IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v376y2024ipas030626192401434x.html
   My bibliography  Save this article

Multi-stage fully adaptive distributionally robust unit commitment for power system based on mixed approximation rules

Author

Listed:
  • Liu, Mao
  • Kong, Xiangyu
  • Ma, Chao
  • Zhou, Xuesong
  • Lin, Qingxiang

Abstract

The escalating integration of renewable energy sources necessitates enhanced power system flexibility. Gas units, with their rapid start-stop capabilities, emerge as crucial assets for system operators grappling with supply-demand fluctuations. This paper proposes a novel multi-stage fully adaptive distributionally robust unit commitment (MFA-DRUC) model to optimize the operation of these flexible units under the uncertainties inherent in real-time dispatch. Leveraging the Wasserstein metric, our approach significantly expands the feasible solution space compared to traditional multi-stage adaptive unit commitment (MA-DRUC) models, bolstering resilience against extreme scenarios. To overcome the computational challenges posed by the model's multi-stage structure, we introduce a mixed approximation rule (MAR) that effectively handles high-dimensional variables and strong coupling characteristics. By employing duality theory, we transform the unit commitment (UC) problem into a computationally tractable mixed-integer linear programming problem. Comprehensive simulations across power systems of varying scales, encompassing scenarios such as coal-fired unit decommissioning and gas unit integration, validate the efficacy of our proposed MFA-DRUC model. These results underscore its potential to enhance the reliability and efficiency of power systems navigating the complexities of a renewables-driven future.

Suggested Citation

  • Liu, Mao & Kong, Xiangyu & Ma, Chao & Zhou, Xuesong & Lin, Qingxiang, 2024. "Multi-stage fully adaptive distributionally robust unit commitment for power system based on mixed approximation rules," Applied Energy, Elsevier, vol. 376(PA).
    • Handle: RePEc:eee:appene:v:376:y:2024:i:pa:s030626192401434x
    DOI: 10.1016/j.apenergy.2024.124051
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S030626192401434X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2024.124051?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Yang, Xiao & Li, Yuanzheng & Zhao, Yong & Yu, Yaowen & Lian, Yicheng & Hao, Guokai & Jiang, Lin, 2023. "Data-driven nested robust optimization for generation maintenance scheduling considering temporal correlation," Energy, Elsevier, vol. 278(C).
    2. Jianzhe Zhen & Frans J. C. T. de Ruiter & Ernst Roos & Dick den Hertog, 2022. "Robust Optimization for Models with Uncertain Second-Order Cone and Semidefinite Programming Constraints," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 196-210, January.
    3. Dagui Liu & Weiqing Wang & Huie Zhang & Wei Shi & Caiqing Bai & Huimin Zhang, 2023. "Day-Ahead and Intra-Day Optimal Scheduling Considering Wind Power Forecasting Errors," Sustainability, MDPI, vol. 15(14), pages 1-17, July.
    4. Khojasteh, Meysam & Faria, Pedro & Lezama, Fernando & Vale, Zita, 2022. "Optimal strategy of electricity and natural gas aggregators in the energy and balance markets," Energy, Elsevier, vol. 257(C).
    5. Moretti, Luca & Martelli, Emanuele & Manzolini, Giampaolo, 2020. "An efficient robust optimization model for the unit commitment and dispatch of multi-energy systems and microgrids," Applied Energy, Elsevier, vol. 261(C).
    6. Jin, Xiaoyu & Liu, Benxi & Liao, Shengli & Cheng, Chuntian & Yan, Zhiyu, 2022. "A Wasserstein metric-based distributionally robust optimization approach for reliable-economic equilibrium operation of hydro-wind-solar energy systems," Renewable Energy, Elsevier, vol. 196(C), pages 204-219.
    7. Liu, Yixin & Shi, Haoqi & Guo, Li & Xu, Tao & Zhao, Bo & Wang, Chengshan, 2022. "Towards long-period operational reliability of independent microgrid: A risk-aware energy scheduling and stochastic optimization method," Energy, Elsevier, vol. 254(PB).
    8. Jin, Xiaoyu & Liu, Benxi & Liao, Shengli & Cheng, Chuntian & Zhang, Yi & Zhao, Zhipeng & Lu, Jia, 2022. "Wasserstein metric-based two-stage distributionally robust optimization model for optimal daily peak shaving dispatch of cascade hydroplants under renewable energy uncertainties," Energy, Elsevier, vol. 260(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Huang, Wenwei & Qian, Tong & Tang, Wenhu & Wu, Jianzhong, 2025. "A distributionally robust chance constrained optimization approach for security-constrained optimal power flow problems considering dependent uncertainty of wind power," Applied Energy, Elsevier, vol. 383(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Liu, Benxi & Liu, Tengyuan & Liao, Shengli & Lu, Jia & Cheng, Chuntian, 2023. "Short-term coordinated hybrid hydro-wind-solar optimal scheduling model considering multistage section restrictions," Renewable Energy, Elsevier, vol. 217(C).
    2. Wang, Jin & Zhao, Zhipeng & Zhou, Jinglin & Cheng, Chuntian & Su, Huaying, 2024. "Co-optimization for day-ahead scheduling and flexibility response mode of a hydro–wind–solar hybrid system considering forecast uncertainty of variable renewable energy," Energy, Elsevier, vol. 311(C).
    3. Duan, Jiandong & Gao, Qi & Xia, Yerui & Tian, Qinxing & Qin, Bo, 2024. "MMD-DRO based economic dispatching considering flexible reserve provision from concentrated solar power plant," Energy, Elsevier, vol. 308(C).
    4. Jie Hao & Xiuting Guo & Yan Li & Tao Wu, 2024. "Uncertain Scheduling of the Power System Based on Wasserstein Distributionally Robust Optimization and Improved Differential Evolution Algorithm," Energies, MDPI, vol. 17(15), pages 1-21, August.
    5. Zhao, Hongye & Liao, Shengli & Ma, Xiangyu & Fang, Zhou & Cheng, Chuntian & Zhang, Zheng, 2024. "Short-term peak-shaving scheduling of a hydropower-dominated hydro-wind-solar photovoltaic hybrid system considering a shared multienergy coupling transmission channel," Applied Energy, Elsevier, vol. 372(C).
    6. Fan, Guozhu & Peng, Chunhua & Wang, Xuekui & Wu, Peng & Yang, Yifan & Sun, Huijuan, 2024. "Optimal scheduling of integrated energy system considering renewable energy uncertainties based on distributionally robust adaptive MPC," Renewable Energy, Elsevier, vol. 226(C).
    7. Liu, Mao & Kong, Xiangyu & Lian, Jijian & Wang, Jimin & Yang, Bohan, 2025. "Distributionally robust coordinated day-ahead scheduling of Cascade pumped hydro energy storage system and DC transmission," Applied Energy, Elsevier, vol. 384(C).
    8. Han, Fengwu & Zeng, Jianfeng & Lin, Junjie & Gao, Chong & Ma, Zeyang, 2023. "A novel two-layer nested optimization method for a zero-carbon island integrated energy system, incorporating tidal current power generation," Renewable Energy, Elsevier, vol. 218(C).
    9. Chong Wu & Tong Xu & Shenhao Yang & Yong Zheng & Xiaobin Yan & Maoyu Mao & Ziyi Jiang & Qian Li, 2025. "Gas−Hydro Coordinated Peaking Considering Source-Load Uncertainty and Deep Peaking," Energies, MDPI, vol. 18(5), pages 1-23, March.
    10. Son, Yeong Geon & Kim, Sung Yul, 2025. "Distributionally robust planning for power-to- gas integrated large wind farm systems incorporating hydrogen production switch control model," Energy, Elsevier, vol. 314(C).
    11. Polimeni, Simone & Moretti, Luca & Martelli, Emanuele & Leva, Sonia & Manzolini, Giampaolo, 2023. "A novel stochastic model for flexible unit commitment of off-grid microgrids," Applied Energy, Elsevier, vol. 331(C).
    12. Armghan, Hammad & Xu, Yinliang & Bai, Xiang & Ali, Naghmash & Chang, Xinyue & Xue, Yixun, 2024. "A tri-level control framework for carbon-aware multi-energy microgrid cluster considering shared hydrogen energy storage," Applied Energy, Elsevier, vol. 373(C).
    13. Jiang, Sufan & Gao, Shan & Pan, Guangsheng & Zhao, Xin & Liu, Yu & Guo, Yasen & Wang, Sicheng, 2020. "A novel robust security constrained unit commitment model considering HVDC regulation," Applied Energy, Elsevier, vol. 278(C).
    14. Wang, Yi & Qiu, Dawei & Sun, Mingyang & Strbac, Goran & Gao, Zhiwei, 2023. "Secure energy management of multi-energy microgrid: A physical-informed safe reinforcement learning approach," Applied Energy, Elsevier, vol. 335(C).
    15. Wang, Yubin & Dong, Wei & Yang, Qiang, 2022. "Multi-stage optimal energy management of multi-energy microgrid in deregulated electricity markets," Applied Energy, Elsevier, vol. 310(C).
    16. Ye, Jin & Shuai, Qilin & Hua, Qingsong, 2025. "Dynamic programming-based low-carbon and economic scheduling of integrated energy system," Energy, Elsevier, vol. 322(C).
    17. Lu Wei & Yiyin Li & Boyu Xie & Ke Xu & Gaojun Meng, 2024. "Two-Stage Optimal Scheduling Based on the Meteorological Prediction of a Wind–Solar-Energy Storage System with Demand Response," Energies, MDPI, vol. 17(6), pages 1-13, March.
    18. Lan, Penghang & Chen, She & Li, Qihang & Li, Kelin & Wang, Feng & Zhao, Yaoxun, 2024. "Intelligent hydrogen-ammonia combined energy storage system with deep reinforcement learning," Renewable Energy, Elsevier, vol. 237(PB).
    19. Wang, Jin & Zhao, Zhipeng & Zhou, Jinglin & Cheng, Chuntian & Su, Huaying, 2024. "Developing operating rules for a hydro–wind–solar hybrid system considering peak-shaving demands," Applied Energy, Elsevier, vol. 360(C).
    20. Zhang, Liu & Zhang, Kaitian & Zheng, Zhong & Chai, Yi & Lian, Xiaoyuan & Zhang, Kai & Xu, Zhaojun & Chen, Sujun, 2023. "Two-stage distributionally robust integrated scheduling of oxygen distribution and steelmaking-continuous casting in steel enterprises," Applied Energy, Elsevier, vol. 351(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:376:y:2024:i:pa:s030626192401434x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.