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Power drain and the electric vehicle

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(L-R) Jongchan Choi and Danielle Meyer, students working with Prof. JK Wang.
If the demand for electric vehicles (EVs) continues to rise as expected, power grid losses and brown outs could become the new norm as drivers increasingly charge up for travel.

New research from electrical engineers at The Ohio State University calls for an “urgent need” to expand public EV charging stations nationwide, while simultaneously prepping power grid infrastructure to handle the added stress.

Ohio State Electrical and Computer Engineering Assistant Professor Jiankang Wang, along with her students Danielle Meyer and JongChan Choi, published their research in the latest IEEE Xplore.

To help offset the power drain caused by the new wave of EV charging stations, the team calls for a dual integration of both increased renewable energy and micro grids. Through this, they see less of an impact on the national electrical infrastructure, while increasing opportunities for green energy use.

Car manufacturers may like a bonus aspect of their plans – the potential for selling even more electric cars. To give an idea of where the EV market demand is headed, a report by IHS estimates the swing to EVs and plug-in hybrid vehicles will lead to the installation of 10.7 million new charging stations globally by 2020, up from 135,000 in 2011.

Meanwhile, according to Nissan, sales for its Leaf EVs were racking up more each month of 2014 than all of 2013 combined. The creation of the West Coast Electric Highway Network, which installed new charging stations across California, Washington and Oregon, further shows some communities are already looking to meet the new demand.

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Prof. JK Wang
Ohio State engineers explain how EV charging stations must become a normal part of large city electrical grid infrastructure. Utility agencies should prepare early by “seriously considering” rate structures, availability of power, total load management and methods for charging.

Previous scholarly research into EVs and the impact on the electrical grid typically invokes stochastic modeling as a way to determine EV charging use. Stochastic modeling is a mathematical tool for estimating probability distributions of potential outcomes by allowing for random variation over time.

A technique called distributed generation (DG) is also previously recommended by engineers as a general solution to the growing electrical grid stress. This involves generating power on-site, rather than centrally, which helps eliminate the cost and redundancies associated with transmission and distribution.

Wang and her team, however, propose adapting both concepts toward EV charging stations. This involves creating or storing a variety of smaller energy resource systems, which then connect to the larger grid. These sources can work separately, or in conjunction.

“This use of DG to offset grid stress can enable the construction of more EV charging stations, which can increase sales in an area, and also furthers the ‘green’ goal of EVs,” Ohio State research explains. “On-site stationary electric storage, installed at the household level, can provide a way to further power EVs in the absence of high DG output, without adding more stress to the grid. Continuing to make generation more renewable through combinations of energy storage and generation technologies will further facilitate EV adoption and sales.”

Wang’s Electric Power Grid Research Group at Ohio State investigates physical and economic engineering issues relevant to the grid-level integration of energy and power delivery. In particular, its research works on distribution power networks, which deliver electricity to citizens and connect distributed generators to power systems, making electricity supplies more reliable, energy efficient and environment friendly. 

For more information about the Ohio State Department of Electrical and Computer Engineering: https://ece.osu.edu

For more information about the Electrical Power Grid Research Group at Ohio Statehttps://sites.google.com/site/jkwangdgrid/home