基于时序降维的电力电量平衡主导影响因素辨识方法OA

[Objective]With the continuously increasing penetration of volatile renewable energy,the balance mode of the new-type power system exhibits probabilistic characteristics,rendering the traditional fixed-scenario-based power and energy balance analysis inadequate.To improve the representativeness of balance scenarios for actual source-load fluctuations,it is essential to accurately identify the key source-load features that dominate balance conclusions.To this end,this paper proposes a method for identifying the dominant influencing factors of power and energy balance based on time-series dimensionality reduction.[Methods]First,source-load time-series features are defined to implement dimensionality reduction of balance scenarios,addressing the high dimensionality and strong temporal correlation of source-load time-series curves.Then,the multivariate coupling mapping relationship between source-load time-series features and power and energy balance indices is characterized using a data-driven model combined with the Nataf transformation.Next,a global-sensitivity-analysis-based method is proposed to identify the dominant influencing factors of power and energy balance.Finally,a case study is conducted using source-load data of a provincial power grid.[Results]Compared with principal component analysis-based scenario aggregation,the proposed method reduces the calculation error by 27.33%under the Kolmogorov-Smirnov distance metric,which verifies the effectiveness of the proposed method.[Conclusions]The proposed method quantifies the influence of source-load time-series features on power and energy balance indices and enhances the representativeness of typical balance scenarios for source-load fluctuations,thereby providing theoretical and technical support for the balance analysis of renewable energy systems.