Abstract
In the Smart Grid environment, it is expected that Home Energy Management Systems (HEMS) should be capable of managing appliances inside the house, in order to provide reserve services. This paper aims at estimating the flexibility of a Portuguese typical residential load diagram. For this purpose, a bottom-up approach is used, i.e., the aggregated flexible load demand is determined taking into account the availability of each appliance to be controlled by the HEMS. The comfort requirements of the consumers are also incorporated in the flexibility calculations by integrating realistic load models into HEMS.

Abstract
The Electric Vehicle (EV) is one source of flexibility to the electric power system. When aggregated by a market agent, it can offer its flexibility in the balancing reserve market. In order to meet this goal, a framework of optimization and forecasting algorithms must designed to cover the different time horizons of the decision process. This paper describes a full framework for EV aggregators participating in different electricity market sessions. This framework is illustrated for the balancing reserve market and the impact of forecasts of different quality for the balancing reserve direction is evaluated. The test case consists in synthetic time series generated from real data for 3000 EV participating in the Iberian electricity market.

Abstract
The concerns about global warming (greenhouse-gas emissions), scarcity of fossil fuels reserves, and primary energy independence of regions or countries have led to a dramatic increase of renewable energy sources (RES) penetration in electric power systems, mainly wind and solar power. This created new challenges associated with the variability and uncertainty of these sources. Handling these two characteristics is a key issue that includes technological, regulatory, and computational aspects. Advanced tools for handling RES maximize the resultant benefits and keep the reliability indices at the required level. Recent advances in forecasting and management algorithms provided means to manage RES. Forecasts of renewable generation for the next hours/days play a crucial role in the management tools and protocols of the system operator. These forecasts are used as input for setting reserve requirements and performing the unit commitment (UC) and economic dispatch (ED) processes. Probabilistic forecasts are being included in the management tools, enabling a move from deterministic to stochastic methods, which conduct to robust solutions. On the technological side, advances to increase mid-merit and base-load generation flexibility should be a priority. The use of storage devices to mitigate uncertainty and variability is particularly valuable for isolated power system, whereas in interconnected systems, economic criteria might be a barrier to invest in new storage facilities. The possibility of sending active and reactive control set points to RES power plants offers more flexibility. Furthermore, the emergence of the smart grid concept and the increasing share of controllable loads contribute with flexibility to increase the RES penetration levels. (C) 2013 John Wiley & Sons, Ltd.

Abstract
This paper aims at studying the impact of the Electric Vehicles (EV) charging demand and its uncertainty in the adequacy of the transmission grid using the Linearized approach of the Symmetric Fuzzy Power Flow analysis. The fuzzy modelling of the uncertainties caused by the presence of EV in the system is discussed. Two types of charging scenarios are considered: dumb charging and smart charging. Finally, a fuzzy power flow analysis considering the uncertainties associated to the EV load is applied to a test system as well as to the peak load scenario of Portuguese system in 2030, discussing the possibility of congestion occurrence and nodes voltages out of the tolerance limits.

Abstract
Power system regulators and operators are creating conditions for encouraging the participation of the demand-side into reserve markets. The electric vehicle (EV), when aggregated by a market agent, holds sufficient flexibility for offering reserve bids. Nevertheless, due to the stochastic nature of the drivers' behavior and market variables, forecasting and optimization algorithms are necessary for supporting an EV aggregator participating in the electricity market. This paper describes a new day-ahead optimization model between energy and secondary reserve bids and an operational management algorithm that coordinates EV charging in order to minimize differences between contracted and realized values. The use of forecasts for EV and market prices is included, as well as a market settlement scheme that includes a penalty term for reserve shortage. The optimization framework is evaluated in a test case constructed with synthetic time series for EV and market data from the Iberian electricity market.

Abstract
Combining conventional and unconventional generations with hard forecasting properties and consumption variability has made the task of fitting large amounts of wind generation into unit commitment procedures very challenging. In this context, massive integration of electric vehicles in the electric systems, is expected. This paper aims at evaluating the adequacy of a generating system taking into account the electric vehicles and high wind power integration level in its energy mix. This study was conducted on the European project MERGE framework, and the results have shown that the generating system evaluated is reliable. Nevertheless, the simulations carried out for scenarios with adverse weather conditions (dry years) have revealed specific circumstances that might jeopardize the system adequacy.