Investigation of AIMD Based Charging Strategies for EVs Connected to a Low-Voltage Distribution Network - Intelligent Computing for Sustainable Energy and Environment - page 440

with and without the inclusion of the price modulated available power term
( Δ ( k )) in (3)).
Table 1 provides a summary comparison of the different charging strategies
considered in the paper. From the table it can be seen that SmartP (i.e. price-
adjusted AIMD) EV charging provides the best overall performance with the
lowest line voltage drops, smallest maximum power requirements and the cheap-
est charging rate (40-50% less than with uncontrolled charging).

Fig. 3. (a) Plot of the TOU electricity price signal E ( k ); (b) Comparison of EV loads
obtained using AIMD smart charging with and without price adjusted available power.

Tabl e 1. Comparison of all charging scenarios considered

Scenarios Mini Voltage(p.u) Max Load 1 (%) Energy 2 (kWh) Cost(cents/kWh)
NoEV

0.92 108 - -

Unctrl 0.87 165 10.15 19.70

Smart 3

0.91 118 10.15 12.69

SmartP 4 0.92 109 10.15 10.90

5 Conclusions

In this paper, a novel distributed smart EV algorithm has been proposed for
managing EV charging on a low-voltage residential distribution network. The
algorithm, which is based on the AIMD EV charging algorithms proposed in [5],
is designed to take account of capacity, infrastructure and voltage constraints
on the network and encourage EV charging at off-peak times. Using a simula-
tion of a representative low-voltage residential distribution network with 50%
EV penetration the proposed algorithm has been benchmarked against uncon-
trolled charging and shown to effectively mitigate the impact of EV charg-
ing on the grid. Our results show that for the scenarios considered the pro-
posed AIMD charging strategy is able to comfortably support up to 50% EV
1 Maximum loading as a percentage of the sub-station rating (400kVA).
2 Average energy per EV per day.
3 AIMD smart charging without price adjustment.
4 AIMD smart charging with price adjustment.