An approach for minimizing annual energy loss by electric vehicle scheduling to optimal fast charging stations

Abstract

ABSTRACT In order to mitigate the effects of electric vehicles’ (EVs) high power consumption on the distribution network, this study proposes a two-stage approach. First, it uses node traffic density forecasts to select optimal locations for fast charging stations (FCS). Then, to reduce the power demand on the distribution system, it schedules EVs to these FCSs. Considering constraints such as charger number, station capacity, and desired battery state of charge (SOC), the objectives are to minimize annual energy loss, optimize load demand, decrease station development costs (SDC), and maximize charging station (CS) income. The best locations for FCS are determined using a gray wolf optimization (GWO) technique. Rather than allocating EVs to uniform base loads, the study recommends analyzing different initial SOC cases to minimize power demand at FCSs. For EV scheduling, a queuing-averse charging station algorithm is suggested to reduce annual energy loss and increase station income. For various SOC cases relative to the base load scenario, this suggested method achieves an average daily FCS charging time of 648 min, a power loss of 16.8 kW, and an annual energy loss of 9.38 kW.