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.

Abstract
This paper presents a new approach to incipient fault diagnosis in power transformers, based on the results of dissolved gas analysis. A set of autoassociative neural networks or autoencoders are trained, so that each becomes tuned with a particular fault mode. Then, a parallel model is built where the autoencoders compete with one another when a new input vector is entered and the closest recognition is taken as the diagnosis sought. A remarkable accuracy is achieved with this architecture, in a large data set used for result validation. © 2011 IEEE.

Abstract
This paper analyzes the application of clustering techniques for wind power scenario reduction. The results have shown the unimodal structure of the scenario generated under a Monte Carlo process. The unimodal structure has been confirmed by the modes found by the information theoretic learning mean shift algorithm. The paper also presents a new technique able to represent the wind power forecasting uncertainty by a set of representative scenarios capable of characterizing the probability density function of the wind power forecast. From an initial large set of sampled scenarios, a reduced discrete set of representative scenarios associated with a probability of occurrence can be created finding the areas of high probability density. This will allow the reduction of the computational burden in stochastic models that require scenario representation. © 2011 IEEE.