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在规模化光伏发电中,对光伏功率预测误差进行补偿是储能系统的一项重要功能.光伏发电输出具有强随机波动和不确定性,传统储能容量配置方法难以满足光伏发电并网需求.本文提出了一种基于不确定性分析理论的光伏电站储能容量配置方法.首先利用k-均值聚类算法对天气类型进行分类,分析了不同天气类型下的功率预测误差分布特征.采用核密度估计(KDE)方法拟合储能系统日最大功率和最大容量需求的分布,并计算在不同置信水平下储能系统的功率和容量需求,引入功率满意度和容量满意度两个指标,得到储能优化配置方案.最后基于某光伏电站的实际运行数据对论文方法进行了验证.结果表明,论文方法可以有效补偿功率预测误差,同时减少储能系统投入运行的比例.“,”Compensating for photovoltaic (PV) power forecast errors is an important function of energy storage systems. As PV power outputs have strong random fluctuations and uncertainty, it is difficult to satisfy the grid-connection requirements using fixed energy storage capacity configuration methods. In this paper, a method of configuring energy storage capacity is proposed based on the uncertainty of PV power generation. A k-means clustering algorithm is used to classify weather types based on differences in solar irradiance. The power forecast errors in different weather types are analyzed, and an energy storage system is used to compensate for the errors. The kernel density estimation is used to fit the distributions of the daily maximum power and maximum capacity requirements of the energy storage system; the power and capacity of the energy storage unit are calculated at different confidence levels. The optimized energy storage configuration of a PV plant is presented according to the calculated degrees of power and capacity satisfaction. The proposed method was validated using actual operating data from a PV power station. The results indicated that the required energy storage can be significantly reduced while compensating for power forecast errors.