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

Abstract
This paper presents an analysis of the impacts of photovoltaics and storage units for self-consumption in the day-ahead spot prices. A methodology is proposed, to access these impacts in the Iberian electricity market for 2015, 2020 and 2030.

Abstract
As Distributed Generation (DG) is increasingly integrated into the networks, intermittency issues of some types of DG (such as wind energy) will potentially increase network operating costs or decrease network security. Concern regarding these effects has grown among Network Operators and in order to avoid them, new or updated wind parks are required to provide ancillary services to the network. These ancillary services include the fault ride-through capability and mainly those wind parks connected to a collector switching or distribution substation are asked to provide this service. Although some wind parks can already supply fault ridethrough capability to the network, most of them are still largely unable to do so due to the current DG protection scheme. This work concentrates on the development and assessment of new settings for the wind park protection scheme by closed-loop test in real time. This aims to allow wind parks to provide fault ride-through capability to the distribution network. A distribution network with a DFIG based wind turbine with ride-through-fault capability was modelled on the RTDS Simulator. The protective relay settings were then implemented into a commercial numerical programmable relay to be assessed by closed-loop real time test. Conclusions of the protective relay with the fault ridethrough requirement are presented and discussed.

Abstract
This paper extends the symmetric/constrained fuzzy power flow models by including the potential correlations between nodal injections. Therefore, the extension of the model allows the specification of fuzzy generation and load values and of potential correlations between nodal injections. The enhanced version of the symmetric/constrained fuzzy power flow model is applied to the 30-bus IEEE test system. The results prove the importance of the inclusion of data correlations in the analysis of transmission system adequacy. Copyright (c) 2015 John Wiley & Sons, Ltd.