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

TL;DR

The paper addresses improving grid resilience and disaster relief by optimizing EV based resources (V2G) and mobile charging assets (CSP) under uncertainty. It surveys state-of-the-art classical optimization methods and introduces quantum computing formulations that translate these problems into QUBO forms suitable for QA and QAOA, including binary expansions and quadratic penalties. The authors discuss hybrid quantum–classical architectures and provide evidence from early QC-enabled results such as Q-EVCS and quantum-assisted siting to illustrate potential speedups and adaptability. The findings suggest that quantum computing could accelerate convergence and enable real-time, high-dimensional decision-making in complex energy–transport networks, greatly enhancing restoration efficiency and grid resilience.

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.