Investigation of AIMD Based Charging Strategies for EVs Connected to a Low-Voltage Distribution Network - Intelligent Computing for Sustainable Energy and Environment - page 433

Investigation of AIMD Based Charging
Strategies for EVs Connected to a Low-Voltage
Distribution Network
Mingming Liu and Sean McLoone
Department of Electronic Engineering, National University of Ireland, Maynooth
Maynooth, Ireland
[e-mail], [e-mail]
Abstract. In this paper we consider charging strategies that mitigate
the impact of domestic charging of EVs on low-voltage distribution net-
works and which seek to reduce peak power by responding to time-of-
day pricing. The strategies are based on the distributed Additive Increase
and Multiplicative Decrease (AIMD) charging algorithms proposed in [5].
The strategies are evaluated using simulations conducted on a custom
OpenDSS-Matlab platform for a typical low voltage residential feeder
network. Results show that by using AIMD based smart charging 50%
EV penetration can be accommodated on our test network, compared
to only 10% with uncontrolled charging, without needing to reinforce
existing network infrastructure.
Keywords: EV charging, AIMD, Smart Grid, Distributed algorithm.
1 Introduction
In the near future, with an increasing number of EVs plugging into the grid in
residential areas it is likely that coincident uncontrolled charging will overload
local distribution networks and substantially increase peak power requirements.
It is therefore essential that careful consideration is given to developing smart
grid infrastructure and charging strategies to mitigate the impact of the roll out
of EVs on the grid. EV charging strategies have been the focus of considerable
research effort in recent years [1]-[4]. Clement-Nyns et al. [4], propose a coordi-
nated charging method is to minimize power losses and maximize the main grid
load factor. In Richardson et al. [2] a technique based on linear programming
to determine the optimal charging rate is developed in order to maximize the
total power that can be delivered to EVs while meeting distribution network
constraints. In [1] a coordinated charging algorithm using both quadratic and
dynamic programming is employed to shift the EV loads to off-peak times while
minimizing the power losses for both deterministic and stochastic data. In [6] a
transportation micro-simulation is employed to secure power system operation
using a multi-agent system (MAS) to coordinate EV charging behavior. In [8],
to maximize a customer's own utility, a simple adaption strategy based on price