AI-Based Assessment of Power System Resilience under Climate Change-Induced Extreme Weather Events

Power system resilience analysis methods aim to assess the ability of a power system to withstand and recover from high-impact, low-probability events, such as natural disasters. These methods often entail performing AC Optimal Power Flows (OPF) using nonlinear optimization algorithms for numerous sequential Monte Carlo (MC) iterations, which require high computational resources. This may restrict the number of feasible simulations, with the risk of reducing the reliability on the analysis outcomes and limiting the scope of the resilience assessment. This paper introduces the use of Artificial Intelligence (AI) to enhance the computational performance of power system resilience analysis. The paper proposes to use artificial neural networks (ANN) as a surrogate model to approximate the OPF solution for different scenarios and contingencies. The case study demonstrates that ANN provide an interesting solution, reducing the computational burden to a fraction of a classical OPF approaches, while keeping remarkable accuracy in the estimation of resiliency KPIs, such as energy not served (ENS) and survivability. A case study of resiliency analysis with ANN application is presented. The analysis is conducted with reXplan, a novel tool for climate power system resilience analysis, and an ANN built on Pytorch framework. SimBench, an open dataset for network analysis, provides the grid data, generation, and demand profiles. A comparison of the KPIs calculated with ANN and simulated results with OPF is provided, showing the effectiveness and efficiency of the proposed approach.