Keywords
Physics-informed learning
Renewable power dispatch
Variational quantum algorithms
Noise resilience
Submitted : 2026-03-23
Accepted : 2026-04-07
Published : 2026-04-22
Abstract
Rising renewable penetration introduces severe non-convexity in power dispatching, straining classical optimization. While variational quantum algorithms (VQAs) on NISQ devices offer combinatorial potential, “black-box” approaches struggle with scalability and grid constraints. We propose the physics-informed hybrid quantum-classical dispatching (PI-HQCD) framework to address these limitations. PI-HQCD maps power flow and storage constraints directly into a topology-aware Hamiltonian, shrinking the search space. A noise-adaptive regularization technique bounds the objective’s Lipschitz constant, ensuring convergence under measurement noise. Experiments on IEEE 39-bus and 118-bus systems show PI-HQCD outperforms stochastic dual dynamic programming (SDDP) in cost and renewable utilization. Theoretical analysis confirms our topology-aligned ansatz achieves gradient variance scaling, mitigating barren plateaus. This work bridges physical laws and quantum algorithms for next-generation grid operations.
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