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Adaptive Robust Optimal Scheduling of Combined Heat and Power Microgrids Based on Photovoltaic Mechanism/Data Fusion-Driven Power Prediction

Author

Listed:
  • Yueyang Xu

    (School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China)

  • Yibo Wang

    (School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China)

  • Chuang Liu

    (School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China)

  • Jian Xiong

    (School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China)

  • Mo Zhou

    (School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China)

  • Yang Du

    (School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China)

Abstract

In order to effectively deal with the adverse effects of the randomness of photovoltaic output on the operation of combined heat and power (CHP) microgrids, this paper proposes an adaptive robust optimal scheduling strategy for CHP microgrids based on photovoltaic mechanism/data fusion-driven power prediction. Firstly, the mechanism of the clear sky radiation model is used to calculate the photovoltaic clear sky limit output and random output, and the latter is reorganized in different periods by using the idea of similar days. Then, the data-driven random prediction results are superimposed with the clear sky limit output, the photovoltaic mechanism/data fusion-driven power prediction model is established, and the fusion-driven power prediction framework is provided. Secondly, the boundary information of uncertain factors is deeply explored, and an adaptive robust uncertainty set considering the confidence interval of predictive error statistical information is constructed. On this basis, a robust optimization model of CHP microgrids with the lowest operating cost is proposed, and the optimization model is solved by column and constraint generation algorithm. Finally, the rationality and effectiveness of the proposed model are verified through simulation examples and analytical calculations.

Suggested Citation

  • Yueyang Xu & Yibo Wang & Chuang Liu & Jian Xiong & Mo Zhou & Yang Du, 2025. "Adaptive Robust Optimal Scheduling of Combined Heat and Power Microgrids Based on Photovoltaic Mechanism/Data Fusion-Driven Power Prediction," Energies, MDPI, vol. 18(3), pages 1-23, February.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:3:p:732-:d:1584103
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    References listed on IDEAS

    as
    1. Zhang, Chao & Yin, Wanjun & Wen, Tao, 2024. "An advanced multi-objective collaborative scheduling strategy for large scale EV charging and discharging connected to the predictable wind power grid," Energy, Elsevier, vol. 287(C).
    2. Ma, Xin & Peng, Bo & Ma, Xiangxue & Tian, Changbin & Yan, Yi, 2023. "Multi-timescale optimization scheduling of regional integrated energy system based on source-load joint forecasting," Energy, Elsevier, vol. 283(C).
    3. Yang, Xiaohui & Wang, Xiaopeng & Leng, Zhengyang & Deng, Yeheng & Deng, Fuwei & Zhang, Zhonglian & Yang, Li & Liu, Xiaoping, 2023. "An optimized scheduling strategy combining robust optimization and rolling optimization to solve the uncertainty of RES-CCHP MG," Renewable Energy, Elsevier, vol. 211(C), pages 307-325.
    4. Lyu, Zhilin & Ni, Xingyu & Bai, Xiaoqing & Wang, Chongyang & Liu, Bin, 2024. "CNN data-driven active distribution network: Integration research of topology reconstruction and optimal scheduling in multi-source uncertain environment," Energy, Elsevier, vol. 309(C).
    5. Yang, Mao & Wang, Jinxin & Chen, Yiming & Zeng, Yuxuan & Su, Xin, 2024. "Data-driven robust optimization scheduling for microgrid day-ahead to intra-day operations based on renewable energy interval prediction," Energy, Elsevier, vol. 313(C).
    6. Wu, Thomas & Hu, Ruifeng & Zhu, Hongyu & Jiang, Meihui & Lv, Kunye & Dong, Yunxuan & Zhang, Dongdong, 2024. "Combined IXGBoost-KELM short-term photovoltaic power prediction model based on multidimensional similar day clustering and dual decomposition," Energy, Elsevier, vol. 288(C).
    7. Wang, Jianzhou & Yu, Yue & Zeng, Bo & Lu, Haiyan, 2024. "Hybrid ultra-short-term PV power forecasting system for deterministic forecasting and uncertainty analysis," Energy, Elsevier, vol. 288(C).
    8. Ye, Feng & Brodie, Joseph & Miles, Travis & Aziz Ezzat, Ahmed, 2024. "AIRU-WRF: A physics-guided spatio-temporal wind forecasting model and its application to the U.S. Mid Atlantic offshore wind energy areas," Renewable Energy, Elsevier, vol. 223(C).
    9. Stienecker, Malte, 2024. "Impact of forecasted heat demand on day-ahead optimal scheduling and real time control of multi-energy systems," Energy, Elsevier, vol. 297(C).
    10. Zheng, Yingying & Zhao, Wenjing & Varga, Monika & Li, Daoliang, 2024. "Stochastic energy management of large industrial-scale aquaponics considering robust optimization-based demand response program," Applied Energy, Elsevier, vol. 374(C).
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