In this project summary paper, we summarize the key results and use-cases explored in the German Federal Ministry of Education and Research (BMBF) funded project "Q-GRID" which aims to assess potential quantum utility optimization applications in the electrical grid. The project focuses on two layers of optimization problems relevant to decentralized energy generation and transmission as well as novel energy transportation/exchange methods such as Peer-2-Peer energy trading and microgrid formation. For select energy grid optimization problems, we demonstrate exponential classical optimizer runtime scaling even for small problem instances, and present initial findings that variational quantum algorithms such as QAOA and hybrid quantum annealing solvers may provide more favourable runtime scaling to obtain similar solution quality. These initial results suggest that quantum computing may be a key enabling technology in the future energy transition insofar that they may be able to solve business problems which are already challenging at small problem instance sizes.
In this project summary paper, we summarize the key results and use-cases explored in the German Federal Ministry of Education and Research (BMBF) funded project "Q-GRID" which aims to assess potential quantum utility optimization applications in the electrical grid. The project focuses on two layers of optimization problems relevant to decentralized energy generation and transmission as well as novel energy transportation/exchange methods such as Peer-2-Peer energy trading and microgrid formation. For select energy grid optimization problems, we demonstrate exponential classical optimizer runtime scaling even for small problem instances, and present initial findings that variational quantum algorithms such as QAOA and hybrid quantum annealing solvers may provide more favourable runtime scaling to obtain similar solution quality. These initial results suggest that quantum computing may be a key enabling technology in the future energy transition insofar that they may be able to solve business problems which are already challenging at small problem instance sizes.
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.
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.
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.
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.
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.
Quantum Simulation-based Optimization (QuSO) is a recently proposed class of optimization problems that entails industrially relevant problems characterized by cost functions or constraints that depend on summary statistic information about the simulation of a physical system or process. This work extends initial theoretical results that proved an up-to-exponential speedup for the simulation component of the QAOA-based QuSO solver for the unit commitment problem to an end-to-end speedup, explicitly including the outer optimization component. The numerical experiments were conducted using randomly generated power grid instances of varying sizes and loads that adhere to the physical properties of real world power grids. Exploiting clever classical pre-computation, we develop a very efficient classical quantum circuit simulation that bypasses costly ancillary qubit requirements of the original algorithm, allowing for large-scale experiments. We show that 16 QAOA layers suffice to outperform a strong classical baseline for problems involving up to 14 qubits in scenarios of high load and perform on par otherwise. In summary, our results thus extend previous partial quantum speedup results for QuSO problems to an end-to-end setting that encompasses the runtime of the complete algorithm for a problem of industrial relevance.