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
Energy policies in the European Union (EU) and its 27 member states respond to three main concerns namely energy security, economic development, and environmental sustainability. All the three "Es'' are pursued simultaneously with some slight differences in emphasizing the mutual importance of these, in particular the cost factors. The legislation of the EU (e. g., ETS-Emission Trading Scheme, directives) increasingly guides the member states' energy policies. However, energy policy directions are still made domestically, for example, on the support on renewable energy technologies. In this work, we look into distributed generation (DG), since it has been grown considerable in the past few years and can be used to partially fulfill renewable energy targets. The policy makers have to make decisions about regulation directives, more specifically they have to change the current regulation in order to incentive the increase in DG. However, these decisions have not only economic impacts but also technical impacts that must be accounted for. In this regard, a decision aid tool would help the policy makers in estimating producer economic impacts, as well as power network technical impacts, of various possible regulation directives. Here, we propose an interactive decision aid tool that models the aforementioned impacts and thus, can be used by policy makers to experiment with different regulation directives before deciding on the ones to set.

Abstract
This paper proposes a new methodology for dynamic security assessment and preventive control. In the first phase, an artificial neural network (ANN) is trained to provide the security status. ANN inputs are settled by a feature selection approach that takes into account the requisites of the control algorithm, to be applied in the second phase. The adaptive control methodology is based on the steepest descent method, where the usual explicit math functions to be dealt with are emulated by the trained ANN. To illustrate the developed approach, the methodology was applied to the control of dynamic security of Madeira island power system. Results attained so far show that the proposed approach was able to find the optimal control actions.

Abstract
This paper proposes a methodology to consider the effects of the integration of DG on planning. Since DG has potential to defer investments in networks, the impact of DG on grid capacity is evaluated. A multi-objective optimization tool based on the meta-heuristic MEPSO is used, supporting an alternative approach to exploiting the Pareto front features. Tests were performed in distinct conditions with two well-known distribution networks: IEEE-34 and IEEE-123. The results combined minimization and maximization in order to produce different Pareto fronts and determine the extent of the impact caused by DG. The analysis provides useful information, such as the identification of futures that should be considered in planning. A future means a set of realizations of all uncertainties. MEPSO also presented a satisfactory performance in obtaining the Pareto fronts.

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.