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The resilience of electric systems is receiving growing attention due to their increased vulnerability to infrastructure damages and widespread outages from frequent extreme climactic conditions attributed to global warming effects. Resilience evaluation methods should recognize the uncertainties and correlations in the performance variations of different types of energy resources, load characteristics, extreme events and their impacts on the grid elements. However, there is a lack of established methods and resilience metrics that are widely accepted. In this context, this paper presents the development of probabilistic extreme event model, impact assessment model, and optimal restoration model for active distribution systems, and integrates the models using a non-sequential Monte Carlo Simulation framework. The inter-dependencies of time-varying demand, renewable energy output, and energy storage characteristics are incorporated in the framework. A set of metrics is proposed to quantify the resilience of the system against extreme events and their outage impacts at the load points. The metrics and their probability distribution thus obtained can be utilized in probabilistic value-based investment planning to select appropriate measures to enhance the system resilience. Selected case studies are conducted on the IEEE 69-bus test system to show the efficacy of the proposed framework.
Gautam et al. (Tue,) studied this question.