Optimal operation strategy of wind-hydrogen integrated energy system based on NSGA-II algorithm

Teng Sun, Weidong Wang, X. Wen
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Abstract

In order to improve the economy of the multi energy system and the efficiency of energy utilization, the research adopts the non dominated sorting genetic algorithms II (NSGA-II) to expand the population space. The elite strategy is introduced to improve the intelligent algorithm, and then the diversity of the population is retained to improve the optimization accuracy of the algorithm. In addition, the adaptive operator is introduced to improve the NSGA-II algorithm to improve the global search efficiency. The performance test of fast non dominated sorting genetic algorithm shows that the improved algorithm using elite strategy has better performance in coverage index, diversity index and convergence index. For example, in terms of convergence index, the improved NSGA-II algorithm has improved 0.0159, 0.822, 0.0243 and 0.0171 in four ZDT test functions. On the energy optimization operation for the integration of wind and hydrogen, the improved NSGA-II algorithm has obtained lower cost, with a total configuration cost of 606 million yuan, while the total system configuration cost corresponding to the unimproved NSGA-II algorithm is 624 million yuan, so the total system cost after the algorithm improvement has decreased by 18 million yuan. Therefore, this method has better economy and higher energy efficiency.
基于NSGA-II算法的风-氢一体化能源系统优化运行策略
为了提高多能系统的经济性和能源利用效率,本研究采用非支配排序遗传算法II (NSGA-II)扩展种群空间。引入精英策略对智能算法进行改进,同时保留种群的多样性,提高算法的优化精度。此外,引入自适应算子对NSGA-II算法进行改进,提高了全局搜索效率。对快速非支配排序遗传算法的性能测试表明,采用精英策略的改进算法在覆盖度指标、多样性指标和收敛性指标上具有更好的性能。例如,在收敛指标方面,改进的NSGA-II算法在4个ZDT测试函数中分别提高了0.0159、0.822、0.0243和0.0171。在风氢融合能源优化运行中,改进后的NSGA-II算法获得了更低的成本,总配置成本为6.06亿元,而未改进的NSGA-II算法对应的系统总配置成本为6.24亿元,因此算法改进后的系统总成本降低了1800万元。因此,该方法具有较好的经济性和较高的能源效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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