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.

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.

Abstract

Abstract