Charge management of plug-in electric vehicles for distribution transformer life enhancement

J. R. da Silva *, P. M. de Oliveira, T. R. Lima, A. Z. de Freitas

Electronic and Electrical Engineering Department, Nove de Julho University, São Paulo, SP, Brazil


A large population of power transformers along with other power system grid infrastructures have been in service for decades and considered to be in their final ageing stage. On the other hand, due to economy and business growth in our era, the electricity demand is growing rapidly. Therefore, transformers became the most critical devices in power system due to their long repair or replacement time. Plug-in electric vehicles (PEV) have been identified as an option that can reduce criteria pollutant and greenhouse gas emissions associated with the transportation sector. The electricity demand of one of these vehicles is comparable to that of a typical U.S. household and thus clustering of PEVs in a neighborhood might have adverse effects on the transformer and disruption of service. In this paper, the electricity demand of a neighborhood is modeled based on measured vehicle and household data. Then the threshold temperature is determined to program the charging process. In the proposed method, we used adaptive neuro-fuzzy inference system (ANFIS). Simulation results show that the ANFIS can accurately predict the spot hot of transformer.


Transformer, Hot spot, ANFIS, PEV

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Article history

Received 25 December 2018, Received in revised form 18 March 2019, Accepted 22 March 2019

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da Silva JR, de Oliveira PM, and Lima TR et al. (2019). Charge management of plug-in electric vehicles for distribution transformer life enhancement. Annals of Electrical and Electronic Engineering, 2(5): 14-18

References (17)

  1. Brooker RP and Qin N (2015). Identification of potential locations of electric vehicle supply equipment. Journal of Power Sources, 299: 76-84.   [Google Scholar]
  2. Buragohain M and Mahanta C (2008). A novel approach for ANFIS modelling based on full factorial design. Applied Soft Computing, 8(1): 609-625.   [Google Scholar]
  3. Chiu SL (1994). Fuzzy model identification based on cluster estimation. Journal of Intelligent and Fuzzy Systems, 2(3): 267-278.   [Google Scholar]
  4. Clement-Nyns K, Haesen E, and Driesen J (2009). The impact of charging plug-in hybrid electric vehicles on a residential distribution grid. IEEE Transactions on Power Systems, 25(1): 371-380.   [Google Scholar]
  5. Dimitrova Z and Maréchal F (2015). Techno-economic design of hybrid electric vehicles using multi objective optimization techniques. Energy, 91: 630-644.   [Google Scholar]
  6. Esmaili M and Goldoust A (2015). Multi-objective optimal charging of plug-in electric vehicles in unbalanced distribution networks. International Journal of Electrical Power and Energy Systems, 73: 644-652.   [Google Scholar]
  7. Geiles TJ and Islam S (2013). Impact of PEV charging and rooftop PV penetration on distribution transformer life. In the IEEE Power and Energy Society General Meeting, IEEE, Vancouver, Canada: 1-5.   [Google Scholar]
  8. Gong Q, Midlam-Mohler S, Marano V, and Rizzoni G (2012). Study of PEV charging on residential distribution transformer life. IEEE Transactions on Smart Grid, 3(1): 404-412.   [Google Scholar]
  9. Hajforoosh S, Masoum MA, and Islam SM (2015). Real-time charging coordination of plug-in electric vehicles based on hybrid fuzzy discrete particle swarm optimization. Electric Power Systems Research, 128: 19-29.   [Google Scholar]
  10. Lausenhammer W, Engel D, and Green R (2016). Utilizing capabilities of plug in electric vehicles with a new demand response optimization software framework: Okeanos. International Journal of Electrical Power and Energy Systems, 75: 1-7.   [Google Scholar]
  11. Qian K, Zhou C, and Yuan Y (2015). Impacts of high penetration level of fully electric vehicles charging loads on the thermal ageing of power transformers. International Journal of Electrical Power and Energy Systems, 65: 102-112.   [Google Scholar]
  12. Rathore C and Roy R (2016). Impact of wind uncertainty, plug-in-electric vehicles and demand response program on transmission network expansion planning. International Journal of Electrical Power and Energy Systems, 75: 59-73.   [Google Scholar]
  13. Razeghi G, Zhang L, Brown T, and Samuelsen S (2014). Impacts of plug-in hybrid electric vehicles on a residential transformer using stochastic and empirical analysis. Journal of Power Sources, 252: 277-285.   [Google Scholar]
  14. Rutherford MJ and Yousefzadeh V (2011). The impact of electric vehicle battery charging on distribution transformers. In the Twenty-Sixth Annual IEEE Applied Power Electronics Conference and Exposition, IEEE, Fort Worth, USA: 396-400.   [Google Scholar]
  15. Takagi T and Sugeno M (1983). Derivation of fuzzy control rule from human operator's control actions. IFAC Proceedings Volumes, 16(13): 55-60.   [Google Scholar]
  16. Yilmaz M (2015). Limitations/capabilities of electric machine technologies and modeling approaches for electric motor design and analysis in plug-in electric vehicle applications. Renewable and Sustainable Energy Reviews, 52: 80-99.   [Google Scholar]
  17. Zhang S and Xiong R (2015). Adaptive energy management of a plug-in hybrid electric vehicle based on driving pattern recognition and dynamic programming. Applied Energy, 155: 68-78.   [Google Scholar]