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We propose two scenario-based optimization models for power grid resiliencedecision making that integrate output from a hydrology model with a power flowmodel. The models are used to identify an optimal substation hardening strategyagainst potential flooding from storms for a given investment budget, which ifimplemented enhances the resilience of the power grid, minimizing the powerdemand that is shed. The same models can alternatively be used to determine theoptimal budget that should be allocated for substation hardening when long-termforecasts of storm frequency and impact (specifically restoration times) areavailable. The two optimization models differ in terms of capturing riskattitude: one minimizes the average load shed for given scenario probabilitiesand the other minimizes the worst-case load shed without needing scenarioprobabilities. To demonstrate the efficacy of the proposed models, we furtherdevelop a case study for the Texas Gulf Coast using storm surge maps developedby the National Oceanic and Atmospheric Administration and a synthetic powergrid for the state of Texas developed as part of an ARPA-E project. For areasonable choice of parameters, we show that a scenario-based representationof uncertainty can offer a significant improvement in minimizing load shed ascompared to using point estimates or average flood values. We further show thatwhen the available investment budget is relatively high, solutions thatminimize the worst-case load shed can offer several advantages as compared tosolutions obtained from minimizing the average load shed. Lastly, we show thateven for relatively low values of load loss and short post-hurricane powerrestoration times, it is optimal to make significant investments in substationhardening to deal with the storm surge considered in the NOAA flood scenarios.

Scenario-based Optimization Models for Power Grid Resilience to Extreme Flooding Events