Multivariate stochastic modeling of plugin electric vehicles charging profile and grid impact analysis

Abstract

The electrification of the transportation sector presents a significant challenge to the power grid. The accurate modeling of the plug-in electric vehicle (PEV) charging load is essential since it can guide policymakers and grid operators to make informed decisions regarding grid reinforcement to facilitate the additional charging demand of PEVs. This research paper introduces a hybrid technique for estimating the aggregated PEV charging load based on a stochastic model and real-world dynamics. The proposed methodology includes multivariate stochastic modeling of PEV driving behaviors which is combined with the powertrain simulations performed on multiple models of PEVs operating under two different driving cycles to determine critical parameters such as energy consumption, the initial state of charge (SOC), and grid charging energy requirements. These parameters are used to estimate daily PEV charging demand based on real-world dynamics. The grid impact analysis is performed on the model of an actual distribution network, located at the National University of Sciences and Technology (NUST), through load flow analysis to identify the weakest nodes in the grid and the percentage of transformer loading due to additional PEV charging load. In addition, the research paper proposes a techno-economic grid reinforcement solution, performed on the Homer Pro, to minimize the detrimental impact of the additional PEV charging load on grid voltage stability. The proposed solution includes measures such as optimal placement of distributed generations (DGs), including photovoltaic (PV) panels, storage systems, and diesel generators, to ensure reliable and efficient charging infrastructure for PEVs.