Abstract:
Due to surge in the use of electric vehicles (EVs), electric vehicle charging station (EVCS) load modelling emerges to be vital in designing or revamping existing EV charging facilities. The present work addresses this optimal scheduling problem by proposing a novel probabilistic model to predict the optimum demand at a charging station for fixed time duration and thereafter for variable durations using dynamic fault tree analysis considering the charging urgency of the vehicles. To cater to the increased load demand due to enhanced EV penetration and to mitigate its associated technical challenges, incorporation of Mobile Charging Station (MCS) is proposed in the present work. However, placement of MCS poses a significant challenge of their optimal siting and scheduling. This has been achieved by identifying priority areas for their positioning from a proposed Mobile Charging Station Allocation Indicator (MCSAI) computed based on escalated requirement at an EVCS. Extensive investigations have been conducted for placement of combination of fixed and mobile EVCS in a standard IEEE 33 and 85 bus distribution system and it is shown that the resultant hybrid system leads to a significant improvement of more than 5 %, 15 % and 25, 30 % respectively as measured by indices such as line loss reduction index voltage profile improvement index.