Economic-Environmental Cooperative Optimal Scheduling of Wind-Solar Storage Microgrids Based on Improved MOPSO

We propose an optimization method based on an improved multi-objective particle swarm optimization (MOPSO) algorithm for the economic-environmental cooperative optimal scheduling of a wind-solar storage microgrid. By introducing a dynamic weight adjustment mechanism and an elite retention strategy, the method effectively balances operating costs and carbon emission objectives. A multi-energy microgrid test system comprising photovoltaic panels, wind turbines, pumped storage, and a micro gas turbine is constructed for this study. Simulation tests are conducted using typical daily data from the IEEE Reliability Test System (IEEE-RTS). The results demonstrate that the proposed method achieves an 8.5% reduction in operating costs and a 23% decrease in carbon emissions. This study provides a technical pathway for the intelligent optimization and control of new power systems and serves as an important reference for achieving the Dual Carbon Target.