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An Improved Multi-Objective Brain Storm Optimization Algorithm for Hybrid Microgrid Dispatch
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Author(s): Kai Zhang (CHN Energy Xinjiangganquanpu Comprehensive Energy Co., Ltd., China)and Zi Tang (CHN Energy Xinjiangganquanpu Comprehensive Energy Co., Ltd., China)
Copyright: 2024
Volume: 15
Issue: 1
Pages: 21
Source title:
International Journal of Swarm Intelligence Research (IJSIR)
Editor(s)-in-Chief: Yuhui Shi (Southern University of Science and Technology (SUSTech), China)
DOI: 10.4018/IJSIR.336530
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Abstract
The increasing integration of renewable energy sources into microgrids has led to challenges in achieving daily optimal scheduling for hybrid alternating current/direct current microgrids (HMGs). To solve the problem, this article presents a novel hybrid AC/DC microgrid scheduling method based on an improved brain storm optimization (BSO) algorithm. Firstly, with economic and energy storage device health as the primary objective functions, this paper proposes a dispatch model for AC-DC hybrid microgrids. To overcome the limitations of traditional algorithms, including premature convergence and can only find non-inferior solution sets, this article proposes a multi-objective BSO algorithm that integrates learning and selection strategies. Additionally, a fuzzy decision-making method is employed to achieve optimal daily dispatching and select the most suitable compromise solution. Finally, experiments are conducted to verify the effectiveness of the proposed multi-objective optimal scheduling method and to demonstrate the practicality and effectiveness of the method in real application scenarios.
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