MA Aokai;NING Chao;

• 1:School of Electronic Information and Electrical Engineering
• 2:Shanghai Jiao Tong University

Abstract：

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.

Key Words： green electricity-hydrogen-ammonia coupling system;renewable energy;distributionally robust optimization;uncertainty;multi-modality information

Abstract:

Keywords:

Foundation: 国家自然科学基金资助项目（62473256）

Authors: MA Aokai;NING Chao;

DOI: 10.13226/j.issn.1006-6772.BB24123101

References：

• [1]高正平，涂安琪，李天新，等.面向零碳电力的氨燃烧技术研究进展[J].洁净煤技术，2022,28(3):173-184.GAO Zhengping,TU Anqi,LI Tianxin,et al. Recent advances on ammonia combustion technology for zero-carbon power[J]. Clean Coal Technology,2022,28(3):173-184.
• [2]邱一苇，吉旭，朱文聪，等.面向新能源规模化消纳的绿氢化工技术研究现状与关键支撑技术展望[J].中国电机工程学报，2023,43(18):6934-6955.QIU Yiwei,JI Xu,ZHU Wencong,et al. Research status of green hydrogen-based chemical engineering technology and prospect of key supporting technologies for large-scale utilization of new energies[J]. Proceedings of the CSEE,2023,43(18):6934-6955.
• [3]刘畅，卓建坤，赵东明，等.利用储能系统实现可再生能源微电网灵活安全运行的研究综述[J].中国电机工程学报，2020,40(1):1-18.LIU Chang,ZHUO Jiankun,ZHAO Dongming,et al. A review on the utilization of energy storage system for the flexible and safe operation of renewable energy microgrids[J]. Proceedings of the CSEE,2020,40(1):1-18.
• [4]舒印彪，陈国平，贺静波，等.构建以新能源为主体的新型电力系统框架研究[J].中国工程科学，2021,23(6):61-69.SHU Yinbiao,CHEN Guoping,HE Jingbo,et al. Building a new electric power system based on new energy sources[J]. Strategic Study of CAE,2021,23(6):61-69.
• [5]张国栋，刘凯.能源互联网背景下的微电网能量管理分析[J].发电技术，2019,40(1):17-21.ZHANG Guodong, LIU Kai. Analysis of microgrid energy management under the background of energy Internet[J]. Power Generation Technology,2019,40(1):17-21.
• [6]LIN S J,WANG Y P,LIU M B,et al. Stochastic optimal dispatch of PV/wind/diesel/battery microgrids using state-space approximate dynamic programming[J]. IET Generation, Transmission&Distribution,2019,13(15):3409-3420.
• [7]GARCIA-TORRES F,BORDONS C,TOBAJAS J,et al. Stochastic optimization of microgrids with hybrid energy storage systems for grid flexibility services considering energy forecast uncertainties[J]. IEEE Transactions on Power Systems, 2021,36(6):5537-5547.
• [8]GUO Z J,BAI J Y,WEI W,et al. Feasibility in multistage robust dispatch with renewables:A recursive characterization and scalable approximation[J]. IEEE Transactions on Automation Science and Engineering,2024,22:1579-1590.
• [9]KHALILI R,KHALEDI A,MARZBAND M,et al. Robust multiobjective optimization for the Iranian electricity market considering green hydrogen and analyzing the performance of different demand response programs[J]. Applied Energy, 2023, 334:120737.
• [10]LI L Y,NING C,QIU H F,et al. Online data-stream-driven distributionally robust optimal energy management for hydrogen-based multimicrogrids[J]. IEEE Transactions on Industrial Informatics,2024,20(3):4370-4384.
• [11]MA X T,NING C,LI L Y,et al. Bayesian nonparametric two-stage distributionally robust unit commitment optimization:From global multimodality to local trimming-Wasserstein ambiguity[J].IEEE Transactions on Power Systems,2024,39(5):6702-6715.
• [12]CHEN C M, WU X Y, LI Y, et al. Distributionally robust dayahead scheduling of park-level integrated energy system considering generalized energy storages[J]. Applied Energy, 2021, 302:117493.
• [13]ZHENG X D,KHODAYAR M E,WANG J H,et al. Distributionally robust multistage dispatch with discrete recourse of energy storage systems[J]. IEEE Transactions on Power Systems, 2024,39(6):6960-6973.
• [14]ZHONG J J,LI Y,WU Y,et al. Optimal operation of energy hub:An integrated model combined distributionally robust optimization method with Stackelberg game[J]. IEEE Transactions on Sustainable Energy,2023,14(3):1835-1848. 1835-1848.
• [15]YU Z P,LIN J,LIU F,et al. Optimal sizing and pricing of gridconnected renewable power to ammonia systems considering the limited flexibility of ammonia synthesis[J]. IEEE Transactions on Power Systems,2024,39(2):3631-3648. 3631-3648.
• [16]ZHU H,LIU H J. Fast local voltage control under limited reactive power:Optimality and stability analysis[J]. IEEE Transactions on Power Systems,2016,31(5):3794-3803.
• [17]MOHAJERIN ESFAHANI P,KUHN D. Data-driven distributionally robust optimization using the Wasserstein metric:Performance guarantees and tractable reformulations[J]. Mathematical Programming,2018,171(1):115-166.
• [18]NING C,MA X T. Data-driven Bayesian nonparametric Wasserstein distributionally robust optimization[J]. IEEE Control Systems Letters,2023,7:3597-3602.
• [19]DUAN C, FANG W L, JIANG L, et al. Distributionally robust chance-constrained approximate AC-OPF with Wasserstein metric[J]. IEEE Transactions on Power Systems, 2018, 33(5):4924-4936.