction
• Land Clearing
• Garden & Landscape Supplies
• Deck & Patio Builders
• Deck Repair
• Patio Design
• Stone, Pavers, & Concrete
• Paver Installation
• Driveway & Paving Contractors
• Driveway Repair
• Asphalt Paving
• Garage Door Repair
• Fence Contractors
• Fence Installation
• Gate Repair
• Pergola Construction
• Spa & Pool Maintenance
• Swimming Pool Contractors
• Hot Tub Installation
• HVAC Contractors
• Electricians
• Appliance Services
• Solar Energy Contractors
• Outdoor Lighting Installation
• Landscape Lighting Installation
• Outdoor Lighting & Audio/Visual Specialists
• Home Theater & Home Automation Services
• Handyman Services
• Closet Designers
• Professional Organizers
• Furniture & Accessories Retailers
• Furniture Repair & Upholstery Services
• Specialty Contractors
• Color Consulting
• Wine Cellar Designers & Builders
• Home Inspection
• Custom Artists
• Columbus, OH Painters
• New York City, NY Landscapers
• San Diego, CA Bathroom Remodelers
• Minneapolis, MN Architects
• Portland, OR Tile Installers
• Kansas City, MO Flooring Contractors
• Denver, CO Countertop Installers
• San Francisco, CA New Home Builders
• Rugs & Decor
• Home Improvement
• Kitchen & Tabletop
• Bathroom Vanities
• Bathroom Vanity Lighting
• Bathroom Mirrors
• Bathroom Fixtures
• Nightstands & Bedside Tables
• Kitchen & Dining
• Bar Stools & Counter Stools
• Dining Chairs
• Dining Tables
• Buffets and Sideboards
• Kitchen Fixtures
• Wall Mirrors
• Living Room
• Armchairs & Accent Chairs
• Coffee & Accent Tables
• Sofas & Sectionals
• Media Storage
• Patio & Outdoor Furniture
• Outdoor Lighting
• Ceiling Lighting
• Chandeliers
• Pendant Lighting
• Wall Sconces
• Desks & Hutches
• Office Chairs
• View All Products
• Designer Picks
• Side & End Tables
• Console Tables
• Living Room Sets
• Chaise Lounges
• Ottomans & Poufs
• Bedroom Furniture
• Nightstands
• Bedroom Sets
• Dining Room Sets
• Sideboards & Buffets
• File Cabinets
• Room Dividers
• Furniture Sale
• Trending in Furniture
• View All Furniture
• Bath Vanities
• Single Vanities
• Double Vanities
• Small Vanities
• Transitional Vanities
• Modern Vanities
• Houzz Curated Vanities
• Best Selling Vanities
• Bathroom Vanity Mirrors
• Medicine Cabinets
• Bathroom Faucets
• Bathroom Sinks
• Shower Doors
• Showerheads & Body Sprays
• Bathroom Accessories
• Bathroom Storage
• Trending in Bath
• View All Bath
• Houzz x Jennifer Kizzee
• Houzz x Motivo Home
• How to Choose a Bathroom Vanity

• Patio Furniture
• Outdoor Dining Furniture
• Outdoor Lounge Furniture
• Outdoor Chairs
• Adirondack Chairs
• Outdoor Bar Furniture
• Outdoor Benches
• Wall Lights & Sconces
• Outdoor Flush-Mounts
• Landscape Lighting
• Outdoor Flood & Spot Lights
• Outdoor Decor
• Outdoor Rugs
• Outdoor Cushions & Pillows
• Patio Umbrellas
• Lawn & Garden
• Garden Statues & Yard Art
• Planters & Pots
• Outdoor Sale
• Trending in Outdoor
• View All Outdoor
• 8 x 10 Rugs
• 9 x 12 Rugs
• Hall & Stair Runners
• Home Decor & Accents
• Pillows & Throws
• Decorative Storage
• Faux Florals
• Wall Panels
• Window Treatments
• Curtain Rods
• Blackout Curtains
• Blinds & Shades
• Rugs & Decor Sale
• Trending in Rugs & Decor
• View All Rugs & Decor
• Pendant Lights
• Flush-Mounts
• Ceiling Fans
• Track Lighting
• Wall Lighting
• Swing Arm Wall Lights
• Display Lighting
• Table Lamps
• Floor Lamps
• Lamp Shades
• Lighting Sale
• Trending in Lighting
• View All Lighting
• Bathroom Remodel
• Kitchen Remodel
• Kitchen Faucets
• Kitchen Sinks
• Major Kitchen Appliances
• Cabinet Hardware
• Backsplash Tile
• Mosaic Tile
• Wall & Floor Tile
• Accent, Trim & Border Tile
• Whole House Remodel
• Heating & Cooling
• Building Materials
• Front Doors
• Interior Doors
• Home Improvement Sale
• Trending in Home Improvement
• View All Home Improvement
• Cups & Glassware
• Kitchen & Table Linens
• Kitchen Storage and Org
• Kitchen Islands & Carts
• Food Containers & Canisters
• Pantry & Cabinet Organizers
• Kitchen Appliances
• Gas & Electric Ranges
• Range Hoods & Vents
• Beer & Wine Refrigerators
• Small Kitchen Appliances
• Cookware & Bakeware
• Tools & Gadgets
• Kitchen & Tabletop Sale
• Trending in Kitchen & Tabletop
• View All Kitchen & Tabletop
• Storage & Organization
• Baby & Kids

• View all photos
• Dining Room
• Breakfast Nook
• Family Room
• Bed & Bath
• Powder Room
• Storage & Closet
• Outdoor Kitchen
• Bar & Wine
• Wine Cellar
• Home Office
• Popular Design Ideas
• Kitchen Backsplash
• Deck Railing
• Privacy Fence
• Small Closet
• Stories and Guides
• Popular Stories
• Renovation Cost Guides
• Fence Installation Cost Guide
• Window Installation Cost Guide
• Discussions
• Design Dilemmas
• Before & After
• Houzz Research
• View all pros
• View all services
• View all products
• View all sales
• Living Room Chairs
• Dining Room Furniture
• Coffee Tables
• Home Office Furniture
• Join as a Pro
• Interior Design Software
• Project Management
• Custom Website
• Lead Generation
• Invoicing & Billing
• Landscape Contractor Software
• General Contractor Software
• Remodeler Software
• Builder Software
• Roofer Software
• Architect Software
• Takeoff Software
• Lumber & Framing Takeoffs
• Steel Takeoffs
• Concrete Takeoffs
• Drywall Takeoffs
• Insulation Takeoffs
• Stories & Guides
• LATEST FROM HOUZZ
• HOUZZ DISCUSSIONS
• SHOP KITCHEN & DINING
• Kitchen & Dining Furniture
• Sinks & Faucets
• Kitchen Cabinets & Storage
• Knobs & Pulls
• Kitchen Knives
• KITCHEN PHOTOS
• FIND KITCHEN PROS
• Bath Accessories
• Bath Linens
• BATH PHOTOS
• FIND BATH PROS
• SHOP BEDROOM
• Beds & Headboards
• Bedroom Decor
• Closet Storage
• Bedroom Vanities
• BEDROOM PHOTOS
• Kids' Room
• FIND DESIGN PROS
• SHOP LIVING
• Fireplaces & Accessories
• LIVING PHOTOS
• SHOP OUTDOOR
• Pool & Spa
• Backyard Play
• OUTDOOR PHOTOS
• FIND LANDSCAPING PROS
• SHOP LIGHTING
• Bathroom & Vanity
• Flush Mounts
• Kitchen & Cabinet
• Outdoor Wall Lights
• Outdoor Hanging Lights
• Kids' Lighting
• Decorative Accents
• Artificial Flowers & Plants
• Decorative Objects
• Screens & Room Dividers
• Wall Shelves
• About Houzz
• Houzz Credit Cards
• Privacy & Notice
• Cookie Policy
• Your Privacy Choices
• Mobile Apps
• Copyright & Trademark
• For Professionals
• Houzz vs. Houzz Pro
• Houzz Pro vs. Ivy
• Houzz Pro Advertising Reviews
• Houzz Pro 3D Floor Planner Reviews
• Trade Program
• Buttons & Badges
• Your Orders
• Shipping & Delivery
• Return Policy
• Houzz Canada
• Review Professionals
• Suggested Professionals
• Accessibility
• Houzz Support
• COUNTRY COUNTRY

New & Custom Home Builders in Elektrostal'

Location (1).

• Use My Current Location

Popular Locations

• Albuquerque
• Cedar Rapids
• Grand Rapids
• Indianapolis
• Jacksonville
• Kansas City
• Little Rock
• Los Angeles
• Minneapolis
• New Orleans
• Oklahoma City
• Orange County
• Philadelphia
• Portland Maine
• Salt Lake City
• San Francisco
• San Luis Obispo
• Santa Barbara
• Washington D.C.
• Elektrostal', Moscow Oblast, Russia

Professional Category (1)

• Accessory Dwelling Units (ADU)

Featured Reviews for New & Custom Home Builders in Elektrostal'

• Reach out to the pro(s) you want, then share your vision to get the ball rolling.
• Request and compare quotes, then hire the Home Builder that perfectly fits your project and budget limits.

Before choosing a Builder for your residential home project in Elektrostal', there are a few important steps to take:

• Define your project: Outline your desired home type, features, and layout. Provide specific details and preferences to help the builder understand your vision.
• Establish a budget: Develop a comprehensive budget, including construction expenses and material costs. Communicate your budgetary constraints to the builder from the beginning.
• Timeline: Share your estimated timeline or desired completion date.
• Site conditions: Inform the builder about any unique site conditions or challenges.
• Local regulations: Make the builder aware of any building regulations or permits required.
• Land Surveying

What do new home building contractors do?

Questions to ask a prospective custom home builder in elektrostal', moscow oblast, russia:.

If you search for Home Builders near me you'll be sure to find a business that knows about modern design concepts and innovative technologies to meet the evolving needs of homeowners. With their expertise, Home Builders ensure that renovation projects align with clients' preferences and aspirations, delivering personalized and contemporary living spaces.

BUSINESS SERVICES

Connect with us.