Quantum-inspired robust optimization for coordinated scheduling of PV-hydrogen microgrids under multi-dimensional uncertainties
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-H2 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-H2 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:…
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