Abstract
In this paper we present a complete methodology to perform state estimation studies in distribution networks. Due to the peculiarities of these networks the traditional state estimation concept was enlarged in different ways. It includes a load allocation study, as a way to cope with the reduced number of real time measurements in SCADA database. The algorithm estimates binary values of topology variables, due to incomplete or erroneous topology information in the control center and it is able to include data modeled by fuzzy numbers as a way to include fuzzy results of the load allocation procedure or fuzzy assessments from experts. Finally, the paper describes a methodology developed to tune the weights to be used in the state estimation based on a Takagi-Sugeno fuzzy inference system. The paper includes a case study based in the IEEE 24 bus system to highlight and illustrate its application in a variety of situations.

Abstract
This paper presents a method used to validate a spatial load forecasting model based on fuzzy systems implemented in a Geographical Information System. The validation process confirms the adequacy of the rule base, and also it is strictly necessary to define the confidence intervals associated to the predicted spatial demand.

Abstract
One of the important decision problems for a power system operator is to choose the appropriate operation point when possible contingencies must be considered. The operator may decide to do nothing or to take some actions that change the operating point and decrease the negative impact (cost) of some or all the contingencies. The purpose of this paper is to investigate the applicability of utility and value functions to this decision problem, since the large range of consequences raises some doubts about the fulfillment of the theoretical conditions imposed by Decision Theory. Although not essential to this discussion, some alternatives to prescriptive methodologies are presented and discussed, mostly regarding the definition of risk attributes and other determinist equivalents, suitable for multicriteria analysis.

Abstract
This paper addresses the allocation of electrical losses in distribution networks with embedded generation, in a liberalized environment. The nonlinear nature of the issue, the loss changes due to voltage variation and, specially, the contribution of embedded generation to loss variation are considered. The proposed method is based on tracing the real and imaginary parts of the currents and has two steps. First, the losses in the distribution network, in the absence of embedded generation, are allocated to the consumers (or their providers). Second, the variations in the losses that result from the influence of embedded generation are allocated to the generatorsi These variations are a measure of the avoided or added costs related to losses. In the allocation process, made in a branch basis, both real and reactive powers are considered. The methodology presented in this paper can be used to evaluate embedded generation incentives or to design tariffs for the use of the distribution network.

Abstract
This paper demonstrates the application of Markov chain models to valuate generation assets within deregulated electricity markets. A new framework for modeling electricity markets with Markov chain model is proposed. The advantage of the Markov chain model is that it deploys fundamental approaches to identify the key economic forces underlying the electricity markets such as demand on electricity and supplied online generation capacity. Based on this new model, real option calculations are used to valuate generation assets. Markov chain model is combined with 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 capability to analyze the impacts of demand growth patterns, competition strategies of competitors and other key economic forces.

Abstract
This paper presents an enhanced version of an AC Fuzzy Power Flow model designed to integrate correlation data between nodal injections. The model gives the user the possibility to specify fuzzy numbers to represent the possible behavior of loads and generations and outputs fuzzy membership functions for voltage magnitudes and phases, active and reactive flows, losses and generations. The algorithm is organized in two basic steps. The first one corresponds to a linearized procedure while the second aims at introducing correlated data leading to a reduction of the width of the membership output functions. In a final section, we present results obtained with a case study based on a didactic power system to illustrate and highlight details of the proposed models.