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A novel forecast scenario-based robust energy management method for integrated rural energy systems with greenhouses

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  • Tan, Hong
  • Li, Zhenxing
  • Wang, Qiujie
  • Mohamed, Mohamed A.

Abstract

Traditional energy supplies in rural areas are mainly rural power grids and fossil energy, which makes energy use inefficient and is not environmentally unfriendly. With the development of agricultural intelligence and the increasing seriousness of environmental problems, rural areas urgently need to make full use of the rich local renewable resources such as biogas and wind power. Meanwhile, it is also necessary to jointly optimize the supply of electricity and heat energy to improve energy utilization efficiency and reduce emissions. In this paper, a cooperative operation framework of the integrated rural energy system with greenhouses (IRES-GH) is introduced and a novel forecast scenario-based robust energy management method is presented to hedge the uncertainty of electric load and wind power output. Based on the forecast bounds of uncertain variables, a two-stage robust optimization (TSRO) model of the IRES-GH is first built. Using the TSRO model, the operation status of the units and energy storage elements in the worst-case scenario can be got. Then, the deterministic dispatch model is constructed based on the obtained operation status of devices and the day-ahead hourly point forecast scenario of uncertain variables to enhance the economy of the dispatch results. Since the second stage of the TSRO model includes binary variables, the nested column and constraint generation (NC&CG) algorithm is used to solve it. The effectiveness of the model and solution method is verified by two cases with different scales.

Suggested Citation

  • Tan, Hong & Li, Zhenxing & Wang, Qiujie & Mohamed, Mohamed A., 2023. "A novel forecast scenario-based robust energy management method for integrated rural energy systems with greenhouses," Applied Energy, Elsevier, vol. 330(PB).
    • Handle: RePEc:eee:appene:v:330:y:2023:i:pb:s0306261922016002
    DOI: 10.1016/j.apenergy.2022.120343
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    References listed on IDEAS

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    Cited by:

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    5. Wang, Yongli & Guo, Lu & Wang, Yanan & Zhang, Yunfei & Zhang, Siwen & Liu, Zeqiang & Xing, Juntai & Liu, Ximei, 2024. "Bi-level programming optimization method of rural integrated energy system based on coupling coordination degree of energy equipment," Energy, Elsevier, vol. 298(C).
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    7. Qiu, Haifeng & Vinod, Ashwin & Lu, Shuai & Gooi, Hoay Beng & Pan, Guangsheng & Zhang, Suhan & Veerasamy, Veerapandiyan, 2023. "Decentralized mixed-integer optimization for robust integrated electricity and heat scheduling," Applied Energy, Elsevier, vol. 350(C).

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