Research on Optimal Configuration of Microgrid Energy Management Based on Particle Swarm Optimization with Cognitive Factor Change

Energy routers play a pivotal role in microgrids by intelligently managing and optimizing energy flow, enhancing microgrid stability and economics, and effectively promoting the utilization of renewable energy and the sustainable development of microgrids. Microgrid energy optimization is vital for energy routers to ensure efficient resource utilization, balance supply and demand, enhance stability, and integrate renewable sources effectively within the microgrid. A microgrid energy optimization scheme based on an improved cognitive factor algorithm is proposed. The paper takes an objective operating cost of the microgrid system as the optimization function, with power balance, energy storage balance, fuel generation balance, and constrained power as constraint conditions. An objective coordinated optimization configuration scheme for the microgrid is established. The discussion focuses on optimizing the algorithm for multiple objective functions by improving the values of cognitive factors and social factor (Cl and C2) in Particle Swarm Optimization iteration. Simulation result demonstrates that the method not only predicts the daily power curves of photovoltaic and wind power generation and alternating current load but also optimizes the operational state of batteries and fuel cells. It effectively reduces the overall operating cost of the microgrid and accelerates the convergence speed.