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

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

Automated summary

This study proposes an Improved Fast Nondominated Sorting Genetic Algorithm (INSGA-II) for the multiobjective configuration optimization of prosumer Energy Storage Systems (ESS). By implementing a demand response mechanism, peak shaving and valley filling can be achieved to balance load fluctuations. The results show that the algorithm has the best performance and strong algorithmic stability.