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Quantum-inspired robust optimization for coordinated scheduling of PV-hydrogen microgrids under multi-dimensional

Yunxiao Bai1, Yu Sui2, Xiaoyu Deng2

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This study introduces a Quantum-Inspired Robust Optimization (QRO) framework for photovoltaic-hydrogen microgrids. It enhances energy scheduling resilience and reduces costs by dynamically adapting to uncertainties like weather and demand.

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Area of Science:

  • Energy Systems Engineering
  • Optimization Theory
  • Artificial Intelligence

Background:

  • Rural microgrids integrating photovoltaic (PV) generation and hydrogen storage face operational challenges due to high uncertainty in PV output, hydrogen demand, and market prices.
  • Traditional optimization methods struggle with dynamic, multi-dimensional uncertainties inherent in these systems.
  • Existing approaches lack adaptability to real-time disturbances and evolving operational feedback.

Purpose of the Study:

  • To develop a novel Quantum-Inspired Robust Optimization (QRO) framework for coordinating PV-H2 microgrid scheduling.
  • To enhance microgrid resilience and operational efficiency under dynamic and uncertain conditions.
  • To provide a scalable and adaptive solution for next-generation hydrogen-based microgrid operations.

Main Methods:

  • A Quantum-Inspired Robust Optimization (QRO) framework integrating distributionally robust optimization with reinforcement learning.
  • Adaptive uncertainty sets that evolve based on operational feedback, enhancing resilience to cyberattacks and grid outages.
  • Deep Q-learning and policy gradient methods for continuous refinement of dispatch strategies in non-stationary environments.

Main Results:

  • The QRO framework demonstrated practical effectiveness in a 5 MW PV-H2 microgrid case study over a full-year horizon.
  • Operational costs were reduced by 9.3%, and resilience scores improved by over 20% under adverse conditions (e.g., grid failure, cyberattacks).
  • Convergence speed increased by 42% compared to classical optimization, highlighting computational efficiency and real-time learning feasibility.

Conclusions:

  • The QRO framework offers a scalable and adaptive solution for resilient energy scheduling in PV-H2 microgrids.
  • The integration of quantum-inspired modeling, distributional robustness, and reinforcement learning addresses limitations of traditional methods.
  • The study validates the practical feasibility of advanced AI and optimization techniques for robust microgrid operations.