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Impact Evaluation of Plug-in Electric Vehicles on Power System

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Plug In Electric Vehicles in Smart Grids

Part of the book series: Power Systems ((POWSYS))

Abstract

The aim of this chapter is to expose the probabilistic Plug-in electric vehicle (PEV) charging model and to apply it in a distribution grid to evaluate the PEV impact. The model is based on agent-based techniques, has probabilistic variables, includes queuing theory, and applies Monte Carlo methodology. The PEV user’s charging needs can be divided in two categories: private charging points and public charging points. Regarding private charging points, the PEV charging demand depends strongly on private user’s mobility needs and it includes variables as number of trips per day, driving distance, and arrival time. Also, the user’s profile can be modelled with probabilistic variables as the PEV model and the charging connection point. All these variables can be modelled with probabilistic distributions functions to obtain a probabilistic model with data from different sources. Additionally, public charging points are made available for PEV users that need to plug-in the vehicle between trips to extend the vehicle autonomy. After that, the model is applied to a case study to analyze the impact to the power network. Probabilistic grid impact includes the probability to exceed a maximum voltage drop, and transformer and current saturations. The main impact is on saturations of lines and they can be reduced controlling private points but it does not make sense to control the public points. Fast chargers present some challenges for grid integration because they are public charging points of 50 kW power rate per charger. In this chapter, a stochastic arrivals model is applied to analyze the public fast charging stations, the PEV user’s charging needs and their grid impact. Fast charging stations are designed to extend the autonomy of PEV and their arrivals to the station have a certain stochastic behavior. The probabilistic arrivals for the fast charger impact evaluation are based on queuing theory and the corresponding electricity demand is evaluated. The case study analyzed includes three fast chargers installed in the same grid and they provoke saturations in lines.

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Author information

Authors and Affiliations

  1. Centre d’Innovació Tecnològica en Convertidors Estàtics i Accionaments (CITCEA-UPC), Departament d’Enginyeria Elèctrica, EU d’Enginyeria Tècnica Industrial de Barcelona, Universitat Politècnica de Catalunya-BarcelonaTech, Barcelona, Spain

    Pol Olivella-Rosell, Roberto Villafafila-Robles & Andreas Sumper

Authors
  1. Pol Olivella-Rosell
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  2. Roberto Villafafila-Robles
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  3. Andreas Sumper
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Corresponding author

Correspondence to Pol Olivella-Rosell .

Editor information

Editors and Affiliations

  1. Electrical & Computer Engineering B, Curtin University, Perth, West Australia, Australia

    Sumedha Rajakaruna

  2. Electrical & Computer Engineering, Curtin University, Perth, West Australia, Australia

    Farhad Shahnia

  3. Electrical & Computer Engineering, Curtin University, Perth, West Australia, Australia

    Arindam Ghosh

Appendices

Appendix 6.A—PEV Models

See Table 6.5.

Table 6.5 List of PEV models considered in the case study
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Appendix 6.B—Grid Data

See Tables 6.6, 6.7 and 6.8.

Table 6.6 Cable data considered in the case study
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Table 6.7 MV/LV transformers characteristics
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Table 6.8 System data without fast chargers
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Active power assumed is 80 % of rated capacity for each MV/LV transformer as a conservative assumption.

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© 2015 Springer Science+Business Media Singapore

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Olivella-Rosell, P., Villafafila-Robles, R., Sumper, A. (2015). Impact Evaluation of Plug-in Electric Vehicles on Power System. In: Rajakaruna, S., Shahnia, F., Ghosh, A. (eds) Plug In Electric Vehicles in Smart Grids. Power Systems. Springer, Singapore. https://doi.org/10.1007/978-981-287-299-9_6

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  • DOI: https://doi.org/10.1007/978-981-287-299-9_6

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-287-298-2

  • Online ISBN: 978-981-287-299-9

  • eBook Packages: EnergyEnergy (R0)

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