Current power systems are undergoing an energy transition, where technological elements such as distributed generation and electric vehicles through AC or DC microgrids are important elements to face this transition. This paper presents a methodology for quantifying distributed resource-based generation and the number of electric vehicles that can be connected to isolated DC grids without impacting the safe operation of these networks. The methodology evaluates the maximum capacity of distributed generation considering the uncertainty present in the electric vehicle charging of fleets composed of five types of electric vehicles. Specifically, the uncertainty is associated with the following variables: the home arrival time, home departure time, traveled distance, and battery efficiency. The methodology was applied to a 21-bus DC microgrid and a 33-bus DC network under different test conditions. The results show that higher penetrations of EVs and distributed resource-based generation can be introduced while guaranteeing a secure operation of the DC networks.

Amid growing concerns about climate change, electricity-powered transportation systems stand out as an opportunity to help in reducing fuel consumption. Electric vehicles (EVs) would connect to the grid using clean, renewable electricity; however, the interconnection between EVs and the grid brings about new challenges for traditional power systems. Plug-in hybrid EVs and plug-in EVs have started to become more prevalent in the system; therefore, their impacts and benefits are also of concern. Among these concerns is the detailed analysis of the impact that EVs may have on short-circuit levels in microgrid protection schemes. In this context, the main contribution of this paper is a detailed evaluation of the impact of EVs on the short-circuit levels and protection coordination schemes in microgrids. For this purpose, a methodology was proposed to measure the impact of EVs on the protection coordination schemes in microgrids using different evaluation indices. The proposed approach was validated on a benchmark IEC microgrid considering different operative scenarios that envisage several levels of EVs penetration. The results evidenced the applicability of the proposed approach and allows to conclude that the incorporation of EVs in microgids impacts the performance of the protection schemes, specifically with respect to short-circuit levels.

Objective: In this paper, we review different approaches to how the penetration of electric vehicles (EV) can be modeled in power networks. We also evaluate and compare experimentally the performance of three probabilistic electric vehicle charging load approaches considering four levels of penetration of EV. Methodology: We carry out a detailed search of the state-of-the-art in charging load modeling strategies for electric vehicles, where the most representative works on this subject were compiled. A probabilistic model based on Monte Carlo Simulation was proposed and two more methods were implemented. These models take into account 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 know the distribution that best fits the data of each model. The above was done considering 20, 200, 2000 and 20,000 electric vehicles on average. The results show that if there are 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 you have a high VE penetration. Conclusions: A review of the state of the art of the modeling of electric vehicles under a G2V approach was presented, where three groups are identified: the deterministic approaches, methods that deal with uncertainty and variability, and finally data driven methods were also identified. Additionally, we observed that the EVCP model 3 and the gamma distribution can be appropriate for modeling the penetration of EVs in probabilistic load flow analysis or for stochastic planning studies for active distribution networks. Financing: Institución Universitaria Pascual Bravo