Abstract
The continuously increasing complexity and interdependence of electric power systems make their monitoring, security analysis and control increasingly important and difficult. Although, an on-line load flow analysis can give insight in the time evolution of the system state, it has been shown that the conventional load flow is not suitable for real time monitoring of the power system. As a result the State Estimation (SE) is nowadays considered as the heart of the modern control centers. To circumvent the numerical problems inherent to the Kalman filter algorithm we present in this paper an algorithm for power system state estimation based in the square root filtering technique. The estimator proposed is decoupled in nature due to modifications made in the measurement equations resulting in a new observation model.

Abstract
The proper analysis and successful operation of a power system implies a reliable estimate of its state. As a result the State Estimation (SE) is nowadays considered as the heart of the modern control centers. Several dynamic state estimation algorithms based on the extended Kalman filtering theory (EKF) have been proposed in the literature but as it has been reported numerical problems may arise in its implementation in practice. To circumvent the problems inherent to the Kalman filter algorithm we present in this paper an algorithm for power system state estimation based in the square root filtering technique. The estimator proposed is decoupled in nature due to modifications made in the measurement equations resulting in a new observation model.

Abstract
In this paper one proposes that the concept of risk should be central in a distribution planning methodology, and that the procedures and models adopted should be used to take that factor in account and assess the robustness and exposure of the solution studied. It is also argued that this purpose is best served if a multicriteria approached is adopt as much as possible, keeping explicit record of the values of the serval attributes that may classify the goodness of a solution.

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
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
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.