Optimized Dispatch of Micro Energy Grid Based on Improved Quantum Particle Swarm Optimization Algorithm

Through the analysis of the typical CCHP structure and the consideration of the daily operation cost and environmental cost of the micro energy network, the wind-natural-storage combined synchronous generation model including electric, flue gas, air and hot water as the basic busbar is established. The optimization results of cold, heat and electric load scheduling of micro energy network in different scenarios are analyzed. By using the quantum particle swarm optimization algorithm (QPSO) to chaotic search, neighborhood mutation and weighted updating of the optimal position center of the population, the particles are effectively prevented from falling into the local optimal solution too early when searching, thus improving the population quality and convergence speed. The analysis results show that the proposed algorithm can effectively solve the CCHP model, reduce the operating cost and environmental cost under the reliable operation of the load demand and micro-energy network, improve the utilization rate of renewable energy, and realize the economic dispatch of the micro energy network.