Abstract
In this paper, a fuzzy model for power system operation is presented. Uncertainties in loads and generations are modelled as fuzzy numbers. System behavior under known (while uncertain) injections is dealt with by a DC fuzzy power flow model. System optimal (while uncertain) operation is calculated with linear programming procedures where the problem nature and structure allows some efficient techniques such as Dantzig Wolfe decomposition and dual simplex to be used. Among the results, one obtains a fuzzy cost value for system operation and possibility distributions for branch power flows and power generations. Some risk analysis is possible, as system robustness and exposure indices can be derived and hedging policies can be investigated.

Abstract
The power flow is the fundamental tool for the study of power systems. The data for this problem are subject to uncertainty. This paper uses interval arithmetic to solve the power flow problem. Interval arithmetic takes into consideration the uncertainty of the nodal information, and is able to provide strict bounds for the solutions to the problem: all possible solutions are included within the bounds given by interval arithmetic. Results are compared with those obtainable by Monte Carlo simulations and by the use of stochastic power flows. Object oriented programming techniques make it possible to use interval arithmetic with minimal modifications to existing software. However, to reduce the conservatism inherent in all interval curtest computations, the paper describes an iterative method used to obtain the "hull" of the solution set.

Abstract
A new type of bus for load flow analysis is proposed in this paper; this type of bus, named PX, is particularly suited for modelling elements like static compensators or asynchronous generators. The inclusion of this PX bus in the Fast Decoupled Load Flow method is presented and discussed. Numerical results are reported when applying this approach to solve load flow problems in two standard systems.

Abstract
A fuzzy AC load flow model is presented in which fuzzy data are used to obtain possibility distributions of voltages, active and reactive flows and losses, currents, and generated powers. These distributions are compared with the ones obtained through a Monte Carlo based simulation in order to evaluate the errors inherent to the fuzzy AC load flow.

Abstract
Operation problems that result from MV distribution networks with local asynchronous generation sources connected are examined. In order to analyze steady-state operating situations, a load-flow calculation must be performed. The presence of asynchronous generators imposes the adoption of a consistent bus modelization using a new bus type, the PX bus, where P is the active generated power and X is a nonlinear magnetizing reactance. This approach introduces some modifications in the conventional load-flow algorithms, thereby increasing the total number of iterations.

Abstract
An online recursive algorithm for power system state estimation is presented which is based on the use of the Kalman filtering theory. The recursive form of the algorithm is advantageous in two ways: it permits the inversion of smaller-dimensioned matrices, which results in simpler algorithms, and it provides the opportunity to obtain a partially updated estimate in case the computer is heavily loaded in a given period and there is not enough time to perform a single batch processing of the measurement vector as a whole.