Abstract
This paper presents a generic framework to evaluate and compare the quality of the uncertainties provided by probabilistic forecasts of power system state when used to perform security assessment for branch overloads. Besides exploiting advanced univariate and multivariate metrics that are traditionally used in weather prediction, the evaluation is complemented by assessing the benefits from exploiting probabilistic forecasts over the current practices of using deterministic forecasts of the system operating conditions. Another important feature of this framework is the provision of parameters tuning when applying flow probabilistic forecasts to perform security assessment for branch overloads. The quality and scalability of this framework is demonstrated and validated on recent historical data of the French transmission system. Although being developed to address branch overload problems, with proper adaptations, this work can be extended to other power system security problems.

Abstract
This work consists in assessing a real islanded power system from a dynamic security point of view, to support the planned installation, in the near future, of a hydro power plant with pumped storage, aiming to increase the integration of renewable energy. The analysed hydro power plant will include a Pelton turbine as it is a high-head hydro facility. Due to economic reasons, the adopted water pumping technology consists in fixed speed pumps coupled to induction motors with direct grid connection. It was possible to verify through detailed simulations of this power system's time domain behaviour that, even though the expected installation of this new power plant will bring additional frequency stability constraints, a robust technical solution may be found dealing with the new constraints without increasing the complexity of operation in this islanded power system. The conclusions obtained from this specific case are also valid for similar isolated power systems, namely when hydro pumping stations are being considered to increase time-variable renewable generation penetration.

Abstract
In this paper, a validation framework is proposed to evaluate the quality of uncertainty forecasts, when used to perform branch flow security assessment. The consistency between probabilistic forecasts and observations and the sharpness of the uncertainty forecasts is verified with advanced metrics widely used in weather prediction. The evaluation is completed by assessing the added value of exploiting uncertainty forecasts over the TSO current practices of using deterministic forecasts. For electric power industry, this proposed validation framework provides a way to compare the performance among alternative uncertainty models, when used to perform security assessment in power systems. The quality of the proposed metrics is illustrated and validated on historical data of the French transmission system.

Abstract
This paper presents a methodology to make EV able to contribute to secondary frequency regulation, by participating in Automatic Generation Control (AGC). The aggregator entity is used in this methodology to act as intermediate between the AGC and the EV controllers. The case-study used is an approximation of the Portuguese transmission/generation network, including existing tie lines with Spain, to which a solid three-phase short circuit was applied. After the disturbance, the impact of EV in the AGC operation was studied for two different situations: EV contributing to secondary frequency regulation and EV behaving as uncontrollable loads. The results obtained for both situations were then compared, in terms of interconnection active power flows in the existing tie-lines and its re-establishment time. ©2010 IEEE.

Abstract
This paper presents a new approach to perform on-line dynamic security assessment and monitoring of electric power systems exploiting a statistical hybrid learning technique - the Kernel Regression Trees This technique, besides producing fast;security classification, can still quantify, hi real-time, the security degree of the system, by emulating continuos security indices that translate the power system dynamic behavior. Moreover it can provide interpretable security structures. The feasibility of this approach was demonstrated in the dynamic security assessment of isolated systems with large amounts of wind power production, like In the Crete island electric network (Greece) Comparative results regarding performances of Decision Trees and Neural Networks are also presented and discussed. From the obtained results, the proposed approach showed to provide good predicting structures whose performance stands up to the performance of the two other existent methods.

Abstract
This paper presents the performed steps to design an Artificial Neural Network (ANN) tool, able to evaluate, within the framework of on-line security assessment, the dynamic security of interconnected power systems having an increased penetration of wind power production. This approach exploits functional knowledge generated off-line, the Linear Regression (LR) variable selection stepwise method to perform automatic Feature Subset Selection (FSS) and ANN to provide a way for fast evaluation of the system security degree. In order to choose the best input/output set of variables for the ANN tool, a comparative analysis is performed, regarding the obtained predicting error, by performing a statistical hypothesis test. The reduced error results confirm the feasibility and quality of the derived security structures.