Kenari, Meghdad TourandazOzdemir, Aydogan2025-03-152025-03-1520250018-95451939-9359https://doi.org/10.1109/TVT.2025.3538538The increasing market share and parking occupancy of electric vehicles have increased the charging stations in parking lots (PLs) and motivated the research to provide proper management strategies. Although there have been several efforts to assess, schedule, and model the load of fast electric vehicle charging stations (FEVCSs), they are inappropriate for charging stations in EVPLs. This paper proposes a novel comprehensive probabilistic approach to calculate the parking lot power and energy for battery and hybrid electric vehicles. At first, a decision is made for the operation mode of an EV arriving at the PL, using an algorithm considering ten influential random variables. Then, the aggregated parking lot power and energy are determined using Monte Carlo simulations. Finally, the Gaussian Mixture Model is used to estimate the parameters of the output probability density functions, where the maximum likelihood estimation is employed to find model components. The proposed approach is applied to a sample parking lot, and its performance is demonstrated by comparing it to a base case study and one of the pioneering techniques introduced in the literature. Finally, a thorough sensitivity analysis is applied to assess the robustness of the outputs under different scenarios. The results demonstrate the superiority of the proposed method compared to the available studies.eninfo:eu-repo/semantics/closedAccessVehicle-to-GridLoad ModelingBatteriesProbabilistic LogicElectric Vehicle ChargingDistribution NetworksOptimal SchedulingGaussian Mixture ModelFast ChargingStochastic ProcessesElectric VehicleParking LotProbabilistic AssessmentTransportation SystemVehicle-to-Grid OperationArticle10.1109/TVT.2025.35385382-s2.0-85217453617