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Day-Ahead and Intra-Day Optimal Scheduling of Integrated Energy System Considering Uncertainty of Source & Load Power Forecasting

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  • Zhengjie Li

    (College of Electrical Engineering, Qingdao University, Qingdao 266071, China)

  • Zhisheng Zhang

    (College of Electrical Engineering, Qingdao University, Qingdao 266071, China)

Abstract

At present, due to the errors of wind power, solar power and various types of load forecasting, the optimal scheduling results of the integrated energy system (IES) will be inaccurate, which will affect the economic and reliable operation of the integrated energy system. In order to solve this problem, a day-ahead and intra-day optimal scheduling model of integrated energy system considering forecasting uncertainty is proposed in this paper, which takes the minimum operation cost of the system as the target, and different processing strategies are adopted for the model. In the day-ahead time scale, according to day-ahead load forecasting, an integrated demand response (IDR) strategy is formulated to adjust the load curve, and an optimal scheduling scheme is obtained. In the intra-day time scale, the predicted value of wind power, solar power and load power are represented by fuzzy parameters to participate in the optimal scheduling of the system, and the output of units is adjusted based on the day-ahead scheduling scheme according to the day-ahead forecasting results. The simulation of specific examples shows that the integrated demand response can effectively adjust the load demand and improve the economy and reliability of the system operation. At the same time, the operation cost of the system is related to the reliability of the accurate prediction of wind power, solar power and load power. Through this model, the optimal scheduling scheme can be determined under an acceptable prediction accuracy and confidence level.

Suggested Citation

  • Zhengjie Li & Zhisheng Zhang, 2021. "Day-Ahead and Intra-Day Optimal Scheduling of Integrated Energy System Considering Uncertainty of Source & Load Power Forecasting," Energies, MDPI, vol. 14(9), pages 1-14, April.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:9:p:2539-:d:545562
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    References listed on IDEAS

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    1. Lin, Boqiang & Chen, Yufang, 2019. "Impacts of policies on innovation in wind power technologies in China," Applied Energy, Elsevier, vol. 247(C), pages 682-691.
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    Cited by:

    1. 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.
    2. Sameer Al-Dahidi & Piero Baraldi & Enrico Zio & Lorenzo Montelatici, 2021. "Bootstrapped Ensemble of Artificial Neural Networks Technique for Quantifying Uncertainty in Prediction of Wind Energy Production," Sustainability, MDPI, vol. 13(11), pages 1-19, June.
    3. Zhihan Shi & Weisong Han & Guangming Zhang & Zhiqing Bai & Mingxiang Zhu & Xiaodong Lv, 2022. "Research on Low-Carbon Energy Sharing through the Alliance of Integrated Energy Systems with Multiple Uncertainties," Energies, MDPI, vol. 15(24), pages 1-20, December.
    4. Wang, Liying & Lin, Jialin & Dong, Houqi & Wang, Yuqing & Zeng, Ming, 2023. "Demand response comprehensive incentive mechanism-based multi-time scale optimization scheduling for park integrated energy system," Energy, Elsevier, vol. 270(C).
    5. Li, Songrui & Zhang, Lihui & Nie, Lei & Wang, Jianing, 2022. "Trading strategy and benefit optimization of load aggregators in integrated energy systems considering integrated demand response: A hierarchical Stackelberg game," Energy, Elsevier, vol. 249(C).
    6. Xinyu Sun & Hao Wu & Siqi Guo & Lingwei Zheng, 2022. "Day-Ahead Optimal Scheduling of Integrated Energy System Based on Type-II Fuzzy Interval Chance-Constrained Programming," Energies, MDPI, vol. 15(18), pages 1-17, September.
    7. Lingling Li & Jiarui Pei & Qiang Shen, 2023. "A Review of Research on Dynamic and Static Economic Dispatching of Hybrid Wind–Thermal Power Microgrids," Energies, MDPI, vol. 16(10), pages 1-23, May.
    8. Li, Ke & Ye, Ning & Li, Shuzhen & Wang, Haiyang & Zhang, Chenghui, 2023. "Distributed collaborative operation strategies in multi-agent integrated energy system considering integrated demand response based on game theory," Energy, Elsevier, vol. 273(C).
    9. 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.
    10. Anderson, Benjamin & Rane, Jayaraj & Khan, Rabia, 2023. "Distributed wind-hybrid microgrids with autonomous controls and forecasting," Applied Energy, Elsevier, vol. 333(C).

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