Owing to the randomness and intermittence of photovoltaic (PV) generation,the risk of power system operation is rapidly increased after integration with large-scale photovoltaic power plant.Traditional spinning reserve estimating methods are no longer able to meet the requirements of power system operation integrated with PV power plant.The probability distribution model of solar irradiance and PV generation output are developed,and the Latin hypercube sampling method is used to simulate PV generation output scenarios.The improved K-means clustering algorithm based on Huffman tree is used to effectively cluster the PV generation output scenarios,which will greatly reduce the scenario number while ensuring the probability distribution characteristics of PV generation output.On this basis,a model for spinning reserve demand estimation in the power system integrated with large-scale PV power plant is proposed to minimize the system comprehensive operation cost,thus balancing the economy and reliability of power system operation.The improved IEEE-RTS 96 system is simulated via the proposed model to prove its rationality and effectiveness.%光伏发电系统出力的随机性与间歇性，使得电力系统的运行风险在大规模光伏电站并网后迅速增加，传统的旋转备用需求评估方法已经不能满足含光伏电站的系统运行要求。文中建立了太阳辐照度和光伏发电系统出力的概率分布模型，并采用拉丁超立方采样模拟光伏发电系统的出力场景；利用基于 Huffman 树的改进 K-means 聚类算法对光伏发电系统的出力场景进行有效聚类，在保证光伏发电系统出力分布特性的前提下减少了场景数量；在此基础上，提出了考虑大规模光伏电站接入的电力系统旋转备用需求评估模型，以系统综合运行费用最低为目标，兼顾了运行的经济性和可靠性。基于改进的 IEEE-RTS 96系统，对所提模型进行了仿真分析，算例结果验证了模型的合理性和有效性。
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