Abstract
In this paper we discuss the use of wind power forecasting in electricity market operations. In particular, we demonstrate how probabilistic forecasts can contribute to address the uncertainty and variability in wind power. We focus on efficient use of forecasts in the unit commitment problem and discuss potential implications for electricity market operations.

Abstract
This paper reports a study on the importance of the training criteria for wind power forecasting and calls into question the generally assumed neutrality of the 'goodness' of particular forecasts. The study, focused on the Spanish Electricity Market as a representative example, combines different training criteria and different users of the forecasts to compare them in terms of the benefits obtained. In addition to more classical criteria, an information theoretic learning training criterion, called parametric correntropy, is introduced as a means to correct problems detected in other criteria and achieve more satisfactory compromises among conflicting criteria, namely forecasting value and quality. We show that the interests of wind farm owners may lead to a preference for biased forecasts, which may be in conflict with the larger needs of secure operating policies. The ideas and conclusions are supported by results from three real wind farms. Copyright (c) 2010 John Wiley & Sons, Ltd.

Abstract
In this paper, we investigate the representation of wind power forecasting (WPF) uncertainty in the unit commitment (UC) problem. While deterministic approaches use a point forecast of wind power output, WPF uncertainty in the stochastic UC alternative is captured by a number of scenarios that include cross-temporal dependency. A comparison among a diversity of UC strategies (based on a set of realistic experiments) is presented. The results indicate that representing WPF uncertainty with wind power scenarios that rely on stochastic UC has advantages over deterministic approaches that mimic the classical models. Moreover, the stochastic model provides a rational and adaptive way to provide adequate spinning reserves at every hour, as opposed to increasing reserves to predefined, fixed margins that cannot account either for the system's costs or its assumed risks.

Abstract
This paper proposes a methodology to consider the effects of the integration of DG on planning. Since DG has potential to defer investments in networks, the impact of DG on grid capacity is evaluated. A multi-objective optimization tool based on the meta-heuristic MEPSO is used, supporting an alternative approach to exploiting the Pareto front features. Tests were performed in distinct conditions with two well-known distribution networks: IEEE-34 and IEEE-123. The results combined minimization and maximization in order to produce different Pareto fronts and determine the extent of the impact caused by DG. The analysis provides useful information, such as the identification of futures that should be considered in planning. A future means a set of realizations of all uncertainties. MEPSO also presented a satisfactory performance in obtaining the Pareto fronts. © 2011 IEEE.

Abstract
This paper analyzes the application of the Information Theoretic (IT) Mean Shift algorithm for modes finding in order to provide the classification of Electricity Customer Load Patterns. The impact of the algorithm parameters is discussed and then clustering indices are used in order to make a comparison with the classical methods available. Results show a good capability of the modes found in capturing the data structure, aggregating similar load patterns and identifying the uncommon patterns (outliers). © 2011 IEEE.

Abstract
This paper presents a generation adequacy evaluation based on analytical calculation, considering electric vehicles. The scenarios used are exploring electric vehicles penetrations in six different European countries, in order to assess their impact on the security of supply. An analytical method is developed to perform this evaluation. Afterwards, a discussion about the accuracy of this methodology is done and the differences between this approach and a flexible Sequential Monte Carlo Simulation are identified © 2011 IEEE.