Quantum Computing for EVs to Enhance Grid Resilience and Disaster Relief: Challenges and Opportunities

TL;DR

This paper explores how quantum computing can address complex optimization challenges in vehicle-to-grid technology and mobile charging station placement to improve power grid resilience and disaster response.

Contribution

It reviews current optimization methods for V2G and CSP, identifies their limitations, and discusses how quantum computing can overcome these computational bottlenecks.

Findings

Quantum computing has potential to solve complex grid optimization problems.

Current methods face scalability issues in disaster scenarios.

Quantum approaches could accelerate grid restoration processes.

Abstract

The power grid is the foundation of modern society, however extreme weather events have increasingly caused widespread outages. Enhancing grid resilience is therefore critical to maintaining secure and reliable operations. In disaster relief and restoration, vehicle-to-grid (V2G) technology allows electric vehicles (EVs) to serve as mobile energy resources by discharging to support critical loads or regulating grid frequency as needed. Effective V2G operation requires coordinated charging and discharging of many EVs through optimization. Similarly, in grid restoration, EVs must be strategically routed to affected areas, forming the mobile charging station placement (CSP) problem, which presents another complex optimization challenge. This work reviews state-of-the-art optimization methods for V2G and mobile CSP applications, outlines their limitations, and explores how quantum computing (QC) could overcome current computational bottlenecks. A QC-focused perspective is presented on enhancing grid resilience and accelerating restoration as extreme weather events grow more frequent and severe.