Distributed energy planning is a complex issue, and the non-dominated sorting genetic algorithm (NSGA-II) is widely employed for the system multi-objective optimization. This algorithm screens the intermediate population based on the fixed crowding distance principle, it does not consider the dynamic crowding change and cannot satisfy the diverse search requirements of solution space in different evolutionary periods. In this paper, an improved NSGA-II method based on dynamic crowding distance and information entropy is proposed. Then a case study of a wind-solar integrated microgrid system is implemented, by refereeing the local meteorological conditions and power loads in Yunnan of China, the renewable energy system under study is optimized in terms of the system energy efficiency, energy volatility and net present cost. Results indicate that the energy system achieves a lower cost after optimization by the improved NSGA-II method, and the matching degree of renewable energy generation and electricity demand is evidently enhanced which means a better system operation stability. Comparing to the general NSGA-II, the improved algorithm also has superiority in convergence speed, and the research findings provide an alternative method for optimizing the renewable microgrid system.
Keywords NSGA-II, wind-solar microgrid, dynamic crowding distance, information entropy, multi-objective optimization