Rapid and Less Conservative Interval Power Flow Analysis Based on a Scenario Reflection Method for Renewable Penetrated Power Systems

To overcome the problem of low computing efficiency in interval AC power flow (IACPF) analysis under uncertainty in renewable energy penetrated power systems, this paper proposes a rapid and less conservative interval power flow (IPF) approach based on a scenario reflection method. First, a linearized interval power flow (LIPF) model is developed by depicting bus injection intervals comprising known nodal variables to characterize uncertainties. Then, a rapid scenario reflection (RSR) method is proposed to accelerate the model solution. Unlike traditional optimization approaches, this method reduces computational burden by conducting structural analysis and scenario reflection of the model. Furthermore, considering variables' statistical information, the RSR method is enhanced by incorporating a distributional interval algorithm (DIA) to reduce the conservativeness of interval results. Moreover, given the potential correlation of renewable power generation and load demands, a correlation-aware DIA based on Copula theory and principal component analysis (PCA) is proposed to enhance the accuracy by leveraging the probability distribution information of uncertain scenarios. Finally, comprehensive numerical experiments on various transmission and distribution test systems validate the wide applicability of the proposed methods, showing significant improvements in both computational efficiency and result tightness, while maintaining high accuracy compared to existing IACPF approaches.