含风电和光伏的可再生能源场景削减方法

以风电和光伏为代表的可再生能源渗透率不断增加,其出力不确定性导致的大规模时序场景给电力系统的优化分析带来很高的计算复杂度。以场景削减技术精准刻画区域风电、光伏出力特性是解决以上问题的有效方法之一。提出一种基于聚类与优化算法相结合的可再生能源场景削减方法。首先对数据进行清洗、降噪等预处理,其次利用肘部法则与轮廓系数判断风电、光伏类别个数并进行聚类。然后,利用粒子群与遗传算法分别提取风电、光伏典型出力曲线,并对两种算法结果进行对比,从而生成典型场景。算例分析以欧洲输电系统运营商Amprion提供的2015年1月1日至2019年12月31日风电、光伏出力数据为研究对象,利用所提方法求得的出力曲线可以有效反映该区域风电、光伏出力典型场景,为后续电力系统规划、运行优化等问题提供数据支撑。

Scenario reduction method of renewable energy including wind power and photovoltaic

With the increasing penetration of renewable energy represented by wind power and photovoltaic, the large-scale time sequence scenario caused by the uncertainty of their output brings high computational complexity to the optimization analysis of a power system. One of the effective methods to solve the above problems is to accurately describe the regional wind power and photovoltaic output characteristics with scene reduction. In this paper, a scenario reduction method based on clustering and optimization algorithms is proposed. First, the data are preprocessed e.g. by cleaning and noise reduction. Secondly, the elbow method and silhouette coefficient are used to determine the number of wind power and photovoltaic categories and clusters. Then, the typical output curves of wind power and photovoltaic are extracted by particle swarm optimization and genetic algorithm respectively, and the results of the two algorithms are compared to generate typical scenarios. Taking the wind power and photovoltaic output data from January 1, 2015 to December 31, 2019 provided by European transmission system operator Amprion as the research object, the output curve obtained by the proposed method can effectively reflect the typical scenario of wind power and photovoltaic output in the region, and provide data support for subsequent power system planning and operation optimization. This work is supported by the National Natural Science Foundation of China (No. 51777126).