Integration of Electric Vehicle Load and Charging Infrastructure in Distribution Network

While road electrification offers economic and environmental advantages, the non-conventional load due to electric vehicles usage and charging patterns pose challenges to distribution systems. The strategic design of charging infrastructure is becoming an essential element to facilitate power system planning and decision-making process. This paper presents a probabilistic method to derive charging patterns and estimate the electric vehicle demand profiles under uncertainty and variability. We apply a Gaussian copula to capture correlations between the key multivariates. We investigate the optimal location and size of charging stations based on queueing theory and intercepted traffic flow model. We examine the impact of the charging demand occurred in residential and public area on distribution expansion investment and incremental operational cost. The feasibility of the approach is tested on an interconnected distribution grid and transportation system. The case studies show that a careful probabilistic analysis of the randomness intrinsic to the charging behavior is of great importance to define and implement an integrated power and transportation system design.