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

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), multi-objective 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.