Abstract
The power produced by wind power plants has an extremely random character due to the intermittency of wind. This leads to problems in balancing the power production and demand in the power systems. To overcome this problem, wind power forecast is used. However, as in any prediction tasks, wind power forecasting does not offer perfect results. It is the purpose of this paper to propose a method based on Monte Carlo simulations and artificial intelligence techniques to assess the impact of the deviation of the generated wind power from the predicted values on the power systems when no corrective measures are taken. The method is tested on an IEEE network as well as on a real electric network from the Romanian power system and the results and drawn conclusions are presented here.

Abstract
This chapter proposes a new way to detect and represent the probability of ramping events in short-term wind power forecasting. Ramping is one notable characteristic in a time series associated with a drastic change in value in a set of consecutive time steps. Two properties of a ramp event forecast, that is, slope and phase error, are important from the point of view of the system operator (SO): they have important implications in the decisions associated with unit commitment or generation scheduling, especially if there is thermal generation dominance in the power system. Unit commitment decisions, generally taken some 12-48 h in advance, must prepare the generation schedule in order to smoothly accommodate forecasted drastic changes in wind power availability. © Springer India 2013.

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
The charging flexibility of electric vehicles (EV) when aggregated by a market agent creates an opportunity for selling manual reserve in the electricity market. This paper describes a new optimization algorithm for optimizing manual reserve bids. Furthermore, two operational management algorithms covering alternative gate closures (i.e., day-ahead and hour-ahead) are also described. These operational algorithms coordinate EV charging for mitigating forecast errors. A case-study with data from the Iberian electricity market and synthetic EV time series is used for evaluating the algorithms.

Abstract
This paper addresses the bidding problem faced by an electric vehicles (EV) aggregation agent when participating in the day-ahead electrical energy market. Two alternative optimization approaches, global and divided, with the same goal (i.e. solve the same problem) are described. The difference is on how information about EV is modeled. The global approach uses aggregated values of the EV variables and the optimization model determines the bids exclusively based on total values. The divided approach uses individual information from each EV. In both approaches, statistical forecasting methods are formulated for the EV variables. After the day-ahead bidding, a second phase (named operational management) is required for mitigating the deviation between accepted bids and consumed electrical energy for EV charging. A sequential linear optimization problem is formulated for minimizing the deviation costs. This chain of algorithms provides to the EV aggregation agent a pathway to move to the smart-grid paradigm where load dispatch is a possibility.

Abstract
This paper proposes a method to determine the availability of loads to provide tertiary Reserve Services (RS) in the framework of Smart Grids. The method uses Physically-Based Load Models (PBLM) and considers the remuneration paid for the RS provision. The concept of availability is also discussed in the paper and the method is applied to three different categories of domestic thermal appliances: electric water heater, air conditioners and refrigerators.