Comparisons of two-stage models for flood mitigation of electrical substations

TL;DR

This paper compares stochastic programming and robust optimization models for flood mitigation of electrical substations, analyzing their effectiveness in protecting power grids during hurricanes using realistic case studies.

Contribution

It introduces adapted power flow models with relatively complete recourse and evaluates the impact of budget, uncertainty perspective, and power flow model choice on mitigation strategies.

Findings

Mitigation budget and uncertainty perspective significantly affect strategies.

Choice of power flow model (DC vs LPAC) has minimal impact.

Mitigation solutions perform well in AC power flow validation.

Abstract

We compare stochastic programming and robust optimization decision models for informing the deployment of ad hoc flood mitigation measures to protect electrical substations prior to an imminent and uncertain hurricane. In our models, the first stage captures the deployment of a fixed quantity of flood mitigation resources, and the second stage captures the operation of a potentially degraded power grid with the primary goal of minimizing load shed. To model grid operation, we introduce adaptations of the DC and LPAC power flow approximation models that feature relatively complete recourse by way of an indicator variable. We apply our models to a pair of geographically realistic flooding case studies, one based on Hurricane Harvey and the other on Tropical Storm Imelda. We investigate the effect of the mitigation budget, the choice of power flow model, and the uncertainty perspective on the optimal mitigation strategy. Our results indicate the mitigation budget and uncertainty perspective are impactful whereas choosing between the DC and LPAC power flow models is of little to no consequence. To validate our models, we assess the performance of the mitigation solutions they prescribe in an AC power flow model.