Multi-Objective Optimal Scheduling for Microgrids—Improved Goose Algorithm

Yongqiang Sun, Xianchun Wang, Lijuan Gao, Haiyue Yang, Kang Zhang, Bingxiang Ji () and Huijuan Zhang
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Yongqiang Sun: State Grid Hengshui Electric Power Supply Company, Hengshui 053300, China
Xianchun Wang: State Grid Hengshui Electric Power Supply Company, Hengshui 053300, China
Lijuan Gao: State Grid Hengshui Electric Power Supply Company, Hengshui 053300, China
Haiyue Yang: State Grid Hengshui Electric Power Supply Company, Hengshui 053300, China
Kang Zhang: State Grid Hengshui Electric Power Supply Company, Hengshui 053300, China
Bingxiang Ji: State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401, China
Huijuan Zhang: State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401, China

Energies, 2024, vol. 17, issue 24, 1-29

Abstract: Against the background of the dual challenges of global energy demand growth and environmental protection, this paper focuses on the study of microgrid optimization and scheduling technology and constructs a smart microgrid system integrating energy production, storage, conversion, and distribution. By integrating high-precision load forecasting, dynamic power allocation algorithms, and intelligent control technologies, a microgrid scheduling model is proposed. This model simultaneously considers environmental protection and economic efficiency, aiming to achieve the optimal allocation of energy resources and maintain a dynamic balance between supply and demand. The goose optimization algorithm (GO) is innovatively introduced and improved, enhancing the algorithm’s ability to use global search and local fine search in complex optimization problems by simulating the social aggregation of the goose flock, the adaptive monitoring mechanism, and the improved algorithm, which effectively avoids the problem of the local optimal solution. Meanwhile, the combination of super-Latin stereo sampling and the K-means clustering algorithm improves the data processing efficiency and model accuracy. The results demonstrate that the proposed model and algorithm effectively reduce the operating costs of microgrids and mitigate environmental pollution. Using the improved goose algorithm (IGO), the combined operating and environmental costs are reduced by 16.15%, confirming the model’s effectiveness and superiority.

Keywords: microgrid optimization; multi-objective; improved goose algorithm; economic operation; environmentally friendly (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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