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

TL;DR

This paper introduces a physics-informed hybrid quantum-classical framework for large-scale renewable power dispatching, improving scalability, constraint satisfaction, and efficiency by embedding physical laws into quantum algorithms.

Contribution

It develops a topology-aware Hamiltonian and noise-adaptive regularization to enhance quantum optimization for power systems, addressing scalability and physical constraint issues.

Findings

Outperforms SDDP in efficiency and renewable utilization

Achieves O(1/N) gradient variance scaling, reducing barren plateaus

Demonstrates scalability on IEEE 39-bus and 118-bus systems

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.