基于NSGA-Ⅲ与模糊聚类的光储式充电站储能系统优化运行方法

doi:10.12204/j.issn.1000-7229.2021.03.004

摘要/Abstract

摘要：

针对光储式充电站运行成本高、电网侧负荷波动水平较大的问题,提出一种基于参考点约束的非支配排序遗传算法(nondominated sorting genetic algorithm Ⅲ, NSGA-Ⅲ)与模糊聚类结合的优化算法用于储能系统优化运行。首先,在分析光储式充电站系统结构的基础上,以电网侧负荷方差最小、储能系统运行维护成本最小和向电网购电费用最小为目标函数,建立储能系统多目标优化运行模型;然后,采用NSGA-Ⅲ对模型进行求解,针对多目标优化得到的Pareto最优解集所含信息量大,使得运行人员决策困难的问题,采用模糊聚类方法对Pareto最优解集进行筛选;最后,通过算例验证了所提优化算法的有效性,与粒子群算法相比,所提算法在满足负荷需求的基础上进一步提升了充电站经济性和电网侧的负荷水平,使系统整体性能综合最优。

关键词: 光储式充电站, 储能系统, 优化运行, NSGA-Ⅲ, 模糊聚类

Abstract:

To alleviate the problems of high operating cost of PV-integrated EV charging station and large load fluctuation on the grid side, an optimization algorithm combining NSGA-III (nondominated sorting genetic algorithm III) and fuzzy clustering is proposed for optimization operation of energy storage. Firstly, on the basis of analyzing the structure of the charging station, a multi-objective operation model of the energy storage is established to minimize the load variance of grid side, the operation and maintenance cost of the energy storage, and the purchase cost from the grid. Then, NSGA-III algorithm is applied to solve multi-objective model. For the Pareto sets containing a lot of information, it is difficult for the operators to make a decision. A method based on fuzzy clustering is proposed to select the optimal solution from Pareto sets. Finally, extensive experimental analyses demonstrate the efficiency of the proposed method, and show that, compared with particle swarm optimization, the economy of the system and the grid-side load level are improved on the basis of the load demand, so that the overall performance of the charging station is optimal comprehensively.

Key words: PV-integrated EV charging station, energy storage system, optimization operation, NSGA-III, fuzzy clustering

中图分类号:

TM732

付张杰, 王育飞, 薛花, 张宇, 时珊珊, 方陈. 基于NSGA-Ⅲ与模糊聚类的光储式充电站储能系统优化运行方法[J]. 电力建设, 2021, 42(3): 27-34.

FU Zhangjie, WANG Yufei, XUE Hua, ZHANG Yu, SHI Shanshan, FANG Chen. Optimal Operation Method of Energy Storage System in PV-integrated EV Charging Station Applying NSGA-III and Fuzzy Clustering[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(3): 27-34.

图/表 11

图1 充电站系统结构图

Fig.1 Structure diagram of a charging station

图2 多目标模型求解流程图

Fig.2 Flow chart of the multi-objective model solving process

图3 电动汽车日充电功率需求

Fig.3 Power demand of EVs in a day

图4 典型日光伏功率数据

Fig.4 Photovoltaic power data in typical day

图5 Pareto解集

Fig.5 Pareto solution set

表1 两种算法的储能运行容量求解结果

Table 1 Energy storage operation capacity under two algorithms

表2 两种算法的优化指标求解结果

Table 2 Index opimization results of two algorithms

图6 两种优化算法下电网侧供电功率曲线

Fig.6 Power curve of grid under two algorithms

图7 两种优化算法下储能系统充放电功率曲线

Fig.7 Power curve of energy storage under two algorithms

表3 各误差量级基础数据

Table 3 Basic data of different deviation levels

图8 误差量级对运行结果的影响

Fig.8 Impact of different deviation levels on operation result

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