Abstract
The system operator is responsible for maintaining a constant balance between generation and load to keep frequency at the nominal value. This fundamental objective is achieved with upward (e.g., synchronized and nonsynchronized generation units) and downward (e.g., demand response, storage) reserve capacity. The system operator needs to define, in advance, the reserve capacity requirements that mitigate the risk of imbalances due to forecast errors and unplanned outages of generation units. The research trend is to apply probabilistic methodologies for setting the reserve requirements based on uncertainty forecasts for renewable generation and load, as well as a probabilistic modeling of units' outages. This chapter describes two probabilistic methods, which share a common modeling framework, for quantifying risk and reserve requirements in two types of electricity markets: (1) sequential markets with the reserves market after the energy market clearing and (2) cooptimization (or joint market clearing) of energy and reserves. Two case studies with real data are presented to illustrate the application of both methodologies.

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
Large scale integration of micro-generation, together with active loads and energy storage devices, under micro-grid and multi micro-grid concepts, requires the adoption of advanced control strategies at different distribution network levels. This paper presents advanced control functionality to be housed at high voltage (HV)/medium voltage (MV) substations and to be used to manage micro-generation, active loads and energy storage, subject to different constraints. Some of these constraints involve inter-temporal relations, such as the ones related with energy storage levels in consecutive time moments. This functionality is specially oriented to deal with stressed MV network operation involving overload and excessive voltage drops situations.

Abstract
Reliable and smart information on the flexibility provision of Home Energy Management Systems (HEMS) represents great value for Distribution System Operators and Demand/flexibility Aggregators while market agents. However, efficiently delimiting the HEMS multi-temporal flexibility feasible domain is a complex task. The algorithm proposed in this work is able to efficiently learn and define the feasibility search space endowing DSOs and aggregators with a tool that, in a reliable and time efficient faction, provides them valuable information. That information is essential for those agents to comprehend the fully grid operation and economic benefits that can arise from the smart management of their flexible assets. House load profile, photovoltaic (PV) generation forecast, storage equipment and flexible loads as well as pre-defined costumer preferences are accounted when formulating the problem. Support Vector Data Description (SVDD) is used to build a model capable of identifying feasible HEMS flexibility offers. The proposed algorithm performs efficiently when identifying the feasibility of multi-temporal flexibility offers.