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A significant reduction in the costs of battery energy storage systems by use of smart parking lots in the power fluctuation smoothing process of the wind farms

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  • Jannati, M.
  • Hosseinian, S.H.
  • Vahidi, B.
  • Li, Guo-jie.

Abstract

Wind farm power fluctuations resulted from the wind random nature bring a significant challenge to the wind turbine generators operating in the maximum power point tracking. Furthermore, the smoothing process of a large wind farm in which the Battery Energy Storage System (BESS) is used, needs a considerable initial investment cost. Utilizing Smart Parking Lots (SPLs) can be considered as an applicable solution. In this paper, assuming conventional parking lots in Tehran converted to an SPL set in a near future and thus establishing an enormous charging/discharging capacity, the smoothing process is performed on a 51 MW wind farm. For this purpose, a coordinated control system based on two control algorithms is proposed. The first proposed algorithm chooses eligible SPLs for charging/discharging activity before receiving a new sample of the wind farm output power. Afterwards, the second proposed algorithm determines qualified vehicles in selected SPLs. The main aim is to minimize the number of SPLs (vehicles) taking part in the process. According to the simulation results, the required BESS capacity in the power smoothing process of a typical wind farm decreases considerably when the proposed approach is applied. Thus, the investment cost of BESS is reduced significantly.

Suggested Citation

  • Jannati, M. & Hosseinian, S.H. & Vahidi, B. & Li, Guo-jie., 2016. "A significant reduction in the costs of battery energy storage systems by use of smart parking lots in the power fluctuation smoothing process of the wind farms," Renewable Energy, Elsevier, vol. 87(P1), pages 1-14.
    • Handle: RePEc:eee:renene:v:87:y:2016:i:p1:p:1-14
    DOI: 10.1016/j.renene.2015.09.054
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    References listed on IDEAS

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    1. Jannati, M. & Hosseinian, S.H. & Vahidi, B. & Li, Guo-Jie, 2014. "A survey on energy storage resources configurations in order to propose an optimum configuration for smoothing fluctuations of future large wind power plants," Renewable and Sustainable Energy Reviews, Elsevier, vol. 29(C), pages 158-172.
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    Cited by:

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    3. Yang, Shaohui & He, Hongzhou & Chen, Hu & Wang, Yongqing & Li, Hui & Zheng, Songgen, 2019. "Experimental study on the performance of a floating array-point-raft wave energy converter under random wave conditions," Renewable Energy, Elsevier, vol. 139(C), pages 538-550.
    4. Han, Shuo & He, Mengjiao & Zhao, Ziwen & Chen, Diyi & Xu, Beibei & Jurasz, Jakub & Liu, Fusheng & Zheng, Hongxi, 2023. "Overcoming the uncertainty and volatility of wind power: Day-ahead scheduling of hydro-wind hybrid power generation system by coordinating power regulation and frequency response flexibility," Applied Energy, Elsevier, vol. 333(C).
    5. Jannati, Jamil & Nazarpour, Daryoush, 2018. "Multi-objective scheduling of electric vehicles intelligent parking lot in the presence of hydrogen storage system under peak load management," Energy, Elsevier, vol. 163(C), pages 338-350.
    6. Van-Hai Bui & Akhtar Hussain & Woon-Gyu Lee & Hak-Man Kim, 2019. "Multi-Objective Optimization for Determining Trade-Off between Output Power and Power Fluctuations in Wind Farm System," Energies, MDPI, vol. 12(22), pages 1-18, November.
    7. Guo, Jianwei & Lv, Yongbo & Zhang, Han & Nojavan, Sayyad & Jermsittiparsert, Kittisak, 2020. "Robust optimization strategy for intelligent parking lot of electric vehicles," Energy, Elsevier, vol. 200(C).
    8. Yanjuan Yu & Hongkun Chen & Lei Chen, 2018. "Comparative Study of Electric Energy Storages and Thermal Energy Auxiliaries for Improving Wind Power Integration in the Cogeneration System," Energies, MDPI, vol. 11(2), pages 1-16, January.

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