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基于改进NSGA-Ⅱ的混合储能系统容量优化配置OA北大核心CSTPCD

Capacity optimization allocation of hybrid energy storage system based on improved NSGA-II algorithm

中文摘要英文摘要

针对光伏发电在微电网中的间歇性和不稳定性问题,利用全钒液流电池(vanadium redox batteries,VRB)和超级电容(super capacitors,SC)组成的混合储能系统平抑独立微电网中的功率波动,以提高微电网的供电可靠性.考虑到混合储能系统的容量配置问题,建立最小化混合储能系统年均成本和负荷缺电率的多目标混合储能系统容量优化模型.针对传统精英非支配解排序遗传算法(non-dominated solution sorting genetic algorithm,NSGA-Ⅱ)求解多目标优化问题时局部搜索能力较差,提出一种基于改进精英保留策略的NSGA-Ⅱ.该算法通过引入新的适应度函数进行排序,合理保留优秀个体,提升算法的优化效果,从而提升局部搜索能力,不断逼近Pareto实前沿,获得更优的容量配置方案.最后,通过算例验证了所提算法的合理性.

Against the shortcomings of intermittency and instability of photovoltaic power generation in microgrids,a hybrid energy storage system composed of vanadium redox batteries(VRB)and super capacitors(SC)is utilized to smooth out the power fluctuations in standalone microgrids,thus to improve the power supply reliability of standalone microgrids.Considering the capacity allocation problem of the hybrid energy storage system,a multi-objective hybrid energy storage system capacity optimization model that minimizes the average annual cost of the hybrid energy storage system and the load shortage rate is developed.Aiming at the poor local search ability of the conventional elite non-dominated solution sorting genetic algorithm(NSGA-Ⅱ)algorithm for solving the multi-objective optimization problem,an NSGA-Ⅱ algorithm based on the improved elite retention strategy is proposed.By introducing a new fitness function,the algorithm is sorted and reasonably retains the elite individuals,so it improves the optimization effect,thus to enhance the local search ability,continuously approach the Pareto true frontier,and obtain better capacity configuration solutions.Finally,the rationality of the proposed method is verified by arithmetic examples.

李鑫;张亚丽;李松;邱亚;仇坤

合肥工业大学电气与自动化工程学院,安徽 合肥 230009

独立微电网混合储能容量优化配置改进NSGA-II

stand-alone microgridshybrid energy storagecapacity optimized allocationimproved NSGA-II

《热力发电》 2024 (012)

49-56 / 8

国家自然科学基金项目(62202138)National Natural Science Foundation of China(62202138)

10.19666/j.rlfd.202405113

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