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
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.

Abstract
In order to participate in the electricity market, electric vehicles (EV) need to be aggregated by a market agent, since the current rules do not allow the participation of small loads. The EV aggregator purchases electrical energy for charging its clients, and can offer reserve services. This activity requires forecasting methods for several variables. This paper presents a global view of the relevant variables for an EV aggregation agent participating in the electricity market and discusses the associated forecasting issues. The load forecast problem for direct and indirect control of the EV charging process is discussed. Variables from the market-side, such as reserve price and direction, are also addressed. Existing approaches are reviewed, discussed and tested according to different metrics.

Abstract
The foreseeable increase in the use of electric vehicles (EV) led to the discussion on intermediate entities that could help manage a great number of EV. An aggregation agent for electric vehicles is a commercial middleman between a system operator (SO) and plug-in EV. From the SO 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. This paper provides a comprehensive bibliographic survey on the aggregator role in the power system operation and electricity market. The scope of the survey covers 59 references divided in journal, conference proceedings, thesis, research papers, and technical reports published after 1994. These papers are put into several technical categories: electricity market and EV technical and economic issues; aggregation agent concept, role and business model; algorithms for EV management as a load/resource. Copyright (C) 2011 John Wiley & Sons, Ltd.

Abstract
A decision-making approach based on multi-criteria techniques can help to make the massive deployment of the microgrid concept a viable solution, potentially leading to proper incentive mechanisms for distribution system operators, microgeneration owners and loads. © 2012 Elsevier Inc.