基于皮尔逊相关系数融合密度峰值和熵权法典型场景研究

Research on Typical Scenarios Based on Fusion Density Peak Value and Entropy Weight Method of Pearson’s Correlation Coefficient

新能源出力的波动性、随机性使得新型电力系统在规划以及运行分析时,如何获得典型出力场景是亟须解决的关键问题之一。提出了基于皮尔逊相关系数融合密度峰值和熵权法的典型场景聚类中心确定方法。首先,采用密度峰值方法选取初始聚类中心;然后,基于熵权法选取后续聚类中心;接着,采用后向场景缩减方法获得最终典型场景,以该方法为核心,构建了确定新能源典型出力场景的整体方案;最后,以2021年华北某地风电与光伏的日出力场景进行算例分析及对比验证,验证所提方法的准确性和有效性。研究成果将为电力系统规划与运行提供更为准确的数据支撑。

The volatility and randomness of new energy output make it one of the key problems that need to be solved urgently to obtain typical output scenarios in the planning and operation analysis of new power systems.In this paper,a typical scenario clustering center determination method based on fusion density peak value and entropy weight method of Pearson’s correlation coefficient is proposed.The method first selects the initial clustering center by the density peak value method and then selects the subsequent clustering center based on the entropy weight method.Then,it obtains the final typical scenario by using the backward scenario reduction method.Therefore,this paper takes this method as the core to construct an overall scheme for determining the typical output scenarios of new energy.Finally,this paper uses the daily output scenario of wind power and photovoltaic power in North China in 2021 to carry out an analysis of examples and verification through comparison,which proves the accuracy and effectiveness of the proposed method.The research results will provide more accurate data support for power system planning and operation.