Abstract
A probabilistic forecast, in contrast to a point forecast, provides to the end-user more and valuable information for decision-making problems such as wind power bidding into the electricity market or setting adequate operating reserve levels in the power system. One important requirement is to have flexible representations of wind power forecast (WPF) uncertainty, in order to facilitate their inclusion in several problems. This paper reports results of using the quantile-copula conditional Kernel density estimator in the WPF problem, and how to select the adequate kernels for modeling the different variables of the problem. The method was compared with splines quantile regression for a real wind farm located in the U.S. Midwest. © 2011 IEEE.

Abstract
In this paper we discuss how probabilistic wind power forecasts can serve as an important tool to efficiently address wind power uncertainty in power system operations. We compare different probabilistic forecasting and scenario reduction methods, and test the resulting forecasts on a stochastic unit commitment model. The results are compared to deterministic unit commitment, where dynamic operating reserve requirements can also be derived from the probabilistic forecasts. In both cases, the use of probabilistic forecasts contributes to improve the system performance in terms of cost and reliability. © 2011 IEEE.

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.