Multivariate Stochastic Modeling of Plugin Electric Vehicles Charging Profile and Grid Impact Analysis /

Abstract

The ability of the power system distribution network to facilitate the additional charging demand of plugin electric vehicles (PEVs) is critical to the successful electrification of the transportation sector. Thus, the significance of a reliable electricity system, particularly at the distribution network level, cannot be overstated. An accurate assessment of the PEV charging load profile is critical for distribution system reinforcement planning. In this paper a unique approach is adopted is introduced to estimate aggregated PEV charging load based on the real-world dynamics. The proposed methodology is based on the multivariate stochastic modelling of PEV driving behavior, which is combined with powertrain simulations performed on various models of PEVs operating under two different driving cycles to determine main parameters i.e., energy consumption of PEV during the trip, initial SOC at charging, and grid charging energy requirement factors to estimate daily PEV charging demand based on real-world dynamics. Gird impact analysis is performed on the local distribution network of National University of Sciences and Technology (NUST), Islamabad, Pakistan, where the aggregated PEV charging load of levels 1,2 and 3 is integrated at respective nodes. The load flow analysis is performed to determine the weakest nodes in the grid and the percentage of transformer loading due additional PEV charging load. Furthermore, a technoeconomic grid reinforcement solution is offered to reduce the detrimental influence of PEV charging load on grid voltage stability.