Abstract
The basic function of an electric power system is to satisfy the customer energy requirements as economically as possible and at an adequate level of continuity and quality. Reliability calculations such as those described in this paper can be used with advantage in establishing additional security constraints, that if used in the right context and backed up by the inevitable economic analysis, provide better and more consistent information for system planning or expansion studies. The model described can also be used when the simulation of the actual operating conditions is necessary.

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Planning a radial distribution network requires, among several options, decisions concerning whether or not to include switching devices in the network branches. The general problem can be stated as follows: given a radial network, define the optimal system configuration (location and type of switching devices) by minimising an objective function which includes the cost associated with the switching devices and the economic consequences of the power disconnected and energy not supplied due to failures in the distribution system. In this paper a new algorithm and an efficient computer program are presented. Reliability concepts are used along with dynamic programming techniques in order to establish an objective function and obtain a desired optimised network configuration.

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Metaheuristics Algorithms are widely recognized as one of most practical approaches for Combinatorial Optimization Problems. This paper presents a comparison between two metaheuristics to solve a problem of Phase Balancing in Low Voltage Electric Distribution Systems. Among the most representative mono-objective metaheuristics, was selected Simulated Annealing, to compare with a different metaheuristic approach: Evolutionary Particle Swarm Optimization. In this work, both of them are extended to fuzzy domain to modeling a multi-objective optimization, by mean of a fuzzy fitness function. A simulation on a real system is presented, and advantages of Swarm approach are evidenced.