Abstract
The strong growth that is felt at the level of photovoltaic (PV) power generation craves for more sophisticated and accurate forecasting methods that could be able to support its proper integration into the energy distribution network. Through the combination of the vector autoregression model (VAR) with the least absolute shrinkage and selection operator (LASSO) framework, a set of sparse VAR structures can be obtained in order to capture the dynamic of the underlying system. The robust and efficient alternating direction method of multipliers (ADMM), well known for its great ability dealing with high-dimensional data (scalability and fast convergence), is applied to fit the resulting LASSO-VAR variants. This spatial-temporal forecasting methodology has been tested, using 1-hour and 15-minutes resolution, for 44 microgeneration units time-series located in a city in Portugal. A comparison with the conventional autoregressive (AR) model is performed leading to an improvement up to 11%.

Abstract
It is in the nature of chaotic atmospheric processes that weather forecasts will never be perfectly accurate. This natural fact poses challenges not only for private life, public safety, and traffic but also for electrical power systems with high shares of weather-dependent wind and solar power production. © 2012 IEEE.

Abstract
In the last two decades, renewable energy forecasting progressed toward the development of advanced physical and statistical algorithms aiming at improving point and probabilistic forecast skill. This paper describes a forecasting framework to explore information from a grid of numerical weather predictions (NWP) applied to both wind and solar energy. The methodology combines the gradient boosting trees algorithm with feature engineering techniques that extract the maximum information from the NWP grid. Compared to a model that only considers one NWP point for a specific location, the results show an average point forecast improvement (in terms of mean absolute error) of 16.09% and 12.85% for solar and wind power, respectively. The probabilistic forecast improvement, in terms of continuous ranked probabilistic score, was 13.11% and 12.06%, respectively.

Abstract
The technological revolution in the electric power system sector is producing large volumes of data with pertinent impact in the business and functional processes of system operators, generation companies, and grid users. Big data techniques can be applied to state estimation, forecasting, and control problems, as well as to support the participation of market agents in the electricity market. This chapter presents a revision of the application of data mining techniques to these problems. Trends like feature extraction/reduction and distributed learning are identified and discussed. The knowledge extracted from power system and market data has a significant impact in key performance indicators, like operational efficiency (e.g., operating expenses), investment deferral, and quality of supply. Furthermore, business models related to big data processing and mining are emerging and boosting new energy services.

Abstract
The voltage stability security is a very important and relevant issue in the modern power networks. The voltage instability can drive the system to a blackout. One of the methodologies to deal with this problem is analysing the trajectory sensitivity of the system. The STATCOM is an important hardware that is used to improve the voltage stability of an electric power network. The main objective of this paper is to analyse the results of different studies that were realized in a power system with wind farms. The wind farms were modelled considering that the wind turbines were equipped with pitch control coupled with a Permanent Magnet Synchronous Generator. The EUROSTAG program was used to obtain the simulation results. The Matlab software package was used for the post-processing module developed.

Abstract
The application of technologies for the improvement of voltage stability, particularly during contingencies is a major concern in the field of power networks operation and control. In this paper it is studied the FACTS performance in the dynamic voltage stability of an electric power system. It was considered the influence of two FACTS controllers in parallel or shunt connection, the Static VAR Compensators (SVC) and the Static Synchronous Compensators (STATCOM) in the dynamic voltage stability during a disturbance of the BPA test power network. The models of SVC and STATCOM were developed in EUROSTAG. The Automatic Voltage Regulators (AVR) of the generating units and the turbine speed governors were modelled in detail. Different load models were used and the Under Load Tap Changers (ULTC) were also taken into account. Finally, some conclusions that provide a better understanding of the dynamic voltage stability using FACTS devices during a disturbance are pointed out.