光储充检放智能电站K-means聚类能效计量检测方法

K-means Clustering-based Energy Efficiency Measurement of PV-Storage-Charge-Inspection-integrated Smart Stations

光伏出力具有较强的不确定性和随机性,降低了光储充检放一体化能效计量检测性能。为此,在随机采样模式下,提出光储充检放智能电站能效计量检测方法。首先分析光储充检放智能电站的拓扑结构;其次采用K-means聚类算法聚类处理光伏出力数据,通过Beta分布根据电站拓扑结构生成光伏出力场景,为能效计量检测提供相关数据;最后构建电站能效检测模型,完成光储充检放智能电站能效的计量检测。实验结果表明,所提方法可精准检测光储充检放智能电站的供电功率和充放电功率,能效检测相对偏差小。

PV output has strong uncertainty and stochasticity,which reduces the energy efficiency measurement and detection performance of the PV-storage-charge-inspection integration.Therefore under the randomized sampling mode,the energy efficiency measurement and detection method of PV-storage-charge-inspection-integrated stations was proposed in this paper.First the topological structure of the PV-storage-charge-inspection-integrated station was analyzed.Second the photovoltaic output data were clustered by K-means algorithm,and the photovoltaic output scenario was generated according to the topology of power station through Beta distribution,which provided relevant data for energy efficiency measurement and detection.Finally the power station energy efficiency detection was modeled and the measurement and detection of energy efficiency was realized.The proposed method was indicated by experimental results accurate in detecting supply and charge/discharge powers of the PV-storage-charge-inspection-integrated station with small relative deviation of energy efficiency detection.