Abstract
This paper describes a Load Allocation model to be used in a DMS environment. A process of rough allocation is initiated, based on information on actual measurements and on data about installed capacity and power and energy consumption at LV substations. This process generates a fuzzy load allocation, which is then corrected by a fuzzy state estimator procedure in order to generate a crisp power flow compatible set of load allocations, coherent with available real time measurements recorded in the SCADA.

Abstract
This paper presents a Monte-Carlo algorithm considering loads defined by fuzzy numbers. In this methodology states are sampled according to the probabilistic models governing the life cycle of system components while fuzzy concepts are used to model uncertainty related to future load behavior. This model can be used to evaluate generation/transmission power system reliability for long term planning studies as one uses the more adequate uncertainty models for each type of data. For each sampled state a Fuzzy Optimal Power Flow is run so that one builds its power not supplied membership function. The paper proposes new indices reflecting the integration of probabilistic models and fuzzy concepts and discusses the application of variance reduction techniques if loads are defined by fuzzy numbers. A case-study based on the IEEE 30 bus system illustrates this methodology.

Abstract
In this paper, system component reinforcements are analyzed from the perspective of their impact in increasing flexibility in system design. The proposed framework integrates a fuzzy optimal power flow model through which one can derive, as a function of load uncertainties, possibility distributions for generation, power flows and power not supplied. Exposure and robustness indices, based on risk analysis concepts, are defined. These indices can be used to rank the expansion alternatives, giving the planner insight to system behavior in face of adverse futures. Their use in conjunction with investment assessments is proposed as a necessary step in a decision making methodology.

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.