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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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
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| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025024995 |
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| Summary: | 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. |
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| ISSN: | 2590-1230 |