Abstract
The LV SCADA project aimed at the development of advanced technical, commercial and regulatory solutions to contribute for an effective smart grid implementation. One of the biggest challenges of the project was related with the lack of characterization that usually exists in LV networks, together with the almost non-existing observability. In order to overcome these issues, a LV management system integrating a state estimation tool based on artificial intelligence techniques was developed. The tool is currently installed in one pilot demonstration site that aggregates 2 MV/LV substations. In this paper the performance of tool in real environment is evaluated and the results gathered from the pilot site are analyzed.

Abstract
To enable the use of smart metering historical information of energy measurements in real time network operation, in this paper is proposed the generation of pseudo-measurements, which can be combined with real-time SCADA measurements and feed an online state estimation procedure. Hence increasing the network operator's situational awareness. The goal is to obtain a better representation of the network operation points, voltage values, than the one that is possible to obtain with the direct use of smart metering data, which is based on average values, by increasing the amount of available real time data points.

Abstract
In this paper, we present a Simulator Program that identifies the topologies and supports operation staff in decision-electrical power system operation consider load reestablishment procedures and topological possibilities to each event, offering a selection of the best settings. Facing a electrical power transmission system contingency, the Simulator considers the current state and uses special algorithms that detect the grid topology, interprets results and presents to the operators a rank of procedures with their respective degrees of reliability. For these actions the software Simulator make the circuits breakers states analysis and electric keys, load flow solution with the on line data, makes risk prediction and the mathematical analysis of remote sensing values. The software Simulator uses in some of his actions, special algorithms that are based on Paraconsistent Annotated Logic (PAL), which is a non-classical logic whose main property is to accept contradiction in their fundamentals. These algorithms based on PAL offer greater speed of processing and allows the Paraconsistent Simulator of topologies (ParaSimTop) to be implemented in real time.

Abstract
This paper presents the development of a complete methodology for power systems scheduling with highly variable sources based on a risk assessment model. The methodology is tested in a real case study, namely an island with high penetration of renewable energy production. The uncertainty of renewable power production forecasts and load demand are defined by the probability distribution function, which can be a good alternative to the scenarios approach. The production mix chosen for each hour results from the costs associated to the operation risks, such as load shed and renewable production curtailment. The results to a seven days case study allow concluding about the difficulty to achieve a complete robust solution.

Abstract
This paper analyzes a real case study based on an islanding power grid, where there is wind power curtailment during the grid operation. This curtailment skews the wind power production database creating a huge challenge to the overall power production forecast. Thus, it is presented a solution which has allowed more accurate forecasts in order to improve the renewable production and reduce the fuel consumption in thermal power plants. The proposed filtering approach demonstrated to be a good solution allowing wind power forecasts with less error and net load forecasts with more accuracy.

Abstract
This paper presents novel intraday session models for price forecasts (ISMPF models) for hourly price forecasting in the six intraday sessions of the Iberian electricity market (MIBEL) and the analysis of mean absolute percentage errors (MAPEs) obtained with suitable combinations of their input variables in order to find the best ISMPF models. Comparisons of errors from different ISMPF models identified the most important variables for forecasting purposes. Similar analyses were applied to determine the best daily session models for price forecasts (DSMPF models) for the day- ahead price forecasting in the daily session of the MIBEL, considering as input variables extensive hourly time series records of recent prices, power demands and power generations in the previous day, forecasts of demand, wind power generation and weather for the day- ahead, and chronological variables. ISMPF models include the input variables of DSMPF models as well as the daily session prices and prices of preceding intraday sessions. The best ISMPF models achieved lower MAPEs for most of the intraday sessions compared to the error of the best DSMPF model; furthermore, such DSMPF error was very close to the lowest limit error for the daily session. The best ISMPF models can be useful for MIBEL agents of the electricity intraday market and the electric energy industry.