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
This paper describes a genetic algorithm approach to the optimal multistage planning of distribution networks. The authors describe a mathematical and algorithmic model that they have developed and experimented with success. The paper also presents application examples, with real size systems. The advantages of adopting this new approach are discussed in the planning context, namely in conjunction with the adoption of multicriteria decision making methods.

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
In the present paper an advanced control system for the optimal operation and management of medium size power systems with large penetration from renewable power sources is presented. This control system is aimed to assist the power system operators in their managing task, by proposing them optimal actions for the economic and secure management of the power system. The control system is composed of various modules performing functions like short-term forecasting of the load and of the renewable sources, economic dispatch of the power units and on-line dynamic security assessment. The pilot control system is being installed and evaluated in the Greek island of Lemnos.

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.