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 summarizes efforts in understanding the possible application of Information Theoretic Learning Principles to Power Systems. It presents the application of Renyi's Entropy combined with Parzen windows as a measure of information content of the error distribution in model parameter estimation in supervised learning. It illustrates the concept with an application to the prediction of power generated in a wind park, made by Takagi-Sugeno Fuzzy Inference Systems, whose parameters are discovered with an EPSO-Evolutionary Particle Swarm Optimization algorithm.

Abstract
Prediction models for generation from renewables are needed in the context of a power system with a diversified portfolio. The presentation will discuss a new criterion and procedure to develop prediction models based on Renyils Entropy combined with Parzen windows (an approach named Information Theoretic Learning) that is applied to wind prediction and suggested as a better training paradigm for fuzzy or neural systems.

Abstract
This paper discusses some issues related with the growing importance of wind power and in modern power systems and some challenges raised by the emergence of distributed generation, and how computational intelligence and other modern techniques have been able to provide valuable results in solving the new problems. It presents some solutions obtained with a number of computational intelligence techniques and their application to real cases. © TÜBITAK.

Abstract
Unbalanced feeders produce problems and risky situations since they can trip protection devices, reduce the efficiency and damage some appliances. Therefore achieve the balance of the networks will mean an improvement of the electric service and reduction expenses. The proposed program is a tool for balancing the electric networks; swapping loads among phases of the main lines, to assure that the loads average among the phases of the main lines not do not in large magnitudes. It is assumed that all loads have the same power factor and voltage drops are not considered. As a fundamental contribution of this work, an additional restriction is added to the mathematical model that avoids the introduction of additional phases.

Abstract
This paper valuates generation assets within deregulated electricity markets. A new framework for modeling electricity markets with a Markov chain model is proposed. The Markov chain model captures the fundamental economic forces underlying the electricity markets such as demand on electricity and supplied online generation capacity. Based on this new model, a real option analysis is adopted to valuate generation assets. The Markov chain model is combined with a binomial tree to approximate the stochastic movement of prices on both electric energy and ancillary services, which are driven by the market forces. A detailed example is presented. This method is shown to provide optimal operation policies and market values of generation assets. This method also provides means to analyze the impacts of demand growth patterns, competition strategies of competitors, and other key economic forces.