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Two-Stage Multi-Objective Collaborative Scheduling for Wind Farm and Battery Switch Station

Author

Listed:
  • Zhe Jiang

    (Department of Electrical Engineering, Harbin Institute of Technology, Harbin 150001, China
    Electric Power Research Institute, State Grid Shandong Electric Power Company, Jinan 250003, China)

  • Xueshan Han

    (Key Laboratory of Power System Intelligent Dispatch and Control, Shandong University, Jinan 250061, China
    Collaborative Innovation Center for Global Energy Interconnection (Shandong), Jinan 250061, China)

  • Zhimin Li

    (Department of Electrical Engineering, Harbin Institute of Technology, Harbin 150001, China)

  • Wenbo Li

    (Electric Power Research Institute, State Grid Shandong Electric Power Company, Jinan 250003, China)

  • Mengxia Wang

    (Key Laboratory of Power System Intelligent Dispatch and Control, Shandong University, Jinan 250061, China
    Collaborative Innovation Center for Global Energy Interconnection (Shandong), Jinan 250061, China)

  • Mingqiang Wang

    (Key Laboratory of Power System Intelligent Dispatch and Control, Shandong University, Jinan 250061, China
    Collaborative Innovation Center for Global Energy Interconnection (Shandong), Jinan 250061, China)

Abstract

In order to deal with the uncertainties of wind power, wind farm and electric vehicle (EV) battery switch station (BSS) were proposed to work together as an integrated system. In this paper, the collaborative scheduling problems of such a system were studied. Considering the features of the integrated system, three indices, which include battery swapping demand curtailment of BSS, wind curtailment of wind farm, and generation schedule tracking of the integrated system are proposed. In addition, a two-stage multi-objective collaborative scheduling model was designed. In the first stage, a day-ahead model was built based on the theory of dependent chance programming. With the aim of maximizing the realization probabilities of these three operating indices, random fluctuations of wind power and battery switch demand were taken into account simultaneously. In order to explore the capability of BSS as reserve, the readjustment process of the BSS within each hour was considered in this stage. In addition, the stored energy rather than the charging/discharging power of BSS during each period was optimized, which will provide basis for hour-ahead further correction of BSS. In the second stage, an hour-ahead model was established. In order to cope with the randomness of wind power and battery swapping demand, the proposed hour-ahead model utilized ultra-short term prediction of the wind power and the battery switch demand to schedule the charging/discharging power of BSS in a rolling manner. Finally, the effectiveness of the proposed models was validated by case studies. The simulation results indicated that the proposed model could realize complement between wind farm and BSS, reduce the dependence on power grid, and facilitate the accommodation of wind power.

Suggested Citation

  • Zhe Jiang & Xueshan Han & Zhimin Li & Wenbo Li & Mengxia Wang & Mingqiang Wang, 2016. "Two-Stage Multi-Objective Collaborative Scheduling for Wind Farm and Battery Switch Station," Energies, MDPI, vol. 9(11), pages 1-17, October.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:11:p:886-:d:81691
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    References listed on IDEAS

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    1. Ding, Huajie & Hu, Zechun & Song, Yonghua, 2012. "Stochastic optimization of the daily operation of wind farm and pumped-hydro-storage plant," Renewable Energy, Elsevier, vol. 48(C), pages 571-578.
    2. Wei Wang & Chengxiong Mao & Jiming Lu & Dan Wang, 2013. "An Energy Storage System Sizing Method for Wind Power Integration," Energies, MDPI, vol. 6(7), pages 1-13, July.
    3. Douglas Halamay & Michael Antonishen & Kelcey Lajoie & Arne Bostrom & Ted K. A. Brekken, 2014. "Improving Wind Farm Dispatchability Using Model Predictive Control for Optimal Operation of Grid-Scale Energy Storage," Energies, MDPI, vol. 7(9), pages 1-16, September.
    4. 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.
    5. Deyou Yang & Jiaxin Wen & Ka-wing Chan & Guowei Cai, 2016. "Dispatching of Wind/Battery Energy Storage Hybrid Systems Using Inner Point Method-Based Model Predictive Control," Energies, MDPI, vol. 9(8), pages 1-16, August.
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    Cited by:

    1. Zhe Jiang & Xueshan Han & Zhimin Li & Mingqiang Wang & Guodong Liu & Mengxia Wang & Wenbo Li & Thomas B. Ollis, 2018. "Capacity Optimization of a Centralized Charging Station in Joint Operation with a Wind Farm," Energies, MDPI, vol. 11(5), pages 1-18, May.

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