This paper presents a conceptual framework to successfully integrate electric vehicles into electric power systems. The proposed framework covers two different domains: the grid technical operation and the electricity markets environment. All the players involved in both these processes, as well as their activities, are described in detail. Additionally, several simulations are presented in order to illustrate the potential impacts/benefits arising from the electric vehicles grid integration under the referred framework, comprising steady-state and dynamic behavior analysis.

An electric vehicle (EV) aggregation agent, as a commercial middleman between electricity market and EV owners, participates with bids for purchasing electrical energy and selling secondary reserve. This paper presents an optimization approach to support the aggregation agent participating in the day-ahead and secondary reserve sessions, and identifies the input variables that need to be forecasted or estimated. Results are presented for two years (2009 and 2010) of the Iberian market, and considering perfect and naïve forecast for all variables of the problem.

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 paper addresses the participation of an aggregator of small prosumers in the energy and tertiary reserve markets. A two-stage stochastic optimization model is proposed to exploit the load and generation flexibility of the prosumers. The aim is to define energy and tertiary reserve bids to minimize the net cost of the aggregator buying and selling energy in the day-ahead and real-time markets, as well as to maximize the revenue of selling tertiary reserve during the real-time stage. Scenario-based stochastic programming is used to deal with the uncertainties of photovoltaic power generation, electricity demand, outdoor temperature, end-users’ behavior and preferences. A case study of 1000 small prosumers from MIBEL is used to compare the proposed strategy to two other strategies. The numerical results show that the proposed strategy reduces the bidding net cost of the aggregator by 48% when compared to an inflexible strategy typically used by retailers.