The rapid expansion of electric mobility and distributed renewable generation presents new operational challenges for low-voltage (LV) distribution networks, including increased evening peak demand, higher transformer utilization, and phase unbalance, particularly in emerging regions where options for reinforcement are constrained. This paper describes an open-source Python–OpenDSS framework that combines measured slow-charging profiles with Monte Carlo sampling of electric vehicle (EV) location, vehicle model, initial state of charge, and charging start time, with optional single-phase photovoltaic (PV) sized to offset annual customer demand. Each scenario is evaluated with 100 iterations, and 10-minute time-series power-flow simulations over a one-day (24 h) horizon are summarized using ensemble statistics (e.g., mean and selected percentiles). The framework is applied to a real 50 kVA urban LV feeder in Cuenca, Ecuador (62 residential customers), whose model was built from the utility GIS database and corroborated through an on-site inspection, under EV penetration levels of 5%, 10%, and 15% with EV-only and EV+PV configurations. For the range studied, steady-state voltages and voltage unbalance remain within typical LV compatibility limits, while transformer utilization increases from about 59% in the baseline to around 90% at 15% EV penetration. Co-located PV reduces the net daily energy exchanged with the upstream network but has limited impact on evening transformer peaks. A 7-day extension of the 15% EV scenario is included to illustrate multi-day studies using the same workflow.
Banegas-Arias et al. (Thu,) studied this question.
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