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A data-driven distributionally robust optimization model for multi-energy coupled system considering the temporal-spatial correlation and distribution uncertainty of renewable energy sources

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  • Zhang, Yachao
  • Liu, Yan
  • Shu, Shengwen
  • Zheng, Feng
  • Huang, Zhanghao

Abstract

The increasing expansion of wind and gas turbine installation has intensified the interdependency between power system and natural gas network, which poses great challenges to the coordination scheduling of the multi-energy coupled system (MECS). The data-driven robust optimization (DDRO) model is proposed for the energy coupled system. In this model, the temporal-spatial correlation of wind power can be considered based on the minimum volume enclosing convex hull uncertainty set, and the confidence set about the probability distribution for wind power scenarios with the form of the norm-1 and norm-inf constraints is constructed to handle wind power uncertainty. Moreover, to describe natural gas transient characteristics, the hydrodynamic model for gas flow represented as a series of partial differential equations is transformed by the Wendroff difference scheme and linearization technique. And then the master-subproblem framework and tri-level duality-free decomposition method is developed to solve the above model. Finally, the proposed model and solving method are carried out on two test systems with different scale, and the robust optimization models and distributionally optimization models in the existing literatures are implemented for comparison. Simulation results demonstrate the effectiveness and superiority of the proposed model for solving the coordination scheduling problem of MECS.

Suggested Citation

  • Zhang, Yachao & Liu, Yan & Shu, Shengwen & Zheng, Feng & Huang, Zhanghao, 2021. "A data-driven distributionally robust optimization model for multi-energy coupled system considering the temporal-spatial correlation and distribution uncertainty of renewable energy sources," Energy, Elsevier, vol. 216(C).
    • Handle: RePEc:eee:energy:v:216:y:2021:i:c:s0360544220322787
    DOI: 10.1016/j.energy.2020.119171
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    Cited by:

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    12. Pourmohammadi, Pardis & Saif, Ahmed, 2023. "Robust metamodel-based simulation-optimization approaches for designing hybrid renewable energy systems," Applied Energy, Elsevier, vol. 341(C).
    13. Cai, Pengcheng & Mi, Yang & Ma, Siyuan & Li, Hongzhong & Li, Dongdong & Wang, Peng, 2023. "Hierarchical game for integrated energy system and electricity-hydrogen hybrid charging station under distributionally robust optimization," Energy, Elsevier, vol. 283(C).
    14. Ullah, Zia & Elkadeem, M.R. & Kotb, Kotb M. & Taha, Ibrahim B.M. & Wang, Shaorong, 2021. "Multi-criteria decision-making model for optimal planning of on/off grid hybrid solar, wind, hydro, biomass clean electricity supply," Renewable Energy, Elsevier, vol. 179(C), pages 885-910.
    15. Liang, Weikun & Lin, Shunjiang & Lei, Shunbo & Xie, Yuquan & Tang, Zhiqiang & Liu, Mingbo, 2022. "Distributionally robust optimal dispatch of CCHP campus microgrids considering the time-delay of pipelines and the uncertainty of renewable energy," Energy, Elsevier, vol. 239(PC).
    16. Qing, Ke & Du, Yuefang & Huang, Qi & Duan, Chao & Hu, Weihao, 2024. "Energy scheduling for microgrids with renewable energy sources considering an adjustable convex hull based uncertainty set," Renewable Energy, Elsevier, vol. 220(C).
    17. Liang, Weikun & Lin, Shunjiang & Liu, Mingbo & Sheng, Xuan & Pan, Yue & Liu, Yun, 2023. "Risk assessment for cascading failures in regional integrated energy system considering the pipeline dynamics," Energy, Elsevier, vol. 270(C).

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