Optimization Strategy of Microgrid for Internal Self-balancing Target Based on particle swarm Optimization Algorithm

As a critical component of new-type power systems, microgrids have been investigated to address the escalating complexity of power balance, increased uncertainty in power flow distribution, and heightened security-stability requirements arising from high-penetration renewable energy integration. This study analyzes the developmental trends of intelligent microgrids in China and an optimization method for microgrid system including self-balancing targets of photovoltaic (PV) and storage batteries is proposed. Within the microgrid framework, an optimization model oriented toward internal self-balancing objectives is established. The model prioritizes minimizing the power balance deviation rate (PBDR) and energy balance deviation rate (EBDR) of the microgrid while concurrently incorporating operational costs and environmental benefits. Based on a regional 21-node system, the Particle Swarm Optimization (PSO) algorithm and CPLEX solver are employed to solve the model, yielding optimal planning and operational outcomes. The proposed methodology not only optimizes system performance but also effectively reduces the load peak-valley difference, thereby providing a theoretical foundation for evaluating PV accommodation capacity in distribution transformer areas.