Explicit modeling of multi-energy complementarity mechanism for uncertainty mitigation: A multi-stage robust optimization approach for energy management of hydrogen-based microgrids

To reduce carbon emissions, hydrogen-based multi-energy microgrids (H-MEMGs) have been developed rapidly. However, the growing uncertainties of multiple energy types and their complementary characteristics have proposed new opportunities and challenges to the operation of H-MEMGs. To address these challenges, this paper proposes an uncertainty set conversion model, explicitly formulating the complementary characteristics of multiple energy sources to mitigate the fluctuation of uncertainty. The main idea is to formulate two groups of auxiliary intervals to describe the uncertainty mitigation capacity and the associated cost for energy conversion devices. Besides, by integrating energy storage systems, the uncertainty sets can be rearranged across different energy types and time periods, effectively reducing the impact of individual energy fluctuation and intermittency. Based on the uncertainty set conversion model, an adaptive decision-making strategy is proposed for the optimal energy management of H-MEMGs. Unlike traditional robust optimization approaches that rely on affine decision rules, the proposed method eliminates the requirement for affine assumptions while adaptively optimizing decision strategies within predefined feasible regions, potentially leading to higher solution quality. Numerical tests are implemented on a real H-MEMG. The results demonstrate that the proposed method exhibits better performance. Specifically, it reduces operational costs compared to conventional robust optimization approaches. Furthermore, the proposed method achieves better feasibility guarantees and higher computational efficiency relative to traditional stochastic optimization methods.