Optimization design of hydrogen energy supported microgrid network capacity based on hydrogen energy equipment behavior pattern inversion under uncertain conditions

With the increasing drawbacks of fossil energy characterized by high consumption and emissions, the goals of carbon peaking and carbon neutrality have become central to structural transformation in the energy sector. The study addresses the dual challenges faced by regional hydrogen-supported microgrid networking, namely, uncertain source-load conditions and equipment health states. A novel networking optimization method is proposed that integrates uncertainty modeling, health assessment, and multi-criteria decision-making, aiming to achieve economically optimal, safe, and flexible collaborative operation of microgrids. First, probabilistic statistics and scenario generation techniques are employed to accurately quantify stochastic fluctuations from renewable generation and load demand, thereby enhancing the adaptability of networking schemes through multi-scenario simulations. Second, a dynamic state of health (SOH) assessment is introduced, embedding degradation models of proton exchange membrane fuel cells (PEMFCs) and proton exchange membrane electrolyzers (PEMECs) into the planning process to enable resource allocation optimization over time. Third, a multidimensional evaluation framework is constructed, encompassing operational costs, long-term health state variations, and total investment costs. Finally, under different source-load quantile scenarios, the proposed method demonstrates superior economic efficiency and robustness in PEMFC and PEMEC capacity configuration compared with conventional mixed-integer programming approaches. Moreover, the incorporation of SOH constraints significantly improves both energy output and economic performance of the system.