面向绿电–氢–氨耦合系统的自适应分布鲁棒调度优化  

Adaptive distributionally robust scheduling for green electricity-hydrogenammonia coupling systems

作　　者：马翱凯 宁超 MA Aokai;NING Chao(School of Electronic Information and Electrical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China)

机构地区：[1]上海交通大学电子信息与电气工程学院,上海200240

出　　处：《洁净煤技术》2025年第3期120-127,共8页Clean Coal Technology

基　　金：国家自然科学基金资助项目(62473256)。

摘　　要：绿电–氢–氨耦合系统因其在推动零碳经济转型方面的重要作用而备受瞩目,其在促进可持续发展方面已取得了显著成效。然而,绿电–氢–氨耦合系统的优化调度常常受到不确定性带来的挑战,且由于数据的多样性和环境的复杂性,不确定性常表现出多模态特性,而这一潜在特性常被忽视,导致调度结果趋于保守。因此,提出一种面向绿电–氢–氨耦合系统的自适应分布鲁棒调度优化方法,以应对该耦合系统中所出现的可再生能源与需求不确定性。该方法利用聚类算法从不确定量数据集中挖掘多模态信息,并据此构建混合Wasserstein模糊集以准确刻画不确定性分布。同时,采用仿射决策规则求解该分布鲁棒调度问题,实现调度的非预期性与自适应性。最后,该分布鲁棒问题被等价转化为一个易于求解的混合整数线性规划问题。结果表明,在样本内测试中,所提出的方法比基于Wasserstein模糊集的分布鲁棒优化和鲁棒优化分别提升了3.48%和7.54%;而在样本外测试中,该方法比基于Wasserstein模糊集的分布鲁棒优化和鲁棒优化分别提升了2.75%和6.54%。这些结果充分说明了所提出的方法在处理不确定性方面的优越性。The green electricity-hydrogen-ammonia coupling system has gained significant attention for its role in the transition to a zerocarbon economy,with diverse applications promoting sustainable development.However,its scheduling is challenged by multiple uncertainties.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 distributionally robust scheduling framework to manage uncertainties of renewable energy and demands within this coupling system.We adopt clustering algorithms to extract multi-modality information from the uncertainty data and establish a mixture Wasserstein ambiguity set to accurately represent uncertainty distributions.Finally,we equivalently reformulate this distributionally robust scheduling problem into a mixed-integer linear programming problem using affine decision rules,while ensuring non-anticipativity and adaptability.Case studies demonstrate that,in in-sample tests,our method outperforms Wasserstein distributionally robust optimization and robust optimization by 3.48%and 7.54%,respectively.In out-of-sample tests,our method achieves improvements of 2.75%and 6.54%over Wasserstein distributionally robust optimization and robust optimization,respectively.

关 键 词：绿电–氢–氨耦合系统 可再生能源 分布鲁棒优化 不确定性 多模态信息 

分 类 号：TK91[动力工程及工程热物理] TM73[电气工程—电力系统及自动化]