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

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  • 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
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    References listed on IDEAS

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    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).
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