Abstract
Most optimization and decision problems in power systems include integer or binary variables, leading to combinatorial problems. In this paper, several approaches using metaheuristics and genetic algorithms are presented that deal with real problems of the power industry Most of these methodologies are now implemented in distribution management systems (DMS) used by several utilities.

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This paper reports the building of a system for predicting load curves at Low Voltage Substations supplying a set of consumers of different types (residential, industrial, etc.). The system is built by the aggregation of individual Fuzzy Inference Systems of the Takagi-Sugeno type. The paper describes how actual measurements formed a base of raw data and how test and training sets could be built from this base. Results produced by the prediction system and their comparison with actual load curves confirm the good performance of the model.

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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.

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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.