Local Smart Grids Based on Innovative Market mechanisms: Comparison
Please note this is a comparison between Version 2 by Camila Xu and Version 1 by Łukasz Mazur.

The concept that fits into the creation of locally sustainable power systems without the use of fossil fuels is a smart grid, defined as "a power grid that can effectively and cost-free interfere with the behavior and activities of all users connected to it, including ensuring an economically efficient, sustainable power system with low losses."

  • energy system
  • energy balancing
  • energy management
  • energy self-sufficiency

1. Introduction

In today’s power systems, electricity is generated by large generation units that convert chemical energy stored in fossil fuels. The generated electricity is transmitted (extensive power transmission networks for the highest voltage are needed), which means that it has to be transformed many times. Then, the electricity is distributed and only at the end of this process is used by end users [1]. Heat is currently produced in local [1](decentralized) systems. In this case, chemical energy from fossil fuels is also most often used. Usually, the chemical energy of the fuel in the combustion process is converted into heat in small household stoves, housing estate boiler houses or small municipal heating plants. Large heat generating units that could supply large areas with heat are not implemented. Heat is currently produced in local (decentralized) systems. In this case, chemical energy from fossil fuels is also most often used. Usually, the chemical energy of the fuel in the combustion process is converted into heat in small household stoves, housing estate boiler houses or small municipal heating plants. Large heat generating units that could supply large areas with heat are not implemented. Additionally, industrial plants usually produce heat in the form of process steam in their heating plants or buy this energy factor from local producers (combined heat and power plants—CHP) located nearby.
Additionally, industrial plants usually produce heat in the form of process steam in their heating plants or buy this energy factor from local producers (combined heat and power plants—CHP) located nearby. The use of fossil fuels is responsible for climate change and is considered one of the main threats [2, 3][2][3]. The continuous growth of the human population, which has already exceeded 8 billion [4][4], as well as technological and social development, result in increasing energy consumption. Fossil fuels are still the dominant source of energy. It is expected that in the absence of changes in the energy mix of countries, especially the developing ones, the global demand for oil in 2030 will account for 30% of the global energy demand, followed by coal (24%) and natural gas (24%)  [5].

2. The Concept of Local Smart Grids Based on Innovative Market Mechanisms

The concept that fits into the creation of locally balanced power systems without the use of fossil fuels is the smart grid, defined according to [38][6] as “a power grid that can effectively and cost-free interfere in the behavior and activities of all users connected to it, including to provide an economically efficient, sustainable, low-loss power system”. These are mostly self-sufficient systems that can solve problems within the local network, ensuring the reliable, safe operation of the system with high-quality electricity for each user. The infrastructure of smart grids for the implementation of basic tasks includes systems [39][7] for energy management, and security and protection, as well as IT and communication systems. The creation of locally balanced energy systems without the use of fossil fuels based on a smart grid forces a change in the perception of current energy markets, and then their modernization and change in management algorithms. In this context, the important role of active monitoring of technologies and energy sources should be emphasized. Active monitoring is understood as a process of continuous supervision, diagnosis, control of the boundary zone, regulation of the environment and compensation of the tested system [40][8]. This monitoring is dedicated to energy management in technical systems with a higher level of ability to use information from observation (registration) to achieve energy management (balancing) goals and improve the environment [40][8]. Achieving these goals is possible thanks to, among others, the following factors:
  • Optimization, understood as the search for the best means, method, and technical activity through operations on mathematical models;
  • Modernization of the system through actions on models and searching for novelties;
  • Innovation, i.e., inventing and implementing a new measure, system or technical activity.
Active monitoring, as a superior element of energy management systems, can significantly increase the energy efficiency of the system and allow the obtention of economic benefits [40][8]. Increasingly, in the context of the operation of energy systems without the use of fossil fuels and the formation of energy communities, the term “Net Zero Energy” is introduced. This term may refer to individual buildings (net zero energy buildings — NZEB) [42][9]. These concepts are to be the main solution enabling the reduction in greenhouse gas emissions by 2050 [43][10]. Generally, it is a technical system in which, through optimization activities, an increase in efficiency and the balance of energy (heat, electricity, transport, etc.) is satisfied by the local production of energy from renewable sources  [44, 45][11][12]. Depending on the area (facility), the definition may be slightly different. A summary and comparison of definitions of net-zero areas is presented in publication [42][9]. The net zero smart energy system according [8][13] consists of three subsystems:
  • The energy supply subsystem — a multi-energy, centralized system including wind, solar and water power plants, and pumped storage;
22][23]. This system includes automatic, electronic transactions between suppliers and users. PNWSGD uses “hierarchical signal communication based on peer-to-peer along the paths followed by electrons through the power grid” [52][21]. Each element of the system, node, every 5 min, must predict the dynamic cost of energy at the node and the energy that would flow to/from each neighboring node. Based on the activities, it was found that such an approach is justified and enables the achievement of the set goals [53][22]. Another American project is the “GridSMART Demonstration Project” implemented in 2009–2013 [55] [24]. Its main objective was to design, build and operate an innovative system for the participation of individual consumers and their resources for the operation of the power system in real time, using incentives to increase efficiency in the normal operation of the power system and flexible response in situations of increased system load [55][24]. The system adjusted electricity consumption by consumers in response to a 5 min price signal. The household software developed within the project uses price signals from the local wholesale market to obtain the price of energy and manages the HVAC (heating, ventilation, and air conditioning) system of the household [56][25]. ]. The real-time tariff introduced in the draft describes the wholesale market price [56][25]. In addition to the development of market mechanisms, the project also included the creation of a monitoring and smart metering system, i.e., the design of a cooling/heating thermostat, aimed at balancing the consumer’s willingness to reduce energy bills in return for his willingness to be flexible. Moreover, it included setting the energy price at which the load will be switched on (or off).Based on the performed activities, it was found that this solution makes it possible to reduce the system load by about 5% in a 3.5 h system [56][25]. In addition, consumer surveys confirmed satisfaction with the results of the project [56][25]. PowerMatcher Suite is a project of Flexiblepower Alliance Network. It consists of two open-source technologies: PowerMatcher and Energy Flexibility Platform and Interface (EF-Pi), which are complementary. PowerMatcher is a so-called intelligent network coordination and control mechanism based on TE to balance local energy resources and controllable, disposable loads (devices) in real time [57][26]. PowerMatcher provides market mechanisms in order to achieve market equilibrium, and devices “work” on the basis of the game of demand/supply—the main structure here is the technology of agents; each device (receiver) is represented by an agent, and each agent can strive for many purposes. Here, the bidding agent (auctioneer) is distinguished, being “at the top” of the hierarchy of agents—it is this entity that aggregates all offers received from “lower-rank” agents and returns the price to them as an incentive to start production or energy consumption by users. The price is intended to be system-equivalent and may be different for each device. The “lower” rank is the agent of the concentrator, which concentrates or aggregates the offers and places the offer higher in the hierarchy. Devices in the system are represented by a device agent that sends bids and receives prices from the system. Based on the price signal, it sends set values to the device and receives information about its current status. It is a fully scalable system that can be subjected to various modifications and individual settings [57][26]. In turn, EF-Pi is a platform that provides the ability for communication between devices and services within the intelligent network. It is a runtime environment in which it is possible to deploy applications related to smart grids and connect devices to it, providing appropriate interfaces for interaction between participants [57]. Within EF-Pi, there are four so-called control spaces, i.e., “ways to place the information contained in it so that the Application is able to understand this device” [57] .[26]. Within EF-Pi, there are four so-called control spaces, i.e., “ways to place the information contained in it so that the Application is able to understand this device” [26]. In recent years, several projects have been implemented (mainly in the Netherlands, but also in Denmark and Germany) that use PowerMatcher Suite, both in areas with only households, as well as in public facilities or islands (Bornholm) or energy communities with a high coefficient of self-sufficiency  [57][26]. In the described solution of using TE [[19], 50] four villages in India were selected as a case study, two of which had photovoltaic installations and energy storage facilities, and the other two had no generation or energy storage systems. The TE architecture is used to re-represent the generation, energy storage and load subsystems involving the exchange of energy between the subsystems and uses a multi-objective genetic algorithm used to optimize energy consumption and increase the reliability and instability of power trading in the microgrid with minimal energy costs for each village. Studies of three different TE models were carried out, on the basis of which it was found that the most advantageous solution is to create energy sharing systems using RES, in an integrated mode, in which the community is able to exchange energy with each other based on local energy markets. The authors add that in the case of a transactive market, the local energy balance must be maintained [50][19]. Management of systems using RES is increasingly carried out by so-called virtual power plants. They are defined as a cluster of distributed generation units (mainly using RES), controlled loads, energy storage systems participating in the energy market as independent power plants supervised and controlled by the energy management system (EMS)  [58] [27].  Energy management systems can operate using a variety of objective functions, including the following:
  • Minimization of energy generation costs [59 , 60 , 61 , 62][28][
  • Energy use subsystem—an effectively managed distributed generation system (photovoltaic (PV) + heat pumps), low-emission transport, cooperation with the power grid (V2G— vehicle to grid), the introduction of heat recovery, CO2 capture, and storage and utilization systems (CCUS) in industrial areas;
  • The energy transmission subsystem — the introduction of innovative market solutions.

In the last 10 years, many studies and research related to the subject of energy management and the development of local energy markets (LEM) have been conducted. It ought to be noted, however, that despite so many scientific and technical activities, no single strategy for the management and functioning of markets within such systems has been developed, which would allow for full local balancing and full self-sufficiency, without the use of fossil fuels. The proposed activities in the context of ensuring self-sufficiency mainly concern a limited number of facilities (buildings), and the proposed market solutions are related to a limited area.

According to [14] [46]energy markets are “market platforms targeted at electricity end users so that they can negotiate transactions with each other, becoming active market participants, which is a solution for balancing local systems”. Market mechanisms are mainly based on community markets (centralized, where players are looking for the best solution in the context of energy trading) and peer-to-peer markets (decentralized platforms where consumers and prosumers make direct transactions with each other, reducing the role of an energy markets are “market platforms targeted at electricity end users so that they can negotiate transactions with each other, becoming active market participants, which is a solution for balancing local systems”. Market mechanisms are mainly based on community markets (centralized, where players are looking for the best solution in the context of energy trading) and peer-to-peer markets (decentralized platforms where consumers and prosumers make direct transactions with each other, reducing the role of an intermediary).intermediary). The concept of using local energy markets in energy management systems in microgrids is presented in [15][16][17]. In [15] local [10energy markets are used, 47where microgrids can trade with each other under the direction of the local energy market manager (LEMM), 48]minimizing operating costs. IA decentralized two-level model is used here, in which the microgrid decision-making problems are first-level problems, and the LEM billing problem is a second-level problem that is solved using an [10]iterative local energy markets are used, where microgrids can trade with each other under the direction of the local energy market manager (LEMM), minimizing operating costs. A decentralized two-level model is used here, in which the microgrid decision-making problems are first-level problems, and the LEM billing problem is a second-level problem that is solved using an iterative algorithm.algorithm. Article [47][16] presents the concept of a smart energy supplier (SESP) acting as a local energy market operator and an aggregator of prosumers to participate in the day-ahead and balancing markets. Energy prices on local markets are settled by premiums in relation to the day-ahead market prices, which is supposed to be a form of incentive to exchange energy between users. Studies on a stochastic model that take into account the production of energy from PV and changes in market prices indicate that such a local market can increase the local use of renewable energy and reduces the amount of energy purchased from presents the concept of a smart energy supplier (SESP) acting as a local energy market operator and an aggregator of prosumers to participate in the day-ahead and balancing markets. Energy prices on local markets are settled by premiums in relation to the day-ahead market prices, which is supposed to be a form of incentive to exchange energy between users. Studies on a stochastic model that take into account the production of energy from PV and changes in market prices indicate that such a local market can increase the local use of renewable energy and reduces the amount of energy purchased from outside.outside. The implementation of local energy balancing systems in the low-voltage distribution network was dealt with by the authors of publication [48][17],  using the rules of the competitive electricity market (including unbundling, i.e., separating the energy transmission/distribution process from its sale and generation), including auxiliary services (ASs), the economic disposition and optimization of ASs, and the compensation of reactive power and harmonics as well as asymmetry. According to the proposed concept, the local balancing operation is ensured by the so-called node area operator (NAO), and the installations involved in balancing, due to the extensive monitoring system, can be controlled via the Internet or any dedicated form of signal. The power demand of a given area is covered by the local distributed generation and the medium-voltage power grid. The work schedule of the generating units is prepared on the basis of balancing offers submitted by the generators to the operator of the local area. Management and support service offerings are used for optimization through non-linear programming with constraints (sequential quadratic programming (SQP) algorithm). The function of the optimization objective is the cost of local energy balancing and the cost of auxiliary services (reactive power compensation, harmonic leveling, etc.). Simulation studies have confirmed that the introduction of local energy balancing and energy production management gives the possibility of a wider use of energy sources connected to the low-voltage grid, while maintaining grid limitations [48 ][17]. The new concept is transactive systems (transactive energy (TE)), which, according to [49][18] are defined as “systems of economic and control mechanisms (energy management) that allow for a dynamic balance of supply and demand in the entire power infrastructure, using the value of as a key operational parameter”. This approach promotes local energy systems, departing from the hierarchical structure, which introduces the legitimacy of dividing the network into microgrids. TE uses advanced agents and protocols to manage and coordinate energy in the market [50, 51][19][20]. Several scientific activities/projects were created (USA, Europe, and India) in which the functioning and legitimacy of introducing transactive systems were examined [51, 52][20][21]. The Pacific Northwest Smart Grid Demonstration (PNWSGD) project was created in the United States in the context of the development of regional power grids. The five-year project involved 11 utilities, 2 universities and a number of companies in 5 states (Washington, Oregon, Idaho, Montana, Wyoming) [51][20]. This area is extensive and strategic, as it includes the BPA (Bonneville Power Administration) transmission system and generation units (hydro, wind, gas and nuclear power plants) [52][21]. The main objectives of the project included improving the reliability and security of energy supplies, improving efficiency and responding to current demand [53][22]. WAs part of the activities, 55 technical solutions were tested that could contribute to reducing energy consumption and costs related to its use (including smart meters, batteries, and voltage control systems) and an innovative transactive system was implemented to coordinate many distributed generation units [53 , 54][
  • 29][30][31];
  • Profit maximization [14 , 63 , 64 , 65 , 66 , 67 , 68][32][33][34][35][36][37][38];
  • muti-criteria objective function (a combination of the two above and, for example, the minimization of greenhouse gas production, minimization of power losses in the distribution network  [69][39], or maximization of self-sufficiency and economic return  [33] [40]).

The key part of energy management in the implementation of the objective function is to define constraints, e.g., those related to ensuring the balance of power in each period, distributed generation, the operation of energy storage [61][30], the selection of advanced optimization methods [70][41] and forecasting.

There are many management techniques based on optimization [71, 72][42][43]. In a review article [72][43], a division of optimization methods used to solve energy problems was presented in a very meticulous way. Basically, in [72][43],  the following types of optimization are distinguished:

  • Combinatorial, in which the following can be distinguishedć:
    • Exact optimization (branch and bound, and dynamic programming);
    • Approximation (including metaheuristic algorithms and random search).
  • Continuous, including the following:
    • Linear programming (simplex method and interior-point method);
    • Non-linear programming (local and global search).
Another class of methods are methods based on artificial intelligence, fuzzy logic, machine learning and artificial neural networks [72 , 73] [43][44].

References

  1. Holechek, J.L.; Geli, H.M.E.; Sawalhah, M.N.; Valdez, R.A. Global Assessment: Can Renewable Energy Replace Fossil Fuels by 2050? Sustainability 2022, 14, 4792.
  2. Masson-Delmotte, V.; Zhai, P.; Pörtner, H.-O.; Roberts, D.; Skea, J.; Shukla, P.R.; Pirani, A.; Moufouma-Okia, W.; Péan, C.; Pidcock, R.; et al. Global Warming of 1.5 °C; International Panel on Climate Change: Geneva, Switzerland, 2018.
  3. Ripple, W.J.; Wolf, C.; Newsome, T.W.; Gregg, J.W.; Lenton, M.; Barnard, P.; Moomaw, W.R. World scientists warning of climate emergency 2021. BioScience 2021, 71, 894–898.
  4. World Population Clock. Available online: https://www.worldometers.info/world-population/ (accessed on 8 April 2023).
  5. Berahab, R. Global Trends in the Energy Sector and Their Implication on Energy Security in NATO’s Southern Neighbourhood; Elcano Royal Institute: Madrid, Spain, 8 September 2020.
  6. EU Commission Task Force for Smart Grids. Expert Group 1: Functionalities of Smart Grids and Smart Meters; European Commission: Brussels, Belgium, 22 June 2010.
  7. Bayindir, R.; Colak, I.; Fulli, G.; Demirtas, K. Smart grid technologies and applications. Renew. Sustain. Energy Rev. 2016, 66, 499–516.
  8. Bieliński, K. Monitoring as an energy management supporting tool. Rynek Energii 2012, 6, 10–14. (In Polish)
  9. Moghaddasi, H.; Culp, C.; Vanegas, J. Net Zero Energy Communities: Integrated Power System, Building and Transport Sectors. Energies 2021, 14, 7065.
  10. International Energy Agency (IEA). Net Zero by 2050: A Roadmap for the Global Energy Sector. May 2021. Available online: https://www.iea.org/reports/net-zero-by-2050 (accessed on 17 April 2023).
  11. Carlisle, N.; van Geet, O.; Pless, S. Definition of a ’Zero Net Energy’ Community; National Renewable Energy Lab. (NREL): Golden, CO, USA, 2009.
  12. Peterson, K.; Torcellini, P.; Grant, R. A Common Definition for Zero Energy Buildings; Department of Energy (DOE): Washington, DC, USA, 2015.
  13. Hu, H.; Sun, F.; Guo, W.; Pan, L. Net-Zero Intelligent Energy System: Road to a Successful Carbon Neutral Future. In Proceedings of the 12th International Conference on Power, Energy and Electrical Engineering (CPEEE), Shiga, Japan, 25–27 February 2022; pp. 285–291.
  14. Santos, G.; Faia, R.; Pereira, H.; Pinto, T.; Vale, Z. Blockchain-based Local Electricity Market Solution. In Proceedings of the 2022 18th International Conference on the European Energy Market (EEM), Ljubljana, Slovenia, 13–15 September 2022; pp. 1–7.
  15. Sheikhahmadi, P.; Bahramara, P.S.; Shahrokhi, S.; Chicco, G.; Mazza, A.; Catalão, J.P.S. Modeling Local Energy Market for Energy Management of Multi-Microgrids. In Proceedings of the 55th International Universities Power Engineering Conference (UPEC), Turin, Italy, 1–4 September 2020; pp. 1–6.
  16. Crespo-Vazquez, J.L.; Sanchez de la Nieta, A.A.; Gibescu, M. A decision-making framework encouraging local energy exchanges among smart buildings. In Proceedings of the 2019 International Conference on Smart Energy Systems and Technologies (SEST), Porto, Portugal, 9–11 September 2019; pp. 1–6.
  17. Olek, B.; Wierzbowski, M. Local Energy Balancing and Ancillary Services in Low-Voltage Networks with Distributed Generation, Energy Storage, and Active Loads. IEEE Trans. Ind. Electron. 2015, 62, 2499–2508.
  18. Melton, R.B. GridWise Transactive Energy Framework Ver. 1.1. No. PNNL-22946 Ver. 1.1; Pacific Northwest National Lab. (PNNL): Richland, WA, USA, 2019.
  19. Tejaswini, Y.; Jain, P. Transactive Energy Management of Solar Integrated Villages In India. In Proceedings of the 2021 IEEE 2nd International Conference on Electrical Power and Energy Systems (ICEPES), Bhopal, India, 10–11 December 2021; pp. 1–6.
  20. El-Batawy, S.A.; Morsi, W.G. Integration of Prosumers with Battery Storage and Electric Vehicles via Transactive Energy. IEEE Trans. Power Deliv. 2022, 37, 383–394.
  21. Hammerstrom, D.J.; Widergren, S.E.; Irwin, C. Evaluating Transactive Systems: Historical and Current U.S. DOE Research and Development Activities. IEEE Electrif. Mag. 2016, 4, 30–36.
  22. Melton, R. Pacific Northwest Smart Grid Demonstration Project Technology Performance Report Volume 1: Technology Performance; Pacific Northwest National Lab. (PNNL): Richland, WA, USA, 2019.
  23. Smart Stuff: IQ of Northwest Power Grid Raised, Energy Saved. Available online: https://www.pnnl.gov/news/release.aspx?id=4210 (accessed on 2 May 2023).
  24. Widergren, S.E.; Subbarao, K.; Fuller, J.C.; Chassin, D.P.; Somani, A.; Marinovici, M.C.; Hammerstrom, J.L. AEP Ohio gridSMART Demonstration Project Real-Time Pricing Demonstration Analysis; Pacific Northwest National Lab. (PNNL): Richland, WA, USA, 2014.
  25. AEP gridSMART Demonstration Project. Available online: https://www.pnnl.gov/projects/transactive-systems-program/aep-gridsmart-demonstration-project (accessed on 2 May 2023).
  26. Power MatcherSuite Transactive Energy. Available online: https://flexiblepower.github.io/ (accessed on 2 May 2023).
  27. Saboori, H.; Mohammadi, M.; Taghe, R. Virtual Power Plant (VPP), Definition, Concept, Components and Types. In Proceedings of the 2011 Asia-Pacific Power and Energy Engineering Conference, Wuhan, China, 25–28 March 2011; pp. 1–4.
  28. Liang, Z.; Alsafasfeh, Q.; Jin, T.; Pourbabak, H.; Su, W. Risk-Constrained Optimal Energy Management for Virtual Power Plants Considering Correlated Demand Response. IEEE Trans. Smart Grid 2017, 10, 1577–1587.
  29. Ullah, Z.; Hassanin, H. Modeling, optimization, and analysis of a virtual power plant demand response mechanism for the internal electricity market considering the uncertainty of renewable energy sources. Energies 2022, 15, 5296.
  30. Kasaei, M.J.; Gandomkar, M.; Nikoukar, J. Optimal management of renewable energy sources by virtual power plant. Renew. Energy 2017, 114, 1180–1188.
  31. Zepter, J.M.; Lüth, A.; Crespo del Granado, P.; Egging, R. Prosumer integration in wholesale electricity markets: Synergies of peer-to-peer trade and residential storage. Energy Build. 2019, 184, 163–176.
  32. Du, Y.; Li, F. Integrating a Multi-microgrid System into Real-time Balancing Market: Problem Formulation and Solution Technique. In Proceedings of the 2018 IEEE Power & Energy Society General Meeting (PESGM), Portland, OR, USA, 5–10 August 2018; pp. 1–5.
  33. Sadeghi, S.; Jahangir, H.; Vatandoust, B.; Golkar, M.A.; Ahmadian, A.; Elkamel, A. Optimal bidding strategy of a virtual power plant in day-ahead energy and frequency regulation markets: A deep learning-based approach. Int. J. Electr. Power Energy Syst. 2021, 127, 106646.
  34. Hooshmand, R.-A.; Nosratabadi, S.M.; Gholipour, E. Event-based scheduling of industrial technical virtual power plant considering wind and market prices stochastic behaviors—A case study in Iran. J. Clean. Prod. 2018, 172, 1748–1764.
  35. Chen, G.; Li, J. A fully distributed ADMM-based dispatch approach for virtual power plant problems. Appl. Math. Model. 2018, 58, 300–312.
  36. Shabanzadeh, M.; Sheikh-El-Eslami, M.-K.; Haghifam, M.-R. The design of a risk-hedging tool for virtual power plants via robust optimization approach. Appl. Energy 2015, 155, 766–777.
  37. Gougheri, S.S.; Dehghani, M.; Nikoofard, A.; Jahangir, H.; Golkar, M.A. Economic assessment of multi-operator virtual power plants in electricity market: A game theory-based approach. Sustain. Energy Technol. Assess. 2022, 53, 102733.
  38. Ullah, Z.; Hassanin, H.; Cugley, J.; Alawi, M.A. Planning, Operation, and Design of Market-Based Virtual Power Plant Considering Uncertainty. Energies 2022, 15, 7290.
  39. Ju, L.; Tan, Q.; Lu, Y.; Tan, Z.; Zhang, Y.; Tan, Q. A CVaR-robust-based multi-objective optimization model and three-stage solution algorithm for a virtual power plant considering uncertainties and carbon emission allowances. Int. J. Electr. Power Energy Syst. 2019, 107, 628–643.
  40. Ciocia, A.; Amato, A.; Di Leo, P.; Fichera, S.; Malgaroli, G.; Spertino, F.; Tzanova, S. Self-Consumption and Self-Sufficiency in Photovoltaic Systems: Effect of Grid Limitation and Storage Installation. Energies 2021, 14, 1591.
  41. Ahmadian, A.; Ponnambalam, K.; Almansoori, A.; Elkamel, A. Optimal Management of a Virtual Power Plant Consisting of Renewable Energy Resources and Electric Vehicles Using Mixed-Integer Linear Programming and Deep Learning. Energies 2023, 16, 1000.
  42. Abdulkader, R.; Ghanimi, H.M.A.; Dadheech, P.; Alharbi, M.; El-Shafai, W.; Fouda, M.M.; Aly, M.H.; Swaminathan, D.; Sengan, S. Soft Computing in Smart Grid with Decentralized Generation and Renewable Energy Storage System Planning. Energies 2023, 16, 2655.
  43. Pijarski, P.; Kacejko, P.; Miller, P. Advanced Optimisation and Forecasting Methods in Power Engineering—Introduction to the Special Issue. Energies 2023, 16, 2804.
  44. Ahmad, N.; Ghadi, Y.; Adnan, M.; Ali, M. Load Forecasting Techniques for Power System: Research Challenges and Survey. IEEE Access 2022, 10, 71054–71090.
More