Abstract

Abstract
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.

Abstract
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.

Abstract
This paper presents a new Fuzzy Dynamic Programming model that calculates the optimum solution of problems with uncertainties in data defined by fuzzy sets. The result includes the determination of an Intrinsic Risk Threshold of the solution. Extrinsic Risk Thresholds may also be set by a Decision Maker, in order to obtain more robust solutions. The technique is applied to the calculation of Distribution System expansion costs to serve the objectives of a Regulatory Authority (Regulator) in fixing levels of efficiency, targets and penalties to a regulated market.

Abstract
In this paper a model to derive reinforcement strategies driven by an economic criterion is presented. This model is based on a Fuzzy Optimal Power Flow formulation which assumes that loads are characterized by membership functions in the scope of the Fuzzy Set Theory. Therefore, subjective information or incompletely defined data can be included in the planning studies making it possible to characterize in a more adequate way the system behavior regarding the uncertainties of the future. Some risk concepts are also presented and integrated in this planning framework. The planner can thus identify the least costly reinforcement strategy in order to meet to desired risk index target so that a reduction of the system exposure towards the future is obtained.

Abstract
In this paper an improved DC Fuzzy Optimal Power Flow - FOPF - model for planning purposes formulated as a multi-parametric programming problem is briefly presented. This model uses Fuzzy Set Theory concepts to represent information about loads expressed in a subjective way by experts or integrating a certain degree of uncertainty about the future. The proposed algorithm has an heuristic nature so that it is important to evaluate the quality of the derived membership functions. A sampling procedure will be used to build membership functions to be compared with the ones obtained using the FOPF algorithm. In the paper results obtained for two networks based on the IEEE 24 and 30 bus test systems will be presented and discussed.