Multi-Objective Capacity Optimal Configuration of Source-Grid-Load-Storage System Based on Improved Particle Swarm Optimization Algorithm

Against the backdrop of the rapid development of renewable energy, the coordinated and optimized configuration of sources, grids, loads, and storages has become a key topic for enhancing the reliability and economy of power systems. This study proposes a Particle Swarm Optimization algorithm with a dynamic inertia weight mechanism (DWM-PSO). Through constructing a two-layer optimization model, the comprehensive optimization of the capacity configuration of the source-grid-load-storage system is achieved. This method introduces a nonlinear adaptive inertia weight adjustment mechanism into the traditional particle swarm algorithm, effectively balancing the global search and local development capabilities. Meanwhile, a multi-objective optimization model is established, with minimizing the investment cost as the core, taking into account the power supply reliability and the consumption rate of renewable energy. The simulation experiment takes a typical microgrid as a case. The comparative analysis shows that compared with the standard particle swarm algorithm and the genetic algorithm, the multi-objective optimization method based on the improved PSO algorithm can not only effectively realize the capacity configuration of the source-grid-load-storage system, but also significantly improve the convergence speed and the quality of the solution, providing a new optimization tool for power system planning and operation.