A conceptual framework for integrating electric vehicles into electric power systems is given; impacts and benefits arising from their use are discussed.

This paper describes a research developed to identify management procedures to deal with the charging of Electric Vehicles (EVs) batteries in scenarios characterized by a large scale deployment of this new kind of load. Three approaches were studied: dumb charging, dual tariff policy and smart charging. To assess the efficacy of such procedures, the grid integration of EVs was pushed to its limit for each of the adopted charging management approaches. A Medium Voltage (MV) grid, representative of a residential area distribution grid in Portugal, was used as testing environment. Several shares of EVs technologies were considered for different integration scenarios. Voltage profiles and branch congestion levels were evaluated, for the peak load hour, for grid technical limits checking. Losses were also evaluated for a typical daily load profile.

This work presents a methodology to manage Electric Vehicles (EVs) charging in quasi-real-time, considering the participation of EV aggregators in electricity markets and the technical restrictions of the electricity grid components, controlled by the distribution system operator. Two methodologies are presented in this paper to manage EV charging, one to be used by the EV aggregators and the other by the Distribution System Operator (DSO). The methodology developed for the aggregator has as main objective the minimization of the deviation between the energy bought in the market and the energy consumed by EVs. The methodology developed for the DSO allows it to manage the grid and solve operational problems that may appear by controlling EVs charging. A method to generate a synthetic EV data set is used in this work, providing information about EV movement, including the periods when EVs are parked and their energy requirements. This data set is used afterwards to assess the performance of the algorithms developed to manage the EV charging in quasi-real-time.