Objective:

In this paper, different approaches to how the penetration of electric vehicles (EV) can be modeled in power networks are reviewed. The performance of three probabilistic electric vehicle charging load approaches considering four levels of penetration of EV is also evaluated and compared.

Methodology:

A detailed search of the state-of-the-art in charging load modeling strategies for electric vehicles is carried out, where the most representative works on this subject were compiled. A probabilistic model based on Monte Carlo Simulation is proposed, and two more methods are implemented. These models consider the departure time of electric vehicles, the arrival time, and the plug-in time, which were conceived as random variables.

Results:

Histograms of the demand for charging of electric vehicles were obtained for the three models contemplated. Additionally, a similarity metric was calculated to determine the distribution that best fits the data of each model. The above was done considering 20, 200, 2.000, and 20.000 electric vehicles on average. The results show that, if there is a low penetration of electric vehicles, it is possible to model the EV charging demand using a gamma distribution. Otherwise, it is recommended to use a Gaussian or lognormal distribution if there is a high EV penetration.

Conclusions:

A review of the state of the art of the modeling of electric vehicles under a G2V approach is presented, where three groups are identified: deterministic approaches, methods that deal with uncertainty and variability, and data-driven methods. Additionally, it was observed that EVCP model 3 and gamma distribution could be appropriate for modeling the penetration of electric vehicles in probabilistic load flow analysis or for stochastic planning studies for active distribution networks.

Funding:

Institución Universitaria Pascual Bravo