Abstract
This paper presents an enhanced version of an AC Fuzzy Power Flow model designed to integrate correlation data between nodal injections. The model gives the user the possibility to specify fuzzy numbers to represent the possible behavior of loads and generations and outputs fuzzy membership functions for voltage magnitudes and phases, active and reactive flows, losses and generations. The algorithm is organized in two basic steps. The first one corresponds to a linearized procedure while the second aims at introducing correlated data leading to a reduction of the width of the membership output functions. In a final section, we present results obtained with a case study based on a didactic power system to illustrate and highlight details of the proposed models.

Abstract
In this paper we describe two approaches developed by two research teams to address the topology identification problem in the scope of state estimation. Both approaches aim at enlarging the traditional concept of strict state estimation, assuming that the network topology is pre-determined and is fixed. In fact, we are generalizing state estimation, enlarging its domain and aiming at obtaining topology information from a state estimation run. Apart from the description of those two techniques, the paper includes a'set of tests performed over the same test system in order to illustrate the interest of the approaches and to evaluate their performances. © 2001 IEEE.

Abstract
A fuzzy model for power system operation is presented. Uncertainties in loads and generations are modeled 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 in which the problem nature and structure allow 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
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
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
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.