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.

Abstract
The secure integration of renewable generation into modern power systems requires an appropriate assessment of the security of the system in real-time. The uncertainty associated with renewable power makes it impossible to tackle this problem via a brute-force approach, i.e. it is not possible to run detailed online static or dynamic simulations for all possible security problems and realizations of load and renewable power. Intelligent approaches for online security assessment with forecast uncertainty modeling are being sought to better handle contingency events. This paper reports the platform developed within the iTesla project for online static and dynamic security assessment. This innovative and open-source computational platform is composed of several modules such as detailed static and dynamic simulation, machine learning, forecast uncertainty representation and optimization tools to not only filter contingencies but also to provide the best control actions to avoid possible unsecure situations. Based on High Performance Computing (IIPC), the iTesla platform was tested in the French network for a specific security problem: overload of transmission circuits. The results obtained show that forecast uncertainty representation is of the utmost importance, since from apparently secure forecast network states, it is possible to obtain unsecure situations that need to be tackled in advance by the system operator.

Abstract
This paper presents the approach followed under project SENSIBLE to prove, in field-test scenarios, the benefits of integrating and coordinating small-scale storage devices to: (i) reduce the impact of Distributed Renewable Energy Sources in the Low Voltage grid and (ii) support the transition and the operation in islanding mode in the demonstration grid. The functional and ICT architecture developed for the Portuguese Demonstrator of Évora is presented, focusing in the use cases defined to test and validate the tools developed to enable the active management of the LV grid during both normal and islanded modes.

Abstract
Several countries have a significant installed capacity of large-scale reversible hydro power plants. This large-scale storage technology comes with high investments costs, hence the constant search for methods to increase and diversify the sources of revenue. Traditional fixed speed pump storage units typically operate in the day-ahead market to perform price arbitrage and, in specific cases, provide downward replacement reserve (RR). Variable speed pump storage can not only participate in RR but also contribute to frequency restoration reserve (FRR), given their ability to control its operating point in pumping mode. This work proposes a strategy to manage the water resource and maximize the power plant revenue by participating in the day ahead market but also providing ancillary services. Moreover, a model to correctly allocate the water resource throughout the year is presented, as well as an evaluation module to calculate the real revenue of the system.