A probabilistic resilience assessment method for distribution system operation with probabilistic optimal power flow

Abstract

The modern power distribution systems are vulnerable to natural disasters and malicious attacks, while the uncertainty of a large amount of renewable energy sources (RESs) further increases their operational risk in extreme events. An adaptive probabilistic resilience assessment method is thus necessary to provide risk information and help decision making. To tackle the existing problems in uncertainty handling and computation efficiency of resilience assessment in operational stage, this paper establishes a systematic online probabilistic resilience assessment framework. A novel probabilistic modeling method is proposed to characterize the time-varying short-term uncertainty of RESs considering incomplete measurement information due to disasters-induced communication interruption. Moreover, the probabilistic optimal power flow (POPF) model is newly applied to the resilience assessment problem and solved by the cumulant method (CM), which can calculate the statistic information of load power and resilience indexes efficiently with only one optimization at basic operational point. The case study to a modified IEEE 37 node test feeder validates the advantage of the proposed method in reducing operational uncertainty degree, as well as its enough efficiency and accuracy to be applied to the online probabilistic resilience assessment for distribution systems operation.