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Joint expansion planning studies of EV parking lots placement and distribution network

Mozaffari, M ; Sharif University of Technology | 2020

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  1. Type of Document: Article
  2. DOI: 10.1109/TII.2020.2964049
  3. Publisher: IEEE Computer Society , 2020
  4. Abstract:
  5. Long-term distribution network planning (DNP) is considered as one of the most challenging issues for distribution network operators (DNOs). By increasing EVs in cities, it has appeared some new players such as electric vehicles (EVs) owners and electric vehicle parking lots (EVPLs) owners for DNP. In this article, we conduct a new study on the coupled dynamic expansion problem of EVPLs placement and distribution networks. To reach this goal, at first, EVs driving and charging/discharging behavior as some influential factors is modeled using an efficient probabilistic algorithm. An analytical model is then introduced to estimate the number of EVs in EVPLs at different times. To find out the optimal place for installing EVPLs and distribution network reinforcement, a multiobjective (MO) optimization model is formulated considering the main requirements of EVs users, EVPLs owners, and distribution system operators. A linear model is also derived for the energy monument of EVs in EVPLs and residential charging stations to be used in the mentioned MO expansion problem. Numerical results with various cases are presented to illustrate the main advantages of the proposed coupled expansion model. The simulation results indicate increasing the percentage of participated EVs in V2G leads to reduce the DNO costs and increase the EVPLs' revenues. Moreover, the year of feeder reinforcement will be deferred by considering V2G mode and battery degradation. © 2005-2012 IEEE
  6. Keywords:
  7. Battery degradation ; Distribution network (DN) ; Dynamic expansion planning ; Electric vehicle parking lots (EVPLs) ; V2G ; Charging (batteries) ; Reinforcement ; Vehicle-to-grid ; Charging/discharging ; Distribution network operators ; Distribution systems ; Electric Vehicles (EVs) ; Long-term distribution ; Network reinforcements ; Optimization modeling ; Probabilistic algorithm ; Expansion
  8. Source: IEEE Transactions on Industrial Informatics ; Volume 16, Issue 10 , October , 2020 , Pages 6455-6465
  9. URL: https://ieeexplore.ieee.org/document/8950236