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Day-Ahead Scheduling of IES Containing Solar Thermal Power Generation Based on CNN-MI-BILSTM Considering Source-Load Uncertainty

Author

Listed:
  • Kun Ding

    (Economic and Technological Research Institute of State Grid Gansu Electric Power Company, Lanzhou 730030, China)

  • Yalu Sun

    (Economic and Technological Research Institute of State Grid Gansu Electric Power Company, Lanzhou 730030, China)

  • Boyang Chen

    (State Grid Gansu Electric Power Company, Lanzhou 730030, China)

  • Jing Chen

    (School of Electrical and Power Engineering, Hohai University, Nanjing 211100, China)

  • Lixia Sun

    (School of Electrical and Power Engineering, Hohai University, Nanjing 211100, China)

  • Yingjun Wu

    (School of Electrical and Power Engineering, Hohai University, Nanjing 211100, China)

  • Yusheng Xia

    (School of Electrical and Power Engineering, Hohai University, Nanjing 211100, China)

Abstract

The fluctuating uncertainty of load demand as an influencing factor for day-ahead scheduling of an integrated energy system with photovoltaic (PV) power generation may cause an imbalance between supply and demand, and to solve this problem, this paper proposes a day-ahead optimal scheduling model considering uncertain loads and electric heating appliance (EH)–PV energy storage. The model fuses the multi-interval uncertainty set with the CNN-MI-BILSTM neural network prediction technique, which significantly improves the accuracy and reliability of load prediction and overcomes the limitations of traditional methods in dealing with load volatility. By integrating the EH–photothermal storage module, the model achieves efficient coupled power generation and thermal storage operation, aiming to optimize economic targets while enhancing the grid’s peak-shaving and valley-filling capabilities and utilization of renewable energy. The validity of the proposed model is verified by algorithm prediction simulation and day-ahead scheduling experiments under different configurations.

Suggested Citation

  • Kun Ding & Yalu Sun & Boyang Chen & Jing Chen & Lixia Sun & Yingjun Wu & Yusheng Xia, 2025. "Day-Ahead Scheduling of IES Containing Solar Thermal Power Generation Based on CNN-MI-BILSTM Considering Source-Load Uncertainty," Energies, MDPI, vol. 18(9), pages 1-23, April.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:9:p:2160-:d:1640618
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    References listed on IDEAS

    as
    1. Puming Wang & Liqin Zheng & Tianyi Diao & Shengquan Huang & Xiaoqing Bai, 2023. "Robust Bilevel Optimal Dispatch of Park Integrated Energy System Considering Renewable Energy Uncertainty," Energies, MDPI, vol. 16(21), pages 1-23, October.
    2. Chao Tang & Yufeng Zhang & Fan Wu & Zhuo Tang, 2024. "An Improved CNN-BILSTM Model for Power Load Prediction in Uncertain Power Systems," Energies, MDPI, vol. 17(10), pages 1-16, May.
    3. Min Xu & Wanwei Li & Zhihui Feng & Wangwang Bai & Lingling Jia & Zhanhong Wei, 2023. "Economic Dispatch Model of High Proportional New Energy Grid-Connected Consumption Considering Source Load Uncertainty," Energies, MDPI, vol. 16(4), pages 1-20, February.
    4. Rubasinghe, Osaka & Zhang, Tingze & Zhang, Xinan & Choi, San Shing & Chau, Tat Kei & Chow, Yau & Fernando, Tyrone & Iu, Herbert Ho-Ching, 2023. "Highly accurate peak and valley prediction short-term net load forecasting approach based on decomposition for power systems with high PV penetration," Applied Energy, Elsevier, vol. 333(C).
    5. Zhen Zhang & Wenjun Xian & Weijun Tan & Jinghua Li & Xiaofeng Liu, 2024. "Research on Safe-Economic Dispatch Strategy for Renewable Energy Power Stations Based on Game-Fairness Empowerment," Energies, MDPI, vol. 17(23), pages 1-19, December.
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