Advancing Electric Vehicle Load Estimation for Power Distribution Systems

The rapid adoption of electric vehicles (EVs) challenges electric distribution grids due to increased demand and the need for efficient grid management. This paper introduces a methodology to estimate future EV charging loads considering electric, socioeconomic, transport, and household factors. By combining Monte Carlo simulations with regression models, we analyze EVs’ spatial distribution and charging demand in Santiago, Chile. Our main methodological contribution is the integration of Monte Carlo simulations, multivariate linear regression, and spatial analysis to provide nuanced estimations of EV charging demand in different substations. This approach accounts for variability and enhances the reliability and usability of our estimations. Incorporating stochastic variables allows the examination of different EV penetration and usage scenarios, offering insights into their distribution network impacts. This method improves demand estimation, which is key for grid management and infrastructure planning. Our flexible methodology can be applied to various urban and energy contexts, facilitating the adaptation of electrical infrastructure for electric mobility.