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Comparison of intelligent charging algorithms for electric vehicles to reduce peak load and demand variability in a distribution grid

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3 Author(s)
Mets, Kevin ; Department of Information Technology, IBCN at Ghent University, iMinds, G. Crommenlaan 8 Block C0 Bus 201, 9050 Ghent, Belgium ; D'hulst, Reinhilde ; Develder, Chris

A potential breakthrough of the electrification of the vehicle fleet will incur a steep rise in the load on the electrical power grid. To avoid huge grid investments, coordinated charging of those vehicles is a must. In this paper, we assess algorithms to schedule charging of plug-in (hybrid) electric vehicles as to minimize the additional peak load they might cause. We first introduce two approaches, one based on a classical optimization approach using quadratic programming, and a second one, market based coordination, which is a multi-agent system that uses bidding on a virtual market to reach an equilibrium price that matches demand and supply. We benchmark these two methods against each other, as well as to a baseline scenario of uncontrolled charging. Our simulation results covering a residential area with 63 households show that controlled charging reduces peak load, load variability, and deviations from the nominal grid voltage.

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Communications and Networks, Journal of  (Volume:14 ,  Issue: 6 )