Physics-Informed Hybrid Quantum-Classical Dispatching for Large-Scale Renewable Power Systems:A Noise-Resilient Framework

TL;DR

The paper presents PI-HQCD, a physics-informed hybrid quantum-classical framework that embeds topology-aware power-network physics into a quantum optimization substrate to tackle stochastic, non-convex dispatch in renewable-rich grids. By decomposing the Hamiltonian into cost, physics, and risk blocks and encoding dispatch with affine binary expansion, the method achieves scalable, interpretable optimization with improved gradient behavior ($ abla J$ variance scales as $\mathcal{O}(1/N)$) and noise resilience via a physics-regularized objective. The approach demonstrates superior economic efficiency and renewable utilization on IEEE-39 and 118-bus benchmarks compared with SDDP and a baseline VQA, while maintaining robustness to measurement noise and multi-timescale dynamics. The work establishes a practical paradigm for embedding engineering physics in quantum optimization and suggests a path toward scalable quantum advantage in next-generation grid operations.

Abstract

The integration of high-penetration renewable energy introduces significant stochasticity and non-convexity into power system dispatching, challenging the computational limits of classical optimization. While Variational Quantum Algorithms (VQAs) on Noisy Intermediate-Scale Quantum (NISQ) devices offer a promising path for combinatorial acceleration, existing approaches typically treat the power grid as a "black box", suffering from poor scalability (barren plateaus) and frequent violations of physical constraints. Bridging these gaps, this paper proposes a Physics-Informed Hybrid Quantum-Classical Dispatching (PI-HQCD) framework. We construct a topology-aware Hamiltonian that explicitly embeds linearized power flow equations, storage dynamics, and multi-timescale coupling directly into the quantum substrate, significantly reducing the search space dimensionality. We further derive a noise-adaptive regularization mechanism that theoretically bounds the effective Lipschitz constant of the objective function, guaranteeing convergence stability under realistic quantum measurement noise. Numerical experiments on the IEEE 39-bus benchmark and a 118-bus regional grid demonstrate that PI-HQCD achieves superior economic efficiency and higher renewable utilization compared to stochastic dual dynamic programming (SDDP). Theoretical analysis confirms that this topology-aware design leads to an O(1/N) gradient variance scaling, effectively mitigating barren plateaus and ensuring scalability for larger networks. This work establishes a rigorous paradigm for embedding engineering physics into quantum computing, paving the way for practical quantum advantage in next-generation grid operations.

Figures (6)

• Figure 1: Schematic of the Physics-Informed Quantum Encoding strategy. (a) The physical power network topology. (b) The corresponding Hamiltonian interaction graph, preserving sparsity. (c) The Physics-Informed Ansatz, where entangling gates ($ZZ$) are placed exclusively between physically connected qubits.
• Figure 2: Gradient variance scaling analysis. The proposed PI-HQCD ansatz (blue) exhibits a polynomial decay $\mathcal{O}(1/N)$, avoiding the exponential barren plateaus observed in standard random ansatzes (orange).
• Figure 3: Convergence behavior comparison of PI-HQCD and baseline methods on the IEEE-39 bus system.
• Figure 4: Comparison of renewable utilization rate and total operating cost across different dispatch methods.
• Figure 5: Robustness of PI-HQCD against quantum measurement noise.