Quantum for Energy and Utilities 2026

CASE STUDY 16 Quantum computing Optimizing energy communities and peer-to-peer energy exchange Energy communities let prosumers, municipalities and businesses jointly generate, consume and share renewable energy locally, but the hard part is coordination: deciding who should participate, aligning objectives such as local balancing and cost reduction, and managing flows under real operational constraints. Peer-to-peer (P2P) trading adds a transactional layer that matches local supply and demand, yet turning aggregated supply and demand into a minimal, feasible set of simultaneous trades quickly becomes a large combinatorial optimization problem. To push beyond the scaling-up limits of classical solvers, E.ON worked with IBM and D-Wave on hybrid quantum-classical optimization approaches within the Q-GRID research line, targeting two bottlenecks: energy community formation (coalition structure generation) and P2P trade decomposition (a sparse graph decomposition problem related to Birkhoff-style decompositions). In the community-formation benchmark, researchers compared classical heuristics and exact methods against quantum approaches, reporting evidence that quantum annealing on D-Wave can reach comparable solution quality with more favourable runtime scaling-up for approximate optimization in instances exceeding 100 agents, while also benchmarking against QAOA-style approaches on IBM hardware. For P2P trading, a 2025 preprint introduced a hybrid method that plugs a QAOA-based sampling subroutine into a classical Fully-Corrective Frank-Wolfe framework, with experiments spanning simulators and IBM hardware up to 111 qubits. The authors report that the quantum-assisted sampling yields consistently sparser trade decompositions and can improve approximation error on larger graph families, which directly translates to cleaner, more implementable sets of simultaneous P2P transactions.22,23,24 Quantum for Energy and Utilities: Key Opportunities for Energy Transition 32