Algorithms for Mitigating the Effect of Uncertain Geomagnetic Disturbances in Electric Grids

TL;DR

This paper introduces a distributionally robust optimization approach to mitigate the impact of uncertain geomagnetic disturbances on electric grids, enhancing grid resilience through preemptive mitigation actions.

Contribution

It develops a novel DRO model and solution algorithms to address uncertainty in GMDs, improving mitigation strategies for electric grid protection.

Findings

The proposed algorithms effectively mitigate GMD effects in simulations.

The exact reformulation simplifies solving the DRO model.

Numerical results demonstrate improved system resilience.

Abstract

Geomagnetic disturbances (GMDs), a result of space weather, pose a severe risk to electric grids. When GMDs occur, they can cause geomagnetically-induced currents (GICs), which saturate transformers, induce hot-spot heating, and increase reactive power losses in the transmission grid. Furthermore, uncertainty in the magnitude and orientation of the geo-electric field, and insufficient historical data make the problem of mitigating the effects of uncertain GMDs challenging. In this paper, we propose a novel distributionally robust optimization (DRO) approach that models uncertain GMDs and mitigates the effects of GICs on electric grids. This is achieved via a set of mitigation actions (e.g., line switching, locating blocking devices, generator re-dispatch and load shedding), prior to the GMD event, such that the worst-case expectation of the system cost is minimized. To this end, we develop a column-and-constraint generation algorithm that solves a sequence of mixed-integer second-order conic programs to handle the underlying convex support set of the uncertain GMDs. Also, we present a monolithic exact reformulation of our DRO model when the underlying support set can be approximated by a polytope with three extreme points. Numerical experiments on 'epri-21' system show the efficacy of the proposed algorithms and the exact reformulation of our DRO model.