Abstract
Wind power probabilistic forecast is being used as input in several decision-making problems, such as stochastic unit commitment, operating reserve setting and electricity market bidding. This work introduces a new on-line quantile regression model based on the Reproducing Kernel Hilbert Space (RKHS) framework. Its application to the field of wind power forecasting involves a discussion on the choice of the bias term of the quantile models, and the consideration of the operational framework in order to mimic real conditions. Benchmark against linear and splines quantile regression models was performed for a real case study during a 18 months period. Model parameter selection was based on k-fold cross-validation. Results showed a noticeable improvement in terms of calibration, a key criterion for the wind power industry. Modest improvements in terms of Continuous Ranked Probability Score (CRPS) were also observed for prediction horizons between 6 and 20 h ahead.

Abstract
With the liberalization of the electricity markets, price forecasting has become crucial for the decision-making process of market agents. The unique features of electricity price, such as non-stationary, non-linearity and high volatility make this a very difficult task. For this reason, rather than a simple point forecast, market participants are more interested in a probabilistic forecast that is essential to estimate the uncertainty involved in the price. By focusing on this issue, the aim of this paper is to analyze the impact of external factors in the electricity price and present a methodology for probabilistic forecasting of day-ahead electricity prices from the Iberian electricity market. The models are built using regression techniques and aim to obtain, for each hour, the quantiles of 5% to 95% by steps of 5%.

Abstract
This paper aims to discuss both the ICT and grid architectures of the Évora Demonstrator under the project SENSIBLE. The demonstrator is focused on testing grid management functions under normal and emergency operation in a rural low voltage grid, taking advantage of electrochemical, electromechanical and thermal storage technologies as well as renewable energy sources (photovoltaics) that will be deployed at both distribution grid and at clients' electrical installation. In addition, the community engagement strategy is presented since it is crucial for the full implementation of the project.

Abstract
Wind power probabilistic forecast is a key input in decision-making problems under risk, such as stochastic unit commitment, operating reserve setting and electricity market bidding. While the majority of the probabilistic forecasting methods are based on quantile regression, the associated limitations call for new approaches. This paper described a new quantile regression model based on the Reproducing Kernel Hilbert Space (RKHS) framework. In particular, two versions of the model, off-line and on-line, were implemented and tested for a real wind farm. Results showed the superiority of the on-line approach in terms of performance, robustness and computational cost. Additionally, it was observed that, in the presence of correlated data, the optimal on-line learning may cause unreliable modelling. Potential solutions to this effect are also described and implemented in the paper.

Abstract
Multi-temporal decision-making problems require information about the potential temporal trajectories of wind generation for a given time horizon. Typically, the Gaussian copula is used for modelling the dependency between probabilistic forecasts from different lead-times. This paper explores the vine copula framework as a benchmark model since it captures complex multivariate dependence structures with mixed types of dependencies. The results show that a Gaussian copula with a suitable covariance matrix suffice to generate high quality temporal trajectories.

Abstract
Presently, in several countries, the wind and solar power capacity connected to the distribution network is increasing steadily. In parallel, the investment in smart grid technologies is growing, which will support the integration of renewable energy and represents an opportunity to develop advanced management functions. Forecasting is a key input for several management functions of distribution system operators (DSOs). This chapter describes modeling approaches allowing to take advantage of the wealth of power measurements available in near real-time in a smart grid context [e.g., smart meters and SCADA (supervisory control and data acquisition) system], in order to improve the quality of renewable energy forecasting in a computationally efficient manner. As a basis, the power generation vector is to be considered as a multivariate one, that is, by simultaneously focusing on all sites of interest, instead of trying to build and estimate models at every location, individually. Two real-world test cases with wind and solar generation are used to show the improvement in accuracy from exploring distributed information in a probabilistic forecasting framework. © 2016 John Wiley & Sons, Ltd. All rights reserved.