Resumen
Electric vehicles (EVs) can help decarbonise the transportation sector, which is responsible for a great share of greenhouse gas (GHG) emissions. Although different measures have been introduced to foster the penetration of EVs in the society, they have not been deployed at a large scale yet. Electric companies are concerned about the elects of introducing EVs into the grid, especially with a large amount. The charging pattern of EVs is the main factor that determines these elects. Unregulated charging (probably when returning home) would have undesirable consequences (e.g. increase in costs, emissions, reduction of reliability) for the system, it is therefore necessary to develop an "intelligent" charging strategy. Nevertheless, there are different agents (e.g. generators, distributors) and objectives (e.g. cost or emission minimisation, demand valley-filling) to consider for an efficient integration of EVs, which makes
the problem even more dificult. These characteristics justify the existence of different smart charging profiles. It is also important to assess the effect of using real-time management systems instead of pre-set profiles.This document compares different possible strategies for charging EVs and their consequences in the power system. The impact on costs, emissions and RES integration will be obtained using the ROM model. This model determines the UC and daily economic dispatch of the system (optimization module), considering the uncertainty associated to some parameters in real-time operation (simulation module). The Spanish power system for 2020 is analysed under different EV penetration levels and charging strategies (unregulated and smart). The results show the benelets of using smart charging profiles instead of an unregulated profile, obtaining large cost reductions and maintaining system reliability levels. More-
over, the benefits of using a real-time management system are also evaluated, resulting in a variable cost system reduction of 0.01% compared to the use of pre-defined charging profiles.
