Research on multi-objective optimal scheduling of DC microgrid based on improved particle swarm optimization

DC microgrids have become a crucial platform for renewable energy integration due to their advantages of high-efficiency power conversion and low line losses. However, their optimal scheduling faces challenges such as battery lifespan degradation and complex multi-energy coordination. This paper proposes a multi-objective optimization model that comprehensively considers battery lifespan, economic efficiency, and reliability, along with an improved particle swarm optimization (IPSO) algorithm for solving the problem. By incorporating a temperature-lifespan coupling model, a dynamic inertia weight strategy, and a multi-constraint handling mechanism, the study achieves refined scheduling of DC microgrids in low-temperature environments. Simulation results demonstrate that the proposed method reduces operational costs by 12.7% and decreases battery lifespan degradation by 18.3% compared to conventional algorithms, providing theoretical support for the efficient operation of DC microgrids.