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Stochastic Reconfiguration and Optimal Coordination of V2G Plug-in Electric Vehicles Considering Correlated Wind Power Generation

Kavousi Fard, A ; Sharif University of Technology | 2015

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  1. Type of Document: Article
  2. DOI: 10.1109/TSTE.2015.2409814
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2015
  4. Abstract:
  5. This paper investigates the optimal operation of distribution feeder reconfiguration (DFR) strategy in the smart grids with high penetration of plug-in electric vehicles (PEVs) and correlated wind power generation. The increased utilization of PEVs in the system with stochastic volatile behavior along with the high penetration of renewable power sources such as wind turbines (WTs) can create new challenges in the system that will affect the DFR strategy greatly. In order to reach the most efficiency from the PEVs, the idea of vehicle-to-grid (V2G) is employed in this paper to make a bidirectional power flow (either charging/discharging or idle mode) strategy when providing the main charging needs of PEVs. In this regard, we suggest a new stochastic framework based on unscented transformation (UT) to model the uncertainties of the PEVs behaviors when considering the correlated power generation of WTs. The feasibility and satisfying performance of the proposed framework are examined on the IEEE 69-bus test system
  6. Keywords:
  7. Unscented transformation (UT) ; Vehicle-to-grid (V2G) ; Current limiting reactors ; Electric load flow ; Electric power generation ; Electric vehicles ; Mathematical transformations ; Smart power grids ; Stochastic models ; Stochastic systems ; Vehicles ; Wind power ; Wind turbines ; Bidirectional power flow ; Distribution feeder reconfiguration (DFR) ; Optimal coordination ; Plug in Electric Vehicle (PEV) ; Plug-in Electric Vehicles ; Stochastic framework ; Unscented transformations ; Vehicle to Grid (V2G) ; Electric power transmission networks
  8. Source: IEEE Transactions on Sustainable Energy ; Volume 6, Issue 3 , 2015 , Pages 822-830 ; 19493029 (ISSN)
  9. URL: http://ieeexplore.ieee.org/document/7086343/?arnumber=7086343