Abstract
The participation of an EV aggregator in the electricity market for purchasing electrical energy requires an algorithm for managing the EV charging during the operational day. In this paper the coordination of EV for minimizing the deviation between bid and consumed electrical energy is studied and compared with an uncoordinated strategy. Two algorithms are proposed: a heuristic algorithm that dispatches the EV for each time interval separately, and another one, formulated as an optimization problem for dispatching the EV considering all the time intervals. Furthermore, the aggregator architecture is compared with an autonomous architecture where each EV operates and participates in the market individually. The results, for a realistic case-study, show that the aggregator with an optimized coordination strategy achieves the lowest deviation cost and magnitude.

Abstract
It is foreseeable that electricity retailers for electrical mobility will be market agents. These retailers are electric vehicle (EV) aggregation agents, which operate as a commercial middleman between electricity market and EV owners. Furthermore, with the foreseen evolution of the smart-grid concept, these agents will be able to control the EV charging rates and offer several ancillary services. This paper formulates an optimization problem for the EV aggregation agent participation in the day-ahead and secondary reserve market sessions. Forecasting issues are also discussed. The methodology was tested for two years (2009 and 2010) of the Iberian market, considering perfect and naïve forecast for all variables of the problem. © 2011 IEEE.

Abstract
The increasing levels of wind power penetration motivated a revisitation of methods for setting operating reserve requirements for the next and current day. System Operators (SO) are now moving from deterministic intro probabilistic approaches, and including wind power forecasts in their decision-making problems. In this manuscript, a probabilistic approach that evaluates the consequences of setting each possible reserve level through a set of risk indices is compared with frequently used deterministic rules and a probabilistic rule where wind power uncertainty is described by a Gaussian distribution. The comparison is performed over a period of five months for a realistic power system, using real load and wind power generation data. Results highlight the limitations of deterministic rules, challenge the Gaussian assumption and illustrate the usefulness of risk indices derived from the probabilistic forecast and using a full probabilistic methodology.

Abstract
An aggregator agent for electric vehicles is a commercial middleman between a system operator and plug-in electrical vehicles (EV). For the system operator perspective, the aggregator is seen as a large source of generation or load, which could provide ancillary services such as spinning and regulating reserve. Generally these services will be provided in the day-ahead and intraday electricity markets. In addition, the aggregator also participates in the electricity market with supply and demand energy bids. In this paper, the integration of these concepts in an electricity market environment is discussed through proposing a framework for the information characterization (and availability) between aggregator, system operators and clients. A specific market (the Iberian Market - MIBEL) is discussed. In the sequence, the different degrees of availability of the relevant information are identified and characterized, including the variables that are necessary to forecast.

Abstract
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 naive forecast for all variables of the problem.

Abstract
This paper reports results and an evaluation methodology from two new decision-aid tools that were demonstrated at a Transmission System Operator (REN, Portugal) during several months in the framework of the E. U. project Anemos. plus. The first tool is a probabilistic method intended to support the definition of the operating reserve requirements. The second is a fuzzy power flow tool that identifies possible congestion situations and voltage violations in the transmission network. Both tools use as input probabilistic wind power predictions.