Abstract
The integration of photovoltaic (PV) generation and hydrogen storage in rural microgrids enables clean, long-duration energy supply, yet introduces operational challenges under high uncertainty. These include fluctuations in PV output, stochastic hydrogen demand, and volatile market prices. Traditional deterministic or static robust methods often fall short in handling such dynamic, multi-dimensional uncertainties. To address these gaps, this study develops a Quantum-Inspired Robust Optimization (QRO) framework that coordinates PV-H(2) microgrid scheduling by dynamically adapting to real-time disturbances. The QRO approach leverages quantum-classical hybrid principles and integrates distributionally robust optimization with reinforcement learning. Uncertainty sets evolve adaptively based on operational feedback rather than remaining fixed, enhancing resilience to cyberattacks, extreme weather, and grid outages. Deep Q-learning and policy gradient methods are employed to continuously refine dispatch strategies, ensuring robust performance in non-stationary environments. A case study on a 5 MW PV-H(2) microgrid with a 3 MW electrolyzer and 2 MW fuel cell demonstrates the practical effectiveness of the proposed framework. The model incorporates real historical solar profiles, stochastic demand, and price signals over a full-year horizon. Stress-testing under scenarios such as 48-hour grid failure, signal-based cyberattacks, and 40% PV output curtailment reveals substantial gains: operational costs are reduced by 9.3%, resilience scores improve by over 20% in adverse conditions, and convergence speed increases by 42% relative to classical optimization. These improvements reflect not only enhanced adaptability and computational efficiency, but also the practical feasibility of real-time learning in resilient energy scheduling. Importantly, the term "quantum-inspired" refers to classical algorithms that emulate quantum principles-such as probabilistic reasoning and solution diversity-without employing quantum hardware. By unifying quantum-inspired modeling, distributional robustness, and reinforcement learning, this framework offers a scalable and adaptive solution for next-generation hydrogen-based microgrid operations.