Mitigation of the impact of high plug-in electric vehicle penetration on residential distribution grid using smart charging strategies

Vehicle electrification presents a great opportunity to reduce transportation greenhouse gas emissions. The greater use of plug-in electric vehicles (PEVs), however, puts stress on local distribution networks. This paper presents an optimal PEV charging control method integrated with utility demand response (DR) signals to mitigate the impact of PEV charging to several aspects of a grid, including load surge, distribution accumulative voltage deviation, and transformer aging. To build a realistic PEV charging load model, the results of National Household Travel Survey (NHTS) have been analyzed and a stochastic PEV charging model has been defined based on survey results. The residential distribution grid contains 120 houses and is modeled in GridLAB-D. Co-simulation is performed using Matlab and GridLAB-D to enable the optimal control algorithm in Matlab to control PEV charging loads in the residential grid modeled in GridLAB-D. Simulation results demonstrate the effectiveness of the proposed optimal charging control method in mitigating the negative impacts of PEV charging on the residential grid.

[1]  Adnan Bosovic,et al.  Analysis of the impacts of plug-in electric vehicle charging on the part of a real medium voltage distribution network , 2014, IEEE PES Innovative Smart Grid Technologies, Europe.

[2]  Antonio Piccolo,et al.  Plug-in EV charging impact on grid based on vehicles usage data , 2014, 2014 IEEE International Electric Vehicle Conference (IEVC).

[3]  Sami Repo,et al.  Statistical Charging Load Modeling of PHEVs in Electricity Distribution Networks Using National Travel Survey Data , 2012, IEEE Transactions on Smart Grid.

[4]  Ryan Liu,et al.  A survey of PEV impacts on electric utilities , 2011, ISGT 2011.

[5]  William Kersting,et al.  Distribution System Modeling and Analysis , 2001, Electric Power Generation, Transmission, and Distribution: The Electric Power Engineering Handbook.

[6]  Maarouf Saad,et al.  A detailed review on the parameters to be considered for an accurate estimation on the Plug-in Electric Vehicle's final State of Charge , 2016, 2016 3rd International Conference on Renewable Energies for Developing Countries (REDEC).

[7]  F. Cheung National Highway Traffic Safety Administration (NHTSA) notes. An analysis of alcohol-related motor vehicle fatalities by ethnicity. , 1999, Annals of emergency medicine.

[8]  Susan Redline,et al.  IEEE Guide for Loading Mineral- Oil-Immersed Transformers and Step-Voltage Regulators , 2012 .

[9]  L. Hockstad,et al.  Inventory of U.S. Greenhouse Gas Emissions and Sinks , 2018 .

[10]  W. H. Kersting,et al.  Radial distribution test feeders , 1991, 2001 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.01CH37194).

[11]  Probabilistic modeling of EV charging and its impact on distribution transformer loss of life , 2012, 2012 IEEE International Electric Vehicle Conference.

[12]  J. Frolik,et al.  Electric vehicle charging: Transformer impacts and smart, decentralized solutions , 2012, 2012 IEEE Power and Energy Society General Meeting.

[13]  Turan Gonen,et al.  Electric power distribution system engineering , 1985 .

[14]  Yue Yuan,et al.  Modeling of Load Demand Due to EV Battery Charging in Distribution Systems , 2011, IEEE Transactions on Power Systems.

[15]  Jiankang Wang,et al.  Increasing EV public charging with distributed generation in the electric grid , 2015, 2015 IEEE Transportation Electrification Conference and Expo (ITEC).

[16]  Richard Scholer,et al.  Smart Charging Standards for Plug-In Electric Vehicles , 2014 .