Probabilistic Risk Assessment of Electric Vehicle Charging Load on Argentine Distribution Network

Abstract

In this article, we propose a multivariate probabilistic model to analyze the impact of electric vehicle (EV) connection on low-voltage (LV) distribution networks. First, we analyze the distribution network behavior under unmanaged charging strategies, considering different EV penetration factors on the grid. We consider the distances traveled by EVs, EV arrival time, state of charge (SOC), and residential demand as statistical variables. We use a methodology that includes Monte-Carlo simulations. Then, we analyze the level of technical losses, node voltage, and power flows. The results show that leaving the charging process in the hands of the user produces unacceptable technical levels for the proper functioning of the grid. In this paper, we present two possible solutions to increase the level of EV penetration while meeting the technical requirements of the grid. The first is demand management, postponing the average start time of the charging process until late at night. This method depends on the demand curve, but our case study shows good results due to the low-load nature of the grids late at night. The second solution consists of modifying the load factor of the MV/LV transformer (and possibly the traditional radial grid design) to allow for greater EV insertion. This could be interesting in a context where grid design is being considered. Evaluating the results provides relevant information for Argentine distribution system operators (DSOs) and regulatory authorities before drafting efficient and well-founded regulations for the introduction of this technology.