Multiobjective Optimization Configuration of a Prosumer’s Energy Storage System Based on an Improved Fast Nondominated Sorting Genetic Algorithm

With the deepening of “source-load-storage” interaction and development demand response technology, emergence prosumers has led to new vitality potential for optimal operation microgrids. By implementing a mechanism prosumers, peak shaving valley filling are realized, load fluctuations balanced. However, high costs investing operating energy storage system (ESS) restrict their ability participate in scheduling In this paper, objectives obtaining lowest comprehensive smallest fluctuations, an INSGA-II (Improved Fast Nondominated Sorting Genetic Algorithm) algorithm is proposed multiobjective configuration optimization model prosumer's ESS. To ensure diversity population improve search space, based on original NSGA-II algorithm, proportion factor set selection strategy improved. The normal distribution crossover operator introduced process, local chaotic added after formation next generation population. An example science technology park with five users simulated analyzed. Upon comparison various typical intelligent algorithms, results show that performance improved best. At same time, multiple calculation strong algorithmic stability.