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Flexible interaction of plug-in electric vehicle parking lots for efficient wind integration

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  • Heydarian-Forushani, E.
  • Golshan, M.E.H.
  • Shafie-khah, M.

Abstract

The increasing share of uncertain wind generation has changed traditional operation scheduling of power systems. The challenges of this additional variability raise the need for an operational flexibility in providing both energy and reserve. One key solution is an effective incorporation of plug-in electric vehicles (PEVs) into the power system operation process. To this end, this paper proposes a two-stage stochastic programming market-clearing model considering the network constraints to achieve the optimal scheduling of conventional units as well as PEV parking lots (PLs) in providing both energy and reserve services. Different from existing works, the paper pays more attention to the uncertain characterization of PLs takes into account the arrival/departure time of PEVs to/from the PL, the initial state of charge (SOC) of PEVs, and their battery capacity through a set of scenarios in addition to wind generation scenarios. The results reveal that although the cost saving as a consequence of incorporating PL to the grid is below 1% of total system cost, however, flexible interactions of PL in the energy and reserve markets can promote the integration of wind power more than 13.5%.

Suggested Citation

  • Heydarian-Forushani, E. & Golshan, M.E.H. & Shafie-khah, M., 2016. "Flexible interaction of plug-in electric vehicle parking lots for efficient wind integration," Applied Energy, Elsevier, vol. 179(C), pages 338-349.
    • Handle: RePEc:eee:appene:v:179:y:2016:i:c:p:338-349
    DOI: 10.1016/j.apenergy.2016.06.145
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    1. Shafie-khah, M. & Heydarian-Forushani, E. & Golshan, M.E.H. & Siano, P. & Moghaddam, M.P. & Sheikh-El-Eslami, M.K. & Catalão, J.P.S., 2016. "Optimal trading of plug-in electric vehicle aggregation agents in a market environment for sustainability," Applied Energy, Elsevier, vol. 162(C), pages 601-612.
    2. Shafie-khah, M. & Neyestani, N. & Damavandi, M.Y. & Gil, F.A.S. & Catalão, J.P.S., 2016. "Economic and technical aspects of plug-in electric vehicles in electricity markets," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1168-1177.
    3. Boynuegri, A.R. & Uzunoglu, M. & Erdinc, O. & Gokalp, E., 2014. "A new perspective in grid connection of electric vehicles: Different operating modes for elimination of energy quality problems," Applied Energy, Elsevier, vol. 132(C), pages 435-451.
    4. Jian, Linni & Zheng, Yanchong & Xiao, Xinping & Chan, C.C., 2015. "Optimal scheduling for vehicle-to-grid operation with stochastic connection of plug-in electric vehicles to smart grid," Applied Energy, Elsevier, vol. 146(C), pages 150-161.
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    Cited by:

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    10. Heydarian-Forushani, E. & Golshan, M.E.H. & Siano, Pierluigi, 2017. "Evaluating the benefits of coordinated emerging flexible resources in electricity markets," Applied Energy, Elsevier, vol. 199(C), pages 142-154.
    11. Haque, A.N.M.M. & Ibn Saif, A.U.N. & Nguyen, P.H. & Torbaghan, S.S., 2016. "Exploration of dispatch model integrating wind generators and electric vehicles," Applied Energy, Elsevier, vol. 183(C), pages 1441-1451.
    12. Tohid Harighi & Sanjeevikumar Padmanaban & Ramazan Bayindir & Eklas Hossain & Jens Bo Holm-Nielsen, 2019. "Electric Vehicle Charge Stations Location Analysis and Determination—Ankara (Turkey) Case Study," Energies, MDPI, vol. 12(18), pages 1-22, September.
    13. Wu, Chuanshen & Gao, Shan & Liu, Yu & Song, Tiancheng E. & Han, Haiteng, 2021. "A model predictive control approach in microgrid considering multi-uncertainty of electric vehicles," Renewable Energy, Elsevier, vol. 163(C), pages 1385-1396.
    14. Lin, Boqiang & Li, Zheng, 2020. "Is more use of electricity leading to less carbon emission growth? An analysis with a panel threshold model," Energy Policy, Elsevier, vol. 137(C).
    15. Seyfettin Vadi & Ramazan Bayindir & Alperen Mustafa Colak & Eklas Hossain, 2019. "A Review on Communication Standards and Charging Topologies of V2G and V2H Operation Strategies," Energies, MDPI, vol. 12(19), pages 1-27, September.
    16. Zeng, Bo & Sun, Bo & Wei, Xuan & Gong, Dunwei & Zhao, Dongbo & Singh, Chanan, 2020. "Capacity value estimation of plug-in electric vehicle parking-lots in urban power systems: A physical-social coupling perspective," Applied Energy, Elsevier, vol. 265(C).
    17. Wu, Chuanshen & Jiang, Sufan & Gao, Shan & Liu, Yu & Han, Haiteng, 2022. "Charging demand forecasting of electric vehicles considering uncertainties in a microgrid," Energy, Elsevier, vol. 247(C).
    18. Mansourshoar, Paria & Yazdankhah, Ahmad Sadeghi & Vatanpour, Mohsen & Mohammadi-Ivatloo, Behnam, 2022. "Impact of implementing a price-based demand response program on the system reliability in security-constrained unit commitment problem coupled with wind farms in the presence of contingencies," Energy, Elsevier, vol. 255(C).

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