Estimation of uncertainty in electrical loads due to electric vehicle charging

Relevance . This study addresses the need to develop a method for modeling the load profile of electric vehicle charging stations (EVCS) while accounting for parameter uncertainties, including the stochastic intensity of electric vehicle (EV) connections over a given period. The Purpose. Analyze challenges in modeling EVCS load profiles under uncertainty. Develop a simulation method for EVCS load profiles that incorporates multiple stochastic components. Simulate load profiles reflecting the intensity of charging start times using empirical data. Evaluate EVCS load uncertainty, model convergence, and sensitivity to input parameter variations. Methods . The study analyzes existing methods for modeling EVCS load profiles. A Monte Carlo method, implemented in MatLab®, was used to construct a mathematical model of the EVCS load profile. Results. Experimental data on the number of connected EVs are processed to derive an average daily EVCS load profile. A combined probability distribution law, aligned with empirical data and reflecting EV connection intensity, is applied. A parametric model is developed to generate the temporal load profile of EVCS, incorporating key uncertainty factors: charging start time, EV power consumption, charging duration, and the number of EVs. Conclusions. A method and model for simulating EVCS load profiles are proposed, enabling the generation of temporal power profiles under input data uncertainty. The model can be applied to plan EVCS placement, evaluate power imbalance and select parameters for energy storage systems to integrate EVCS and ensure their stable operation in distribution grids.