Probabilistic Analysis of Impacts on Distribution Networks due to the Connection of Diverse Models of Plug-in Electric Vehicles
Keywords:Electric Vehicles, Monte Carlo Simulation, Distributed systems
This work analyzes impacts on distribution networks produced by the connection of diverse types of Plug-in Electric Vehicles (PEVs), into a probabilistic framework. Some of the uncertainty sources are related with technical parameters of PEVs. Therefore, a review of PEVs currently available in the market is reported. Other uncertain parameters are related with the behavior of the PEV owners, for instance, the arrival and departure times to home, and the state of charge of the PEV when it is plugged to the grid. These parameters are modeled by using probability density functions, to then generate random numbers and perform Monte Carlo simulations. Each Monte Carlo simulation corresponds to the calculation of a power-flow in the analyzed network. The proposed methodology is tested on the IEEE 33-bus test distribution network, with the purpose of quantifying the influence of diversity of PEVs. The analysis is performed, specifically, for identify those transformers and lines that could be overloaded. Two scenarios of PEV penetration by 2025 and 2030 were assessed, i.e., considering that 10% and 30% of the residential customers will have at least one PEV, respectively. Obtained results reveal the importance of considering diversity of PEV model to conduct in a suitable manner this type of studies. The proposed methodology is expected to be useful for network planning expansion and to support the design of time of use tariffs.