Optimization and Cost Analysis of Multi Microgrid Economic Scheduling Based on Particle Swarm Optimization Algorithm

This study proposes a particle swarm optimization (PSO)-based approach for multi-microgrid economic dispatch to minimize operational costs, including generation, energy trading, and battery degradation. The model integrates renewable energy sources (wind/PV), energy storage systems, fuel cells, and diesel generators, while considering constraints such as state-of-charge (SOC) limits, power balance, and transaction limits. With dynamic inertia weights and penalty-based constraint handling, PSO optimizes 120-dimensional decision variables across a 24-h horizon. Simulation results demonstrate significant cost reduction, enhanced energy efficiency, and stable SOC management, validating PSO’s effectiveness in complex, nonlinear microgrid optimization.