Quantum Annealing Applications for Power Grid Modernization

Abstract

Power grid modernization is essential for ensuring the reliability, efficiency, and sustainability of power systems in response to increasing demand, supply uncertainty, and decentralization of resources. This dissertation investigates the application of quantum computing (QC) in solving two challenging power grid problems, including Unit Commitment (UC) and Vehicle-to-Grid (V2G) optimization. Through novel approaches in quantum modeling, this work seeks to advance computational strategies that could support grid modernization efforts as grid complexity grows beyond the capabilities of classical optimization methods. The first part of this research introduces QC and the core mechanics of quantum physics that are vital to understanding quantum computations. Different methods of QC and their histories are discussed, including gate-based models and quantum annealing. Quantum annealing (QA) is the focus of the rest of the chapter, discussing the QA process, the mathematics behind it, and the reformulations required to make an optimization problem quantum-compatible. In the second part, a hybrid quantum-classical model is developed to solve the UC problem, combining QA with classical solvers to balance computational efficiency and solution accuracy. The results are compared to a classical deterministic and metaheuristic solutions, which reveals that while classical models outperform quantum solutions at smaller scales, the hybrid approach offers promising advantages in handling more complex grid configurations. This hybrid model provides a pathway for QC to enhance classical optimization techniques, especially as hardware and embedding algorithms continue to evolve. A novel discretization strategy is introduced that is designed to solve the UC problem more efficiently compatible with quantum annealing (QA) hardware. By representing continuous variables as binary variables using a logarithmic binning technique, this approach aims to reduce the number of binary variables needed for problem reformulation. Results show that this novel discretization significantly decreased computational time and yielded fewer binary variables, although further adjustments are needed to improve solution accuracy and optimality. The final part examines the feasibility of using a quantum model to optimize V2G scheduling. This quantum model, aimed at minimizing EV charging costs over a 24-hour period, achieves a reasonable optimality rate relative to a classical deterministic solution for a studied test system. The study demonstrates QC’s potential for real-world applications, albeit on a limited scale, and highlights the scalability possibilities for broader V2G network management as quantum technology matures. This is followed by a review of existing methods for V2G and mobile energy storage system optimization, with a focus on enhancing grid resilience and a discussion around potential quantum advantages and pathways for future utilization. Collectively, these findings demonstrate that while QC in grid optimization is still in its nascent stages, it holds substantial promise for meeting the computational demands of the future power grid. This research paves the way for continued exploration of quantum-enhanced solutions to support grid modernization in the era of renewable energy integration, electrification, and continually increasing complexity.