Simulator to Quantify and Manage Electric Vehicle Load Impacts on Low-voltage Distribution Grids

This technical notes details the methodology behind the Electric Vehicles on the Grid Simulator. This tool is intended to help individual building energy managers, facility owners, distribution service operators, charging point operators, and fleet operators. This model-based simulator enables users to evaluate the potential electric vehicle (EV) load impacts on the low-voltage distribution grid at specific sites and plan for future capacity upgrades. Additionally, the tool can be used to quantify the effects of different vehicle-grid integration technologies to alleviate the peak capacity stress. The Monte Carlo simulation and linear programming are deployed to predict the EV charging and discharging load profiles and load impacts at specific sites, with the consideration of future EV penetration, EV charging and traveling behaviors, availability of charging facility availability, and site loads. Based on existing studies conducted in the United States, Europe, and China, the default EV charging and traveling behaviors are predefined for a quick assessment. Users are encouraged to modify the model inputs for the specific site to derive more accurate and contextualized results. This tool can also help manage an EV fleet’s smart charging and discharging at the low-voltage distribution grid.

[1]  Zhenhong Lin,et al.  Intensity and daily pattern of passenger vehicle use by region and class in China: estimation and implications for energy use and electrification , 2019, Mitigation and Adaptation Strategies for Global Change.

[2]  Simone Maase,et al.  E-mobility : getting smart with data , 2019 .

[3]  Igna Vermeulen,et al.  Simulation of Future Electric Vehicle Charging Behavior—Effects of Transition from PHEV to FEV , 2019, World Electric Vehicle Journal.

[4]  中国汽车技术研究中心,et al.  Annual Report on New Energy Vehicle Industry in China (2018) , 2018 .

[5]  Till Gnann,et al.  The load shift potential of plug-in electric vehicles with different amounts of charging infrastructure , 2018, Journal of Power Sources.

[6]  Eric Wood,et al.  California Plug-In Electric Vehicle Infrastructure Projections: 2017-2025 - Future Infrastructure Needs for Reaching the State's Zero Emission-Vehicle Deployment Goals , 2018 .

[7]  John W. Polak,et al.  Modeling Electric Vehicle Charging Behaviour: What Is the Relationship Between Charging Location, Driving Distance and Range Anxiety? , 2017 .

[8]  Margaret O'Mahony,et al.  Future standard and fast charging infrastructure planning: an analysis of electric vehicle charging behaviour , 2016 .

[9]  Thomas Bräunl,et al.  Driving and charging patterns of electric vehicles for energy usage , 2014 .

[10]  Gregory A. Keoleian,et al.  A microsimulation of energy demand and greenhouse gas emissions from plug-in hybrid electric vehicle use , 2012 .

[11]  L. Norford,et al.  Energy Management Principles , 1985 .

[12]  Sten Karlsson,et al.  On the distribution of individual daily vehicle driving distances , 2014 .

[13]  Colin Bayliss,et al.  Transmission and distribution electrical engineering , 1996 .

[14]  P. Lewington Energy Analysis and Policy , 1990 .