Quantum Computing for EVs Enhances Grid Resilience and Enables Vehicle-to-Grid Disaster Relief Operations

The increasing frequency of extreme weather events poses a significant threat to the stability of power grids, demanding innovative solutions to enhance resilience and accelerate disaster relief. Tyler Christeson, Amin Khodaei, and Rui Fan, all from the University of Denver, investigate how quantum computing could revolutionise the use of electric vehicles as mobile energy resources. Their work examines vehicle-to-grid technology, which allows EVs to support the grid during peak demand or outages, and the complex challenge of strategically deploying EVs as mobile charging stations after disasters. By reviewing current optimisation methods and exploring the potential of quantum computing, the researchers demonstrate how this emerging technology could overcome existing computational limitations and dramatically improve both grid resilience and the speed of restoration efforts following severe weather events.

EV Charging Infrastructure Planning and Optimisation

Recent research comprehensively examines the planning and optimisation of electric vehicle (EV) charging infrastructure and its integration with power distribution systems. Investigations consider factors such as user demand, charging convenience, and the potential for vehicle-to-grid (V2G) technology, where EVs can feed energy back into the grid. Researchers are exploring advanced algorithms, including those leveraging quantum computing, to solve the complex optimisation problems associated with charger placement and grid coordination. This work demonstrates a clear trend towards leveraging cutting-edge technologies to create more resilient, sustainable, and efficient power grids.

A significant focus lies in enhancing distribution system resilience, particularly in the face of extreme weather events. Studies highlight the importance of mobile energy storage (MES) systems and their ability to restore power quickly after disruptions. Researchers are developing sophisticated routing and scheduling algorithms for MES, aiming to optimise their deployment and maximise their impact on grid resilience. Furthermore, investigations explore the integration of distributed energy resources (DERs) and microgrids to create more robust and sustainable power systems.

Quantum Computing for Resilient Vehicle-to-Grid Systems

This research pioneers a new approach to enhancing grid resilience by investigating the application of quantum computing to vehicle-to-grid (V2G) technology and mobile charging station placement. The study addresses the increasing frequency of weather-related power outages and the limitations of classical optimisation methods in coordinating large fleets of electric vehicles for disaster relief and grid restoration. Researchers systematically reviewed existing V2G optimisation techniques, identifying their practical limitations when applied to dynamic and uncertain disaster scenarios. To overcome these challenges, the team explored the potential of quantum computing, specifically quantum annealing, to accelerate optimisation processes.

The work leverages the unique capabilities of quantum mechanics, including superposition and entanglement, to efficiently explore complex solution spaces. Researchers investigated how quantum annealing could be applied to both V2G dispatch and mobile charging station placement, aiming to improve the speed and effectiveness of grid restoration efforts. This work establishes a foundation for future research into quantum-enhanced grid resilience and demonstrates the potential of quantum computing to address critical challenges in modern power systems. The methodology centers on evaluating how quantum computing can enhance V2G services, enabling EVs to stabilise microgrids, act as mobile storage, and provide ancillary services. The team assessed the potential of coordinated EV fleets to support black-start operations and accelerate restoration at lower cost and with greater flexibility.

Quantum Computing Optimizes Electric Vehicle Grid Support

The research team investigated how electric vehicles (EVs) can bolster power grid resilience, particularly during extreme weather events. Their work focuses on vehicle-to-grid (V2G) technology, enabling EVs to discharge energy and support critical loads or regulate grid frequency. The study demonstrates that effectively coordinating many EVs requires advanced optimisation techniques, and the team explored how quantum computing (QC) could overcome current computational limitations in this area. The core of the research involves a detailed mathematical model for V2G optimisation, designed to minimise system costs while improving grid stability.

This model considers factors such as EV charging and discharging power, state of charge, and binary decision variables. Additional constraints were incorporated to enhance system reliability, including ensuring load balance and limiting power flows. The team formulated a multi-objective function that balances energy costs with unserved load costs, allowing for prioritisation of critical zones during emergencies. Through this model, the researchers demonstrated how strategic EV discharge can be allocated to minimise interruption costs and support community microgrids. Existing mathematical programming methods, including linear programming, mixed integer linear programming, and mixed integer nonlinear programming, were reviewed and applied to co-optimise EV dispatch, grid restoration, and the operation of distributed energy resources. However, the team found these approaches limited by scalability and computational cost when dealing with large, uncertain networks, highlighting the potential of quantum computing to address these limitations and accelerate restoration efforts.

Quantum Computing Enhances Grid Resilience Planning

This research comprehensively examines optimisation strategies for enhancing power grid resilience and disaster recovery, specifically focusing on vehicle-to-grid (V2G) technology and mobile charging station placement. The work demonstrates that while current classical optimisation methods have advanced the field, they face limitations in scalability and adaptability when dealing with the uncertainties inherent in extreme weather events. Researchers investigated the potential of quantum computing (QC) to overcome these limitations by enabling the parallel exploration of complex solution spaces and accelerating the convergence towards optimal solutions. Reformulating grid resilience problems into structures compatible with quantum algorithms allows hybrid quantum-classical frameworks to support real-time, adaptive decision-making during grid contingencies. Future work will focus on continued benchmarking of quantum algorithms against classical baselines to quantify the benefits of QC and guide its practical implementation in grid resilience planning. While acknowledging the current limitations of quantum technology, this research establishes a promising path toward significantly improving the efficiency and capabilities of resilient, data-driven grid operation and disaster response.