Abstract
Due to their high energy capacity and potential mass deployment, battery electric vehicles (BEVs) will have a significant impact on power distribution networks. There are issues for the distribution network operator if BEV charging is allowed to take place without any control on the time of day, duration or charging rate. Specifically, the network voltage may fall below prescribed limits at times of peak demand and power flows may cause a thermal overload of assets. The existing literature on scheduling charging/discharging of BEVs makes use of decentralised/centralised control architectures to study the effect of charging/discharging of BEVs on distribution network. This paper presents a teaching-learning algorithm method to optimally charge and discharge the BEVs and hence mitigate the adverse impacts on the distribution network by considering the driving behaviour of car owners. This approach has resulted in reduced transformer loading even when using V2G and G2V modes of operation of the BEVs and hence has prevented transformer aging in low voltage distribution networks.
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