Optimization of microgrid scheduling based on multi-strategy improved MOPSO algorithm

Yang Xue; Shiwei Liang; Fengwei Qian; Jinyi Tang

doi:10.1016/j.gloei.2025.11.001

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Optimization of microgrid scheduling based on multi-strategy improved MOPSO algorithm

Special Issue on Synergistic Optimization of Sustainable Energy Systems and Smart Grids | 更新时间：2026-02-03

- Optimization of microgrid scheduling based on multi-strategy improved MOPSO algorithm
- Global Energy Interconnection Vol. 8, Issue 6, (2025)
- 作者机构：
  
  1. School of Automation Engineering, Shanghai University of Electric Power,Shanghai,PR China,200090
  2. Shanghai Solar Energy Engineering Technology Research Center,Shanghai,PR China,200241
- 作者简介：
- 基金信息：
- DOI：10.1016/j.gloei.2025.11.001
  CLC：
- Published：2025
- 稿件说明：
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Yang Xue, Shiwei Liang, Fengwei Qian, et al. Optimization of microgrid scheduling based on multi-strategy improved MOPSO algorithm[J]. Global Energy Interconnection, 2025, 8(6).
DOI：

Yang Xue, Shiwei Liang, Fengwei Qian, et al. Optimization of microgrid scheduling based on multi-strategy improved MOPSO algorithm[J]. Global Energy Interconnection, 2025, 8(6). DOI： 10.1016/j.gloei.2025.11.001.

摘要

Abstract

A multi-strategy Improved Multi-Objective Particle Swarm Algorithm (IMOPSO) method for microgrid operation optimization is proposed for the coordinated optimization problem of microgrid economy and environmental protection.A grid-connected microgrid model containing photovoltaic cells

wind power

micro gas turbine

diesel generator

and storage battery is constructed with the aim of optimizing the multi-objective grid-connected microgrid economic optimization problem with minimum power generation cost and environmental management cost.Based on the optimization of the standard multi-objective particle swarm optimization algorithm

four strategies are introduced to improve the algorithm

namely

Logistic chaotic mapping

adaptive inertia weight adjustment

adaptive meshing using congestion distance mechanism

and fuzzy comprehensive evaluation.The proposed IMOPSO is applied to the microgrid optimization problem and the performance is compared with other unimproved multi-objective gray wolf algorithm (MOGWO)

multiobjective ant colony algorithm (MOACO)

and MOPSO algorithms

and the total cost of the proposed method is reduced by 3.15%

8.34%

and 10.27%

respectively.The simulation results show that IMOPSO can more effectively reduce the cost and optimize power distribution

and verify the effectiveness of the proposed method.

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