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

LI Xin; ZHANG Yali; LI Song; QIU Ya; QIU Kun

doi:10.19666/j.rlfd.202405113

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

更新时间：2025-09-28

- Capacity optimization allocation of hybrid energy storage system based on improved NSGA-II algorithm
- Thermal Power Generation Vol. 53, Issue 12, Pages: 49-56(2024)
- 作者机构：
  
  合肥工业大学电气与自动化工程学院
- 作者简介：
- 基金信息：
- DOI：10.19666/j.rlfd.202405113
  CLC： TM91
- Published：2024
- 稿件说明：
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LI Xin, ZHANG Yali, LI Song, et al. Capacity optimization allocation of hybrid energy storage system based on improved NSGA-II algorithm[J]. Thermal power generation, 2024, 53(12): 49-56.
DOI：

LI Xin, ZHANG Yali, LI Song, et al. Capacity optimization allocation of hybrid energy storage system based on improved NSGA-II algorithm[J]. Thermal power generation, 2024, 53(12): 49-56. DOI： 10.19666/j.rlfd.202405113.

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（vanadiumredox 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。

Abstract

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.

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