Optimal Placement of PV-DSTATCOM Based EV Charging Stations With Dynamic Pricing

This article proposes incorporation of a method for optimal sizing and placement of a PV-DSTATCOM (a D-STATCOM feeding active power through the dc link from a PV array) combined with an electric vehicle charging station (EVCS) in a radial distribution system. This device can inject active power from DC side and also compensate var on AC side to maintain voltage stability of the network. The DC side of the PV-DSTATCOM is also utilized for an EVCS, drawing power from PV source or the distribution grid based on PV availability and demand at a given time. The point of common coupling (PCC) of the PV-DSTATCOM is determined by the proposed method for IEEE-38 bus system. It uses modal analysis with sensitivity indices to assess the voltage stability and find critical buses where PV-DSTATCOMs can be placed to improve the overall network stability. The method also combines particle swarm optimization (PSO) for optimal sizing of the PV-DSTATCOM with EVCS provision. In addition, this work proposes a dynamic pricing scheme using an adaptively trained artificial neural network (ANN) for EVCSs to effectively manage demands during peak hours (thereby improving network stability) and benefit EV users in terms of charge per kWh of electricity depending upon the present state of charge (SOC) of their vehicle battery and the level of congestion at EVCS. The results obtained show that the proposed scheme improves voltage stability and reduce line loss of the radial distribution system.