Abstract
The text explains that the fuzzy approaches have the objective to bring the decision process in planning closer to the decision maker, by allowing him to understand better the diversity of aspects that must be considered in planning decisions and helping the decision process while keeping, as much information as possible, represented in the definition of fuzzy sets. The paper shows that the qualitative aspects of uncertainty, risk and decision making may be adequately modeled with a fuzzy set approach. It could help the decision maker guiding him towards a decision that takes in account uncertainty in the future, the multiple criteria evaluation of plans, as well as hedging policies.

Abstract
This work presents a model developed to evaluate the Dynamic Adaptation of an Electric Energy Distribution System (EEDS) respect to its planning for a given period of Tariff Control. The model is based on a two-stage strategy that deals with the mid/short-term and long-term planning, respectively. The starting point for modeling is brought about by the results from a multi-attribute method based on Fuzzy Dynamic Programming and on Analytic Hierarchy Processes (FDP + AHP) for a mid/short-term horizon. Such a method produces a set of possible evolution trajectories which can be defined as satisfactory when they evolve above a given risk threshold that the planner is willing to accept. Then, the decision-making activities within the framework of the Analytical Hierarchy Processes are those tasks that allow defining a vector for dynamic adaptation of the system, which is directly associated to an eventual series of imbalances that take place during its evolution.

Abstract
This paper presents a new methodology for assessing both reliability and well-being indices for composite generation and transmission systems. Firstly, a transmission network reduction is applied to find an equivalent for assessing composite reliability for practical large power systems. After that, in order to classify the operating states, Artificial Neural Networks (ANNs) based on Group Method Data Handling (GMDH) techniques are used to capture the patterns of the operating states, during the beginning of the non-sequential Monte Carlo simulation (MCS). The idea is to provide the simulation process with an intelligent memory, based only on polynomial parameters, to speed up the evaluation of the operating states. For the conventional reliability assessment, the ANNs are used to classify the operating states into success and failure. However, for the well-being analysis, only success states are classified into healthy and marginal by the ANNs. The proposed methodology is applied to the IEEE Reliability Test System 1996 and to a configuration of the Brazilian South-Southeastern System.

Abstract
This paper applies the concept of fuzzy controller to voltage/VAR control. Cascading Mamdani controllers are used together with sensitivities to keep voltages in an admissible band while avoiding line flows to exceed admissible limits. A small number of iteration steps with a Newton-Raphson power flow routine are enough to recover voltages into admissible bands.

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.

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.