A two-stage stochastic programming model for electric substation flood mitigation prior to an imminent hurricane

TL;DR

This paper introduces a two-stage stochastic programming model to optimize flood mitigation for electrical substations before hurricanes, aiming to reduce load shedding under uncertain flood scenarios.

Contribution

It develops a novel two-stage stochastic model incorporating flood simulation and applies it to realistic hurricane case studies, analyzing mitigation budget impacts.

Findings

Mitigation effectiveness is highly sensitive to budget constraints.

Better mitigation options significantly improve grid resilience.

Spatial features influence optimal mitigation placement.

Abstract

We present a stochastic programming model for informing the deployment of ad hoc flood mitigation measures to protect electrical substations prior to an imminent and uncertain hurricane. The first stage captures the deployment of a fixed number of mitigation resources, and the second stage captures grid operation in response to a contingency. The primary objective is to minimize expected load shed. We develop methods for simulating flooding induced by extreme rainfall and construct two geographically realistic case studies, one based on Tropical Storm Imelda and the other on Hurricane Harvey. Applying our model to those case studies, we investigate the effect of the mitigation budget on the optimal objective value and solutions. Our results highlight the sensitivity of the optimal mitigation to the budget, a consequence of those decisions being discrete. We additionally assess the value of having better mitigation options and the spatial features of the optimal mitigation.