Abstract
This paper presents a new stochastic programming model for PAR/PST definition and location in a network with a high penetration of wind power, with probabilistic representation, to maximize wind power penetration. It also presents a new optimization meta-heuristic, denoted DEEPSO, which is a variant of EPSO, the Evolutionary Particle Swarm Optimization method, borrowing the concept of rough gradient from Differential Evolution algorithms. A test case is solved in an IEEE test system. The performance of DEEPSO is shown to be superior to EPSO in this complex problem. © 2014 IEEE.

Abstract
The future large-scale deployment of electric vehicles (EV) will not only have impact on load growth, but also create opportunities for the electricity sector. Generally, the current methods for security of supply long-term evaluation do not include this new type of load. While the electric components of the generating systems are usually modelled by the Markov process, this paper presents, as its major contribution, an EV model based on the Nonhomogeneous Poisson process, which has been developed in order to better represent the motorized citizen mobility and the EV opportunity to release spinning reserve to electric systems. The simulation procedure lies in combining both Poisson and Markov processes into a sequential Monte Carlo simulation (SMCS) to measure the impact of EV when evaluating the adequacy of generating systems. This evaluation is divided into two complementary concepts: static reserve (generating capacity reserve) and operating capacity reserve. The proposed models are analyzed using a modified version of the IEEE RTS-96 including renewable sources.

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.