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Q-GRID: Quantum Optimization for the Future Energy Grid

Jonas Blenninger, David Bucher, G. Cortiana, Kumar Ghosh, Naeimeh Mohseni, Jonas Nüßlein, Corey O’Meara, Daniel Porawski, Benedikt Wimmer

Published Aug 5, 2024• 15 citations
Publisher
DOI
10.1007/s13218-024-00866-5

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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.