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Optimization and Scheduling Method for Power Systems Considering Wind Power Forward/Reverse Peaking Scenarios

Author

Listed:
  • Hao Yu

    (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)

  • Shunjiang Wang

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

  • Chunyang Hao

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

  • Jian Xiong

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

Abstract

With the promotion of the dual carbon target, the scale of the wind power grid connection will significantly increase. However, wind power has characteristics such as randomness and volatility, and its grid connection challenges the pressure of system peak shaving, making it increasingly difficult to regulate the power system. To solve the problem of wind power abandonment, the positive and negative peak shaving characteristics of wind power were first analyzed. Based on this, it is proposed that demand response resources and energy storage with adjustable characteristics are used as the new means of wind power consumption. Together with the thermal power units, they participate in the optimization and scheduling of the power grid, forming a coordinated and optimized operation mode of source load storage. With the goal of minimizing system operating costs, a two-stage economic scheduling model was formed for the day-ahead and intra-day periods. Finally, optimization software was used to solve the problem, and the simulation results showed the effectiveness of the proposed economic scheduling model, which can improve the system’s new energy consumption and reduce the system’s operating costs.

Suggested Citation

  • Hao Yu & Yibo Wang & Chuang Liu & Shunjiang Wang & Chunyang Hao & Jian Xiong, 2024. "Optimization and Scheduling Method for Power Systems Considering Wind Power Forward/Reverse Peaking Scenarios," Energies, MDPI, vol. 17(5), pages 1-18, March.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:5:p:1257-:d:1352278
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    References listed on IDEAS

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    1. Ceballos, Salvador & Rea, Judy & Robles, Eider & Lopez, Iraide & Pou, Josep & O'Sullivan, Dara, 2015. "Control strategies for combining local energy storage with wells turbine oscillating water column devices," Renewable Energy, Elsevier, vol. 83(C), pages 1097-1109.
    2. Tuohy, Aidan & Meibom, Peter & Denny, Eleanor & O'Malley, Mark, 2009. "Unit commitment for systems with significant wind penetration," MPRA Paper 34849, University Library of Munich, Germany.
    3. Zhao, Haoran & Wu, Qiuwei & Hu, Shuju & Xu, Honghua & Rasmussen, Claus Nygaard, 2015. "Review of energy storage system for wind power integration support," Applied Energy, Elsevier, vol. 137(C), pages 545-553.
    4. Chen, Maozhi & Lu, Hao & Chang, Xiqiang & Liao, Haiyan, 2023. "An optimization on an integrated energy system of combined heat and power, carbon capture system and power to gas by considering flexible load," Energy, Elsevier, vol. 273(C).
    5. Yang, Hongming & Liang, Rui & Yuan, Yuan & Chen, Bowen & Xiang, Sheng & Liu, Junpeng & Zhao, Huan & Ackom, Emmanuel, 2022. "Distributionally robust optimal dispatch in the power system with high penetration of wind power based on net load fluctuation data," Applied Energy, Elsevier, vol. 313(C).
    6. Roy, Anindita & Kedare, Shireesh B. & Bandyopadhyay, Santanu, 2010. "Optimum sizing of wind-battery systems incorporating resource uncertainty," Applied Energy, Elsevier, vol. 87(8), pages 2712-2727, August.
    7. Chen, Changming & Wu, Xueyan & Li, Yan & Zhu, Xiaojun & Li, Zesen & Ma, Jien & Qiu, Weiqiang & Liu, Chang & Lin, Zhenzhi & Yang, Li & Wang, Qin & Ding, Yi, 2021. "Distributionally robust day-ahead scheduling of park-level integrated energy system considering generalized energy storages," Applied Energy, Elsevier, vol. 302(C).
    8. 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).
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