The green electricity-hydrogen-ammonia coupling system has gained significant attention for its role in the transition to a zero-carbon economy,with diverse applications promoting sustainable development. However,its scheduling is challenged by multipleuncertainties. Due to the diversity of data and the complexity of the environment,uncertainties often exhibit multimodal characteristics,but this is often overlooked,resulting in conservative scheduling outcomes. To this end,this paper proposes an adaptive distributionallyrobust scheduling framework to manage uncertainties of renewable energy and demands within this coupling system. We adopt clusteringalgorithms to extract multi-modality information from the uncertainty data and establish a mixture Wasserstein ambiguity set toaccurately represent uncertainty distributions. Finally,we equivalently reformulate this distributionally robust scheduling problem into amixed-integer linear programming problem using affine decision rules,while ensuring non-anticipativity and adaptability. Case studiesdemonstrate that,in in-sample tests,our method outperforms Wasserstein distributionally robust optimization and robust optimizationby 3.48% and 7.54%,respectively. In out-of-sample tests,our method achieves improvements of 2.75% and 6.54% over Wassersteindistributionally robust optimization and robust optimization,respectively.

马翱凯(2000—),男,浙江嵊州人,硕士研究生。E-mail:akai.ma@sjtu.edu.cn

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MA Aokai,NING Chao. Adaptive distributionally robust scheduling for green electricity-hydrogen-ammonia couplingsystems[J].Clean Coal Technology,2025,31(3):119−126.