Configuration and Operation Optimization in Microgrid Considering Source and Load Uncertainty (CICED 2024)

Guided by the “carbon peak” and “carbon neutrality” policies, clean energy will rapidly develop. In microgrid systems (MGs) with renewable sources, the variability of wind and solar outputs combined with load uncertainty complicates the rational design of system capacity configuration and operational strategies. The paper addresses the uncertainty of source and load through scenario generation and reduction, constructing a two-layer optimization model for MG configuration and operation. The upper layer employs Particle Swarm Optimization (PSO) for configuration optimization, while the lower layer uses Genetic Algorithm (GA) for operational strategies. Finally, the reliability and economy of this dual-layer optimization model are confirmed through simulation studies on both islanded and grid-connected MG configurations and operational strategy. Furthermore, the paper further elucidates the impact of source and load uncertainty on the configuration and operational results of the MG.