Research on an Economic Dispatch Method for Wind-Solar-Storage Microgrids Based on an Improved Particle Swarm Optimization Algorithm –

Addressing the limitations of the traditional particle swarm optimization (PSO) algorithm—such as its susceptibility to premature convergence, inadequate global optimization capability, and poor robustness when solving problems under multiple constraints—in the context of economic dispatch for wind-solar-storage microgrids, this paper proposes an improved PSO algorithm combined with a segmented nonlinear inertia weight scheme and the Metropolis criterion for optimization computations. This study establishes a microgrid dispatch optimization model that fully accounts for the charging/discharging efficiency of energy storage systems, the upper and lower bounds of the State of Charge (SOC), and the capacity degradation characteristics of the systems. The model takes the overall system operating cost as its primary optimization objective while also considering the integration efficiency of wind and solar renewable energy sources, thereby coordinating both global search and local fine-tuning during the algorithm"s iterative phase. The Metropolis simulated annealing criterion is introduced, leveraging the concept of probabilistic acceptance of suboptimal solutions to mitigate the algorithm"s tendency to get trapped in local optima. Multiple comparative simulation results demonstrate that this improved algorithm can effectively reduce the overall operating costs of microgrids, enhance the utilization efficiency of wind and solar energy, achieve faster convergence, and yield solutions with lower fluctuation; thus, it provides a practical reference for microgrid dispatching in the current landscape of renewable energy integration.