Optimum Copula-Based Stochastic Planning of Electric Vehicle Fast-Charging Stations in Coupled Electric-Transport Networks

Abstract

The increasing penetration of electric vehicles (EVs) introduces significant uncertainties into fast-charging station (FCS) planning due to the stochastic nature of EV charging behavior. Accurately representing these uncertainties is essential for making reliable planning decisions in coupled transportation–power networks. This paper proposes a copula-based stochastic planning framework for the optimal allocation of FCSs while accounting for the correlated uncertainties associated with EV charging behavior. A multivariate copula model is employed to capture the dependency structure among key charging variables and generate realistic stochastic charging scenarios, which are subsequently incorporated into the EV charging load forecasting process over the planning horizon. Based on the resulting stochastic charging demand, a multi-objective optimization model is developed to simultaneously minimize investment costs and EV users’ travel distances, improve distribution network performance, and maximize environmental benefits through decarbonization. In addition, distributed generation (DG) units are optimally integrated to improve voltage profiles and reduce power losses. The proposed framework is implemented using MATLAB R2013a and R.4.0.2 and evaluated using both the IEEE 33-bus test system and a realistic 37-bus coupled transportation–power network in Meshgin-Shahr, Iran. The results demonstrate the effectiveness of the proposed stochastic planning framework in addressing uncertainties in EV charging behavior and identifying robust FCS deployment strategies.