Abstract
This paper presents an approach to reliability evaluation of generation/transmission power systems using the Monte Carlo simulation method. In this approach uncertainties are represented using two different frameworks. For one side, probability concepts are used to represent the failure-repair cycle of system components. Loads are represented through classified load diagrams in which each time step corresponds to a fuzzy number. This simulation method allows the user to obtain estimates of the expected exposure and robustness indices and of the Power Not Supplied membership function.

Abstract

Abstract
The deregulation of the electricity industry places a number of complex questions related namely with planning and operating activities. In this paper one will enumerate some of these issues and give an overview about the way this process is being handled in several countries. In the paper one will also describe a methodology to identify the most adequate components to be reinforced considering that one aims at reducing the risk felt by the planner if loads are modeled by fuzzy numbers. At the end, fuzzy concepts are integrated in a model to validate from a technical point of view a set of trades established between electricity buyers and sellers in a competitive environment.

Abstract
In this paper one reviews some concepts related to fuzzy load modeling namely dealing with correlated and uncorrelated models. After briefly describing DC and AC fuzzy power flow models, one addresses the problem of integrating information about correlations between node consumptions or injections in these models. This information will lead to a correction phase performed after running the previous models. This correction phase is organized as a linear programming problem solved in a very efficient way as one takes advantage of the special structure of the problem. The methodology will be illustrated using a case study based on the IEEE 24 bus/38 branch Test System.

Abstract
This paper presents an efficient information debugging methodology, using a dynamic state estimation algorithm which, through the use of a dynamic model for the time evolution of the system state, will meet simultaneously two objectives: the filtering of the incoming data and the forecasting of the state vector one step ahead. Based on this last characteristic a scheme for the detection of suspicious information is built.

Abstract
This paper presents an efficient information debugging methodology, using a dynamic state estimation algorithm which, through the use of a dynamic model for the time evolution of the system state, will meets simultaneously two objectives: the filtering of the incoming data and the forecasting of the state vector one step ahead. Based on this last characteristic a scheme for the detection of suspicious information is built.