Multi-Objective Optimal Scheduling of Microgrids Based on Improved Particle Swarm Algorithm

Zhong Guan, Hui Wang, Zhi Li, Xiaohu Luo (), Xi Yang, Jugang Fang and Qiang Zhao
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Zhong Guan: Wudian New Energy Co., Ltd. of Wuhu City, Wuhu 241012, China
Hui Wang: Wudian New Energy Co., Ltd. of Wuhu City, Wuhu 241012, China
Zhi Li: Wudian New Energy Co., Ltd. of Wuhu City, Wuhu 241012, China
Xiaohu Luo: Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610000, China
Xi Yang: Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610000, China
Jugang Fang: Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610000, China
Qiang Zhao: Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610000, China

Energies, 2024, vol. 17, issue 7, 1-20

Abstract: Microgrid optimization scheduling, as a crucial part of smart grid optimization, plays a significant role in reducing energy consumption and environmental pollution. The development goals of microgrids not only aim to meet the basic demands of electricity supply but also to enhance economic benefits and environmental protection. In this regard, a multi-objective optimization scheduling model for microgrids in grid-connected mode is proposed, which comprehensively considers the operational costs and environmental protection costs of microgrid systems. This model also incorporates improvements to the traditional particle swarm optimization (PSO) algorithm by considering inertia factors and particle adaptive mutation, and it utilizes the improved algorithm to solve the optimization model. Simulation results demonstrate that this model can effectively reduce electricity costs for users and environmental pollution, promoting the optimized operation of microgrids and verifying the superior performance of the improved PSO algorithm. After algorithmic improvements, the optimal total cost achieved was CNY 836.23, representing a decrease from the pre-improvement optimal value of CNY 850.

Keywords: microgrid; multi-objective; improved particle swarm algorithm; optimal scheduling (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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