Economic and environmental power dispatch for energy management systems applied to microgrids with wind energy resources and battery energy storage systems
This paper presents a new economic and environmental power dispatch approach for the energy management of alternating current microgrids integrated with distributed wind energy resources and battery energy storage systems. This study proposes an algorithm for intelligent energy management that adapt...
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Elsevier
2025-09-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025024995 |
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| author | Jhon Montano John E. Candelo-Becerra Cristian Escudero-Quintero Juan Pablo Guzmán |
| author_facet | Jhon Montano John E. Candelo-Becerra Cristian Escudero-Quintero Juan Pablo Guzmán |
| author_sort | Jhon Montano |
| collection | DOAJ |
| description | This paper presents a new economic and environmental power dispatch approach for the energy management of alternating current microgrids integrated with distributed wind energy resources and battery energy storage systems. This study proposes an algorithm for intelligent energy management that adapts to inherent variations in wind energy resources, state of charge of batteries, and power demand. The problem is formulated to minimize variable and fixed generation costs, network power losses, and CO2 emissions of the microgrid with distributed energy resources, considering the constraints of the network, generation, and energy storage. To solve this problem, four metaheuristic optimization algorithms were implemented: Enhanced Prairie Dog Optimization (EPDO), Salp Swarm Algorithm (SSA), Generalized Normal Distribution Optimization (GNDO), and Crow Search Algorithm (CSA). A particle swarm optimization (PSO) algorithm was used to fine-tune the parameters of each algorithm, eliminating the need for manual adjustments and optimizing the quality and processing time. These algorithms are integrated into the objective functions and evaluated using a 24-hour power flow that incorporates a strict penalty scheme to satisfy the operational constraints. The problem was tested in a 33-node feeder system, and the best solutions found with the algorithms were compared to determine the performance to solve the problem. The results show that the approach ensures technical efficiency while minimizing economic and environmental requirements. The simulation results indicate that the SSA and GNDO algorithms outperform EPDO and CSA, achieving reductions of up to 2.001% in fixed costs, 4.684% in variable costs, 1.214% in CO2 emissions, and 6.084% in energy losses. The SSA stands out for its stability and processing efficiency. This promising model can be applied to urban and rural microgrids, as it offers a robust framework for energy management in alternating current systems. |
| format | Article |
| id | doaj-art-b2d198514eea48a8adf7cf80d99dfb4c |
| institution | DOAJ |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-b2d198514eea48a8adf7cf80d99dfb4c2025-08-20T02:47:28ZengElsevierResults in Engineering2590-12302025-09-012710643010.1016/j.rineng.2025.106430Economic and environmental power dispatch for energy management systems applied to microgrids with wind energy resources and battery energy storage systemsJhon Montano0John E. Candelo-Becerra1Cristian Escudero-Quintero2Juan Pablo Guzmán3Department of Electronics and Telecommunications, Institución Universitaria - ITM, Medellín 050028, Colombia; Corresponding author.Departamento de Energía Eléctrica y Automática, Universidad Nacional de Colombia Sede Medellín, Medellín 050030, ColombiaDepartment of Electronics and Telecommunications, Institución Universitaria - ITM, Medellín 050028, ColombiaDepartment of Electronics and Telecommunications, Institución Universitaria - ITM, Medellín 050028, ColombiaThis paper presents a new economic and environmental power dispatch approach for the energy management of alternating current microgrids integrated with distributed wind energy resources and battery energy storage systems. This study proposes an algorithm for intelligent energy management that adapts to inherent variations in wind energy resources, state of charge of batteries, and power demand. The problem is formulated to minimize variable and fixed generation costs, network power losses, and CO2 emissions of the microgrid with distributed energy resources, considering the constraints of the network, generation, and energy storage. To solve this problem, four metaheuristic optimization algorithms were implemented: Enhanced Prairie Dog Optimization (EPDO), Salp Swarm Algorithm (SSA), Generalized Normal Distribution Optimization (GNDO), and Crow Search Algorithm (CSA). A particle swarm optimization (PSO) algorithm was used to fine-tune the parameters of each algorithm, eliminating the need for manual adjustments and optimizing the quality and processing time. These algorithms are integrated into the objective functions and evaluated using a 24-hour power flow that incorporates a strict penalty scheme to satisfy the operational constraints. The problem was tested in a 33-node feeder system, and the best solutions found with the algorithms were compared to determine the performance to solve the problem. The results show that the approach ensures technical efficiency while minimizing economic and environmental requirements. The simulation results indicate that the SSA and GNDO algorithms outperform EPDO and CSA, achieving reductions of up to 2.001% in fixed costs, 4.684% in variable costs, 1.214% in CO2 emissions, and 6.084% in energy losses. The SSA stands out for its stability and processing efficiency. This promising model can be applied to urban and rural microgrids, as it offers a robust framework for energy management in alternating current systems.http://www.sciencedirect.com/science/article/pii/S2590123025024995Energy management systemEconomic and environmental dispatchWind energyEnergy storage systemOptimization algorithm |
| spellingShingle | Jhon Montano John E. Candelo-Becerra Cristian Escudero-Quintero Juan Pablo Guzmán Economic and environmental power dispatch for energy management systems applied to microgrids with wind energy resources and battery energy storage systems Results in Engineering Energy management system Economic and environmental dispatch Wind energy Energy storage system Optimization algorithm |
| title | Economic and environmental power dispatch for energy management systems applied to microgrids with wind energy resources and battery energy storage systems |
| title_full | Economic and environmental power dispatch for energy management systems applied to microgrids with wind energy resources and battery energy storage systems |
| title_fullStr | Economic and environmental power dispatch for energy management systems applied to microgrids with wind energy resources and battery energy storage systems |
| title_full_unstemmed | Economic and environmental power dispatch for energy management systems applied to microgrids with wind energy resources and battery energy storage systems |
| title_short | Economic and environmental power dispatch for energy management systems applied to microgrids with wind energy resources and battery energy storage systems |
| title_sort | economic and environmental power dispatch for energy management systems applied to microgrids with wind energy resources and battery energy storage systems |
| topic | Energy management system Economic and environmental dispatch Wind energy Energy storage system Optimization algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025024995 |
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