Abstract
Electric drive vehicle technologies are being considered as possible solutions to mitigate environmental problems and fossil fuels dependence. Several studies have used life cycle analysis technique, to assess energy use and CO2 emissions, addressing fuels Well-to-Wheel life cycle or vehicle's materials Cradle-to-Grave. However, none has considered the required infrastructures for fuel supply. This study presents a methodology to evaluate energy use and CO2 emissions from construction, maintenance and decommissioning of support infrastructures for electricity and fossil fuel supply of vehicles applied to Portugal case study. Using Global Warming Potential and Cumulative Energy Demand, three light-duty vehicle technologies were considered: Gasoline, Diesel and Electric. For fossil fuels, the extraction well, platform, refinery and refuelling stations were considered. For the Electric Vehicle, the Portuguese 2010 electric mix, grid and the foreseen charging point's network were studied. Obtained values were 0.6-1.5 gCO(2eq)/km and 0.03-0.07 MJ(eq)/km for gasoline, 0.6-1.6 gCO(2eq)/km and 0.02-0.06 MJ(eq)/km for diesel, 3.7-8.5 gCO(2eq)/km and 0.06-0.17 MJ(eq)/km for EV. Monte Carlo technique was used for uncertainty analysis. We concluded that EV supply infrastructures are more carbon and energetic intensive. Contribution in overall vehicle LCA does not exceed 8%.

Abstract
Hydrogen and electric vehicle technologies are being considered as possible solutions to mitigate environmental burdens and fossil fuel dependency. Life cycle analysis (LCA) of energy use and emissions has been used with alternative vehicle technologies to assess the Well-to-Wheel (WTW) fuel cycle or the Cradle-to-Grave (CTG) cycle of a vehicle's materials. Fuel infrastructures, however, have thus far been neglected. This study presents an approach to evaluate energy use and CO2 emissions associated with the construction, maintenance and decommissioning of energy supply infrastructures using the Portuguese transportation system as a case study. Five light-duty vehicle technologies are considered: conventional gasoline and diesel (ICE), pure electric (EV), fuel cell hybrid (FCHEV) and fuel cell plug-in hybrid (FC-PHEV). With regard to hydrogen supply, two pathways are analysed: centralised steam methane reforming (SMR) and on-site electrolysis conversion. Fast, normal and home options are considered for electric chargers. We conclude that energy supply infrastructures for FC vehicles are the most intensive with 0.03-0.53 MJ(eq)/MJ emitting 0.7-27.3 g CO2eq/MJ of final fuel. While fossil fuel infrastructures may be considered negligible (presenting values below 2.5%), alternative technologies are not negligible when their overall LCA contribution is considered. EV and FCHEV using electrolysis report the highest infrastructure impact from emissions with approximately 8.4% and 8.3%, respectively. Overall contributions including uncertainty do not go beyond 12%. Copyright

Abstract
A probabilistic forecast, in contrast to a point forecast, provides to the end-user more and valuable information for decision-making problems such as wind power bidding into the electricity market or setting adequate operating reserve levels in the power system. One important requirement is to have flexible representations of wind power forecast (WPF) uncertainty, in order to facilitate their inclusion in several problems. This paper reports results of using the quantile-copula conditional Kernel density estimator in the WPF problem, and how to select the adequate kernels for modeling the different variables of the problem. The method was compared with splines quantile regression for a real wind farm located in the U.S. Midwest. © 2011 IEEE.

Abstract
In this paper we discuss how probabilistic wind power forecasts can serve as an important tool to efficiently address wind power uncertainty in power system operations. We compare different probabilistic forecasting and scenario reduction methods, and test the resulting forecasts on a stochastic unit commitment model. The results are compared to deterministic unit commitment, where dynamic operating reserve requirements can also be derived from the probabilistic forecasts. In both cases, the use of probabilistic forecasts contributes to improve the system performance in terms of cost and reliability. © 2011 IEEE.

Abstract
In this paper we discuss the use of wind power forecasting in electricity market operations. In particular, we demonstrate how probabilistic forecasts can contribute to address the uncertainty and variability in wind power. We focus on efficient use of forecasts in the unit commitment problem and discuss potential implications for electricity market operations.

Abstract
This paper reports a study on the importance of the training criteria for wind power forecasting and calls into question the generally assumed neutrality of the 'goodness' of particular forecasts. The study, focused on the Spanish Electricity Market as a representative example, combines different training criteria and different users of the forecasts to compare them in terms of the benefits obtained. In addition to more classical criteria, an information theoretic learning training criterion, called parametric correntropy, is introduced as a means to correct problems detected in other criteria and achieve more satisfactory compromises among conflicting criteria, namely forecasting value and quality. We show that the interests of wind farm owners may lead to a preference for biased forecasts, which may be in conflict with the larger needs of secure operating policies. The ideas and conclusions are supported by results from three real wind farms. Copyright (c) 2010 John Wiley & Sons, Ltd.