Multi-Objective Capacity Configuration Optimization of Hybrid Power Systems Based on Enhanced Differential Evolution Algorithm

Jianguo Shi, Inner Mongolia Power (Group) Co., Ltd., Inner Mongolia Electric Power Dispatching and Control Branch, Hohhot, 010010, Inner Mongolia Autonomous Region, China

Qianpeng Hao, Inner Mongolia Power (Group) Co., Ltd., Inner Mongolia Electric Power Dispatching and Control Branch, Hohhot, 010010, Inner Mongolia Autonomous Region, China

Xinjian Wang, Inner Mongolia Power (Group) Co., Ltd., Inner Mongolia Electric Power Dispatching and Control Branch, Hohhot, 010010, Inner Mongolia Autonomous Region, China

Zhibin Jing, Inner Mongolia Power (Group) Co., Ltd., Inner Mongolia Electric Power Dispatching and Control Branch, Hohhot, 010010, Inner Mongolia Autonomous Region, China

Yan-Kai Zhu, North China Electric Power University, School of Control and Computer Engineering, Beijing, 102202, China

Qing-Kui Li, Beijing Information Science & Technology University, School of Automation, Beijing, 102206, China

Abstract
Scientific and reasonable configuration of the capacity of wind power, photovoltaic and energy storage systems in hybrid power system (HPS) is an important prerequisite to realize its comprehensive benefits and promote the development of new energy sources, and it is also a hotspot of current research. In this paper, we improve the multi-objective differential evolution algorithm for HPS multi-objective capacity allocation optimization, and dynamically adjust the differential evolution strategy through Q-Learning to realize the adaptive selection of variant strategies, so as to enhance the reasonableness and accuracy of the optimization results. The study constructs a mathematical model that takes into account the economic (net present value) and reliability (power supply shortage index) objectives, and builds a wind-optical-storage hybrid system using AC coupling to deal with the uncertainty of new energy output and load demand, and verifies the validity of the model and methodology through typical regional examples to provide decision support for the planning of new energy power systems.

Keywords
Multi-Objective optimization; power system; capacity configuration; differential evolution algorithm

Cite this paper
Jianguo Shi, Qianpeng Hao, Xinjian Wang, Zhibin Jing, Yan-Kai Zhu, Qing-Kui Li, Multi-Objective Capacity Configuration Optimization of Hybrid Power Systems Based on Enhanced Differential Evolution Algorithm , SCIREA Journal of Computer. Volume 10, Issue 3, June 2025 | PP. 48-57. 10.54647/computer520463

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