IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v9y2016i11p886-d81691.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/9/11/886/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/9/11/886/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shaohua Hu & Xinlong Zhou & Yi Luo & Guang Zhang, 2019. "Numerical Simulation Three-Dimensional Nonlinear Seepage in a Pumped-Storage Power Station: Case Study," Energies, MDPI, vol. 12(1), pages 1-15, January.
    2. Feras Alasali & Stephen Haben & Victor Becerra & William Holderbaum, 2017. "Optimal Energy Management and MPC Strategies for Electrified RTG Cranes with Energy Storage Systems," Energies, MDPI, vol. 10(10), pages 1-18, October.
    3. Moradi, Jalal & Shahinzadeh, Hossein & Khandan, Amirsalar & Moazzami, Majid, 2017. "A profitability investigation into the collaborative operation of wind and underwater compressed air energy storage units in the spot market," Energy, Elsevier, vol. 141(C), pages 1779-1794.
    4. Xiaodong Yu & Xia Dong & Shaopeng Pang & Luanai Zhou & Hongzhi Zang, 2019. "Energy Storage Sizing Optimization and Sensitivity Analysis Based on Wind Power Forecast Error Compensation," Energies, MDPI, vol. 12(24), pages 1-21, December.
    5. Chen, Long Xiang & Xie, Mei Na & Zhao, Pan Pan & Wang, Feng Xiang & Hu, Peng & Wang, Dong Xiang, 2018. "A novel isobaric adiabatic compressed air energy storage (IA-CAES) system on the base of volatile fluid," Applied Energy, Elsevier, vol. 210(C), pages 198-210.
    6. Qin, Chao & Saunders, Gordon & Loth, Eric, 2017. "Offshore wind energy storage concept for cost-of-rated-power savings," Applied Energy, Elsevier, vol. 201(C), pages 148-157.
    7. Gui, Yonghao & Wei, Baoze & Li, Mingshen & Guerrero, Josep M. & Vasquez, Juan C., 2018. "Passivity-based coordinated control for islanded AC microgrid," Applied Energy, Elsevier, vol. 229(C), pages 551-561.
    8. Cheng, Meng & Sami, Saif Sabah & Wu, Jianzhong, 2017. "Benefits of using virtual energy storage system for power system frequency response," Applied Energy, Elsevier, vol. 194(C), pages 376-385.
    9. Nyong-Bassey, Bassey Etim & Giaouris, Damian & Patsios, Charalampos & Papadopoulou, Simira & Papadopoulos, Athanasios I. & Walker, Sara & Voutetakis, Spyros & Seferlis, Panos & Gadoue, Shady, 2020. "Reinforcement learning based adaptive power pinch analysis for energy management of stand-alone hybrid energy storage systems considering uncertainty," Energy, Elsevier, vol. 193(C).
    10. Nallapaneni Manoj Kumar & Aneesh A. Chand & Maria Malvoni & Kushal A. Prasad & Kabir A. Mamun & F.R. Islam & Shauhrat S. Chopra, 2020. "Distributed Energy Resources and the Application of AI, IoT, and Blockchain in Smart Grids," Energies, MDPI, vol. 13(21), pages 1-42, November.
    11. Ardizzon, G. & Cavazzini, G. & Pavesi, G., 2014. "A new generation of small hydro and pumped-hydro power plants: Advances and future challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 746-761.
    12. Fichter, Tobias & Soria, Rafael & Szklo, Alexandre & Schaeffer, Roberto & Lucena, Andre F.P., 2017. "Assessing the potential role of concentrated solar power (CSP) for the northeast power system of Brazil using a detailed power system model," Energy, Elsevier, vol. 121(C), pages 695-715.
    13. Yıldıran, Uğur & Kayahan, İsmail, 2018. "Risk-averse stochastic model predictive control-based real-time operation method for a wind energy generation system supported by a pumped hydro storage unit," Applied Energy, Elsevier, vol. 226(C), pages 631-643.
    14. Mehrabankhomartash, Mahmoud & Rayati, Mohammad & Sheikhi, Aras & Ranjbar, Ali Mohammad, 2017. "Practical battery size optimization of a PV system by considering individual customer damage function," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 36-50.
    15. Vorushylo, Inna & Keatley, Patrick & Shah, Nikhilkumar & Green, Richard & Hewitt, Neil, 2018. "How heat pumps and thermal energy storage can be used to manage wind power: A study of Ireland," Energy, Elsevier, vol. 157(C), pages 539-549.
    16. Qin, Chao (Chris) & Loth, Eric, 2021. "Isothermal compressed wind energy storage using abandoned oil/gas wells or coal mines," Applied Energy, Elsevier, vol. 292(C).
    17. Karunakaran Venkatesan & Uma Govindarajan & Padmanathan Kasinathan & Sanjeevikumar Padmanaban & Jens Bo Holm-Nielsen & Zbigniew Leonowicz, 2019. "Economic Analysis of HRES Systems with Energy Storage During Grid Interruptions and Curtailment in Tamil Nadu, India: A Hybrid RBFNOEHO Technique," Energies, MDPI, vol. 12(16), pages 1-26, August.
    18. Dennis Dreier & Mark Howells, 2019. "OSeMOSYS-PuLP: A Stochastic Modeling Framework for Long-Term Energy Systems Modeling," Energies, MDPI, vol. 12(7), pages 1-26, April.
    19. Shi, Jie & Wang, Luhao & Lee, Wei-Jen & Cheng, Xingong & Zong, Xiju, 2019. "Hybrid Energy Storage System (HESS) optimization enabling very short-term wind power generation scheduling based on output feature extraction," Applied Energy, Elsevier, vol. 256(C).
    20. Segurado, R. & Madeira, J.F.A. & Costa, M. & Duić, N. & Carvalho, M.G., 2016. "Optimization of a wind powered desalination and pumped hydro storage system," Applied Energy, Elsevier, vol. 177(C), pages 487-499.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:9:y:2016:i:11:p:886-:d:81691. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.