Integrated Intelligence for Electric Grid Resilience using Storm Surge and Inland Flooding Models

Abstract

In the past four decades, Texas has experienced more than 80 hurricanes, including Harvey, which alone caused damages costing over $130B. Given this history and predictions of more frequent and/or more intense storms in the future, it is of paramount importance to make prudent investment decisions to enhance the resilience of the electric grid against such extreme weather events. In this work, we explore two storm-surge models and integration of these models with an inland flooding model to create representative future flood scenarios for the state of Texas. We then discuss how these flood scenarios can further be integrated with a synthetic power system model that accurately quantifies the loss of power in all contingencies for the same geographical region, using a stochastic optimization framework. Our proposed two-stage scenario-based stochastic optimization approach helps identify substations susceptible to flooding due to storm surge and inland flooding, and recommends optimal substation hardening solutions given a finite investment budget. The insights from our work can be used to decide substation hardening strategies to enhance the electric grid’s resilience against a multitude of future storm scenarios.