Montecarlo Technique and Logistic Growth Patterns-Based Forecasting Model for the Integration of Private Electric Vehicle in Ecuador and their Future Power Demand Requeriments.

The large-scale integration of electric vehicles (EVs) is key to an energy transition towards more sustainable transportation systems, but its impact on electric distribution networks is still unknown. The lack of precise methodologies regarding EV market evolution and the mobility patterns for electric charging has hindered the identification of a model that estimates the number of vehicles that could be incorporated into electric systems and, consequently, the evolution of the future demand daily curves. To address this situation, in this work, an integrated stochastic model is developed. This model applies not only a methodology based on sigmoidal logistic growth models (Scurve) to forecast the adoption of electric technologies, but also proposes an algorithm implemented in Python to stochastically determine charging profiles with a temporal resolution of 10 minutes. The proposed model is applied to the regulated customers’ database of the Empresa Eléctrica Quito - E.E.Q., to identify differences in EV charging patterns on weekdays and weekends due to the number of simulated vehicles and technical factors, such as the state of charge (SoC), time, and charging power. By analyzing the controlled integration of fully electric residential light-duty vehicles, considering time-of-use tariff, it has been determined that the distribution system’s maximum demand by 2032 would increase by 3.3% (38 MW) compared to the baseline demand.