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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.

The frequent natural disasters caused by global warming have been threatening the safety of power systems for decades. A resilient power system has the ability to recover quickly following a disaster and has been continuously studied these years. However, with the progress of energy transition, a large quantity of renewable energy sources (RESs) has been integrated into the power distribution systems and the uncertainty has complicated the decision and operation of resilient distribution systems. This paper proposes an online resilience assessment method for islanded distribution systems considering the time-varying uncertainty of RESs and loads, based on only the measured data without any forecasting tools. An offline probabilistic modeling and online conditional probability distribution derivation method is proposed to generate short-term power scenarios of RESs and loads dynamically, and an online resilience assessment framework is established with an optimization-based system response model in different outage scenarios of distribution systems. The case study to a modified IEEE 37-node test feeder validates the advantages of the proposed method in generating accurate short-term scenarios of uncertainties and providing reliable resilience assessment results. The proposed framework can be used as a basic tool for the online operation of resilient distribution systems.