Abstract
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.
Highlights
At present, with the diversification of energy usage patterns and the pursuit of users’ electricity experiences, prosumers with bidirectional power regulation characteristics are constantly emerging [1], and these has brought new vitality to modern microgrid optimization scheduling [2], [3]
Considering the comprehensive cost and load fluctuation as the optimization objectives, the energy storage system (ESS) capacity and charging-discharging scheduling are optimized according to the time-of-use price and load curves, and the problem is solved by the INSGA-II algorithm
Based on the traditional NSGA-II algorithm, the new algorithm improves the proportional factor in the selection strategy, and this can reduce the number of repeated individuals and increase the diversity of the population
Summary
With the diversification of energy usage patterns and the pursuit of users’ electricity experiences, prosumers with bidirectional power regulation characteristics are constantly emerging [1], and these has brought new vitality to modern microgrid optimization scheduling [2], [3]. According to the load level and the electricity price issued by the microgrid, by comprehensively considering the investment cost and operational cost of energy storage in the prosumer, the objective function of these costs and the load fluctuation is established to optimize power capacity and energy capacity and obtain the charging-discharging strategy for energy storage. When preparing to configure ESS for prosumers, it is necessary to design an effective capacity configuration model that considers the electricity price, investment cost and influence of load forecasting according to different application scenarios and prosumer demands As it is affected by many factors, the configuration of an ESS for a prosumer is a multiobjective optimization problem. Because of the special mathematical form of the above model, it is difficult to obtain the analytical solution by using a numerical method, so an intelligent algorithm is introduced for complex models
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