Risk Assessment and Optimal Response Strategies for Resilience of Electric Power Infrastructure to Extreme Weather

Abstract

Extreme weather is an increasingly critical threat to infrastructure systems. This thesis develops a stochastic modeling and decision-making framework for proactive resource allocation and response strategies to improve the resilience of electric power infrastructure in the wake of severe weather events. The framework is based on a physically-based, probabilistic risk assessment approach to estimating the weather-induced damage, and accounts for power flow constraints in designing response actions within electricity distribution networks.

Firstly, we formulate an asymmetric hurricane wind field model that is applicable to forecasting and large-scale ensemble simulation. The hurricane wind field model incorporates low-wavenumber asymmetries, and its parameters are estimated using a Constrained Nonlinear Least Squares problem. Inclusion of asymmetries in the model improves the accuracy of wind risk assessment in the hurricane eye wall, where wind velocities are maximized.

Secondly, the wind field forecasts are used as inputs to a probabilistic model for damage estimation in infrastructure systems. The novelty of this damage model is that it accounts for the spatial variability in damage estimates resulting from the hurricane wind field and forecast uncertainty in the hurricane’s temporal evolution. We demonstrate that our model is capable of accurately predicting outage rates resulting from damage to the electrical grid following Hurricane Michael.

Thirdly, we develop a computational approach for optimal resource allocation and multi-step response operations. Using a two-stage stochastic mixed-integer formulation, we model the strategic deployment of distributed energy resources (DERs) ahead of a storm’s landing, and the joint operation of islanded microgrids and repair of damaged components in the post-storm stage. The failure scenarios in this formulation are drawn from our physically-based damage model. The key challenge here is that the size of the optimization problem increases super-linearly with the network size. To address this computational bottleneck, we develop three solution approaches based on L-shaped Benders decomposition. These approaches incorporate the network structure and power flow constraints to derive more effective Benders cuts. We evaluate the scalability of these approaches on benchmark networks, and show that they are useful in evaluating the resiliency improvements due to optimal DER allocation and response strategies under various resource constraints.