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.
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.
Unit Commitment (UC) is a core optimization problem in power system operation and electricity market scheduling. It determines the optimal on/off status and dispatch of generating units while satisfying system, operational, and market constraints. Traditionally, UC has been solved using mixed-integer programming, dynamic programming, or metaheuristic methods, all of which face scalability challenges as systems grow in size and uncertainty. Recent advances in quantum computing, spanning quantum annealing, variational algorithms, and hybrid quantum classical optimization, have opened new opportunities to accelerate UC solution processes by exploiting quantum parallelism and entanglement. This paper presents a comprehensive survey of existing research on the applications of quantum computing for solving the UC problem. The reviewed works are categorized based on the employed quantum paradigms, including annealing-based, variational hybrid, quantum machine learning, and quantum-inspired methods. Key modeling strategies, hardware implementations, and computational trade-offs are discussed, highlighting the current progress, limitations, and potential future directions for large-scale quantum-enabled UC.
Power flow optimization is paramount in ensuring reliable and efficient operation of electrical grid networks, especially as grids become more intricate with the integration of renewable energy sources. Classical computing methods have long dominated this optimization space but are increasingly challenged by the scalability and computational complexity of contemporary grid systems. This paper introduces a pioneering methodology leveraging the inherent capabilities of quantum computing to tackle optimal power flow (OPF) problems in multifaceted grid networks. Quantum algorithms, characterized by their superposition and entanglement properties, are harnessed to explore the vast solution space more efficiently than their classical counterparts. A quantum annealing approach, combined with the quantum phase estimation algorithm, is employed to ascertain global optima with reduced computational resources and time. Results obtained from simulated quantum environments, benchmarked against classical algorithms, demonstrate a significant reduction in computational time and enhanced accuracy in determining optimal solutions. This work elucidates the potential of quantum computing as a transformative tool in advancing the realms of grid optimization, setting a paradigm for future research in quantum-enabled energy systems.
The increasing complexity of modern power grids, driven by the integration of renewable energy sources, decentralized energy generation, and dynamic load variations, necessitates advanced computational approaches for efficient grid optimization. Conventional algorithms often struggle with the high-dimensionality and computational intensity of power system optimization problems, prompting the exploration of quantum computing as a transformative alternative. Quantum computing, particularly quantum machine learning (QML) and quantum optimization algorithms, has demonstrated significant potential in accelerating grid stability analysis, energy dispatch, and fault detection. This chapter provides a comprehensive investigation into quantum computing applications for power electronics, focusing on quantum-inspired machine learning models and hybrid quantum-classical frameworks for IoT-enabled smart grids. The potential advantages of quantum algorithms, including the Quantum Approximate Optimization Algorithm (QAOA), Variational Quantum Eigensolver (VQE), and Grover’s search algorithm, are analyzed in the context of grid optimization, fault detection, and cybersecurity. Additionally, a comparative analysis of classical and quantum machine learning techniques highlights key performance differences and the challenges associated with practical quantum implementations. Despite the promising advancements, several challenges, including quantum hardware limitations, noise susceptibility, and scalability issues, must be addressed for real-world deployment. Future directions emphasize the need for noise-resilient quantum algorithms, efficient hybrid architectures, and quantum-inspired classical methods to bridge the gap between existing computational techniques and next-generation quantum solutions for power grid management.
We explore how quantum computing (QC) can advance transportation optimization, with a focus on two high-impact areas: traffic signal control and vehicle electrification with grid integration. As transportation systems grow in complexity, classical optimization methods increasingly struggle to deliver scalable and efficient solutions, particularly for real-time environments. This work identifies key challenges within these two domains where QC may offer advantages, particularly in handling combinatorial decision spaces and dynamic constraints. We begin by outlining the limitations of classical approaches for traffic signal control optimization and electric vehicle charging coordination, highlighting where computational limitations arise. Previous quantum formulations are discussed to demonstrate how emerging quantum algorithms, including quantum annealing and hybrid algorithms, could be leveraged to reformulate and address these problems. We also evaluate the suitability of current quantum hardware and discuss the advantages that QC could bring to these domains. While acknowledging the present limitations of QC technologies, this poster emphasizes the importance of preparing quantum-compatible models today. By reviewing and establishing formulations that align with the strengths of quantum algorithms, researchers and practitioners can better position themselves to take advantage of QC advancements as they occur. This work aims to provide a practical, forward-looking perspective on the near-term potential of quantum computing in transportation optimization. Quantum Computing Transportation Optimization Traffic Signal Control Electric Vehicle Charging
The convergence of quantum computing and decentralized energy systems presents a transformative opportunity to address the scalability, security, and real-time intelligence challenges of modern smart grids. This paper provides a comprehensive review of how quantum technologies-including quantum optimization, quantum machine learning (QML), and quantum cryptography-can be integrated into big data-driven smart grid architectures. First, we classify the diverse data sources, analytics bottlenecks, and cyber-physical risks intrinsic to decentralized grids, highlighting the limitations of classical methods under high-dimensional, real-time constraints. Next, we examine the state-of-the-art in quantum optimization techniques, such as quantum approximate optimization algorithm (QAOA), quantum annealing (QA), and hybrid variational methods, for unit commitment (UC), market clearing, mobility scheduling, and state estimation. We then survey quantum-enhanced cybersecurity approaches, including quantum key distribution (QKD), post-quantum cryptography (PQC), and blockchain-integrated control systems, mapping them to grid communication and access control layers. Furthermore, we present a taxonomy of quantum-enabled peer-to-peer (P2P) energy trading platforms, emphasizing cryptographic trust, privacy preservation, and decentralized consensus mechanisms. Cross-domain analysis reveals how quantum modules can augment classical infrastructure to enable secure, scalable, and privacy-preserving grid intelligence. By unifying computation, security, and transaction layers, this review establishes a foundational framework for future research and deployment of quantum-resilient smart grid systems.
In addressing the complexity and real-time demands of modern grid optimization in quantum technology, advancements in computing and other fields have provided new avenues for meeting these needs. This article proposes a structured hybrid quantum-classical framework that uses adiabatic quantum computing to plan the operation of large-scale power networks. A large number of experiments were conducted on benchmark power system cases with 14 to 300 nodes, including various topologies and random operating conditions. The aforementioned method first performs preprocessing and problem partitioning. Then, using spatial and temporal partitioning schemes, the most difficult subproblems are assigned to quantum subroutines. The results show that compared to the state-of-the-art traditional algorithms, the hybrid method has a constraint violation rate of less than 0.03 in all test cases, an average operational cost reduction of 6%, and energy savings of 18%. Runtime evaluation shows that the hybrid solver outperforms pure classical and purely quantum methods, and the computation time exhibits sublinear scaling as the grid size increases. The structure can be flexibly adjusted and can still function normally in emergencies, even if parameters and resources are modified or the topology changes. Research indicates that combining adiabatic quantum computing with hybrid optimization systems can enhance the system's scalability, solution quality, and computational efficiency. This method is currently being applied to the construction of new smart grids.