Are local wind power resources well estimated?

Planning and financing of wind power installations require very importantly accurate resource estimation in addition to a number of other considerations relating to environment and economy. Furthermore, individual wind energy installations cannot in general be seen in isolation. It is well known that the spacing of turbines in wind farms is critical for maximum power production. It is also well established that the collective effect of wind turbines in large wind farms or of several wind farms can limit the wind power extraction downwind. This has been documented by many years of production statistics. For the very large, regional sized wind farms, a number of numerical studies have pointed to additional adverse changes to the regional wind climate, most recently by the detailed studies of Adams and Keith [1]. They show that the geophysical limit to wind power production is likely to be lower than previously estimated. Although this problem is of far future concern, it has to be considered seriously. In their paper they estimate that a wind farm larger than 100 km ^2 is limited to about 1 W m ^-2 . However, a 20 km ^2 off shore farm, Horns Rev 1, has in the last five years produced 3.98 W m ^-2  [5]. In that light it is highly unlikely that the effects pointed out by [1] will pose any immediate threat to wind energy in coming decades. Today a number of well-established mesoscale and microscale models exist for estimating wind resources and design parameters and in many cases they work well. This is especially true if good local data are available for calibrating the models or for their validation. The wind energy industry is still troubled by many projects showing considerable negative discrepancies between calculated and actually experienced production numbers and operating conditions. Therefore it has been decided on a European Union level to launch a project, ‘The New European Wind Atlas’, aiming at reducing overall uncertainties in determining wind conditions. The project is structured around three areas of work, to be implemented in parallel. One of the great challenges to the project is the application of mesoscale models for wind resource calculation, which is by no means a simple matter [3]. The project will use global reanalysis data as boundary conditions. These datasets, which are time series of the large-scale meteorological situation covering decades, have been created by assimilation of measurement data from around the globe in a dynamical consistent fashion using large-scale numerical models. For wind energy, the application of the reanalysis datasets is as a long record of the large-scale wind conditions. The large-scale reanalyses are performed in only a few global weather prediction centres using models that have been developed over many years, and which are still being developed and validated and are being used in operational services. Mesoscale models are more diverse, but nowadays quite a number have a proven track record in applications such as regional weather prediction and also wind resource assessment. There are still some issues, and use of model results without proper validation may lead to gross errors. For resource assessment it is necessary to include direct validation with in situ observed wind data over sufficiently long periods. In doing so, however, the mesoscale model output must be downscaled using some microscale physical or empirical/statistical model. That downscaling process is not straightforward, and the microscale models themselves tend to disagree in some terrain types as shown by recent blind tests [4]. All these ‘technical’ details and choices, not to mention the model formulation itself, the numerical schemes used, and the effective spatial and temporal resolution, can have a significant impact on the results. These problems, as well as the problem of how uncertainties are propagated through the model chain to the calculated wind resources, are central in the work with the New European Wind Atlas. The work of [1] shows that when wind energy has been implemented on a very massive scale, it will affect the power production from entire regions and that has to be taken into account. References [1] Adams A S and Keith D W 2013 Are global wind power resource estimates overstated? Environ. Res. Lett. 8 015021 [2] 2011 A New EU Wind Energy Atlas: Proposal for an ERANET+ Project (Produced by the TPWind Secretariat) Nov. [3] Petersen E L Troen I 2012 Wind conditions and resource assessment WIREs Energy Environ. 1 206–17 [4] Bechmann A, Sørensen N N, Berg J, Mann J Rethore P-E 2011 The Bolund experiment, part II: blind comparison of microscale flow models Boundary-Layer Meteorol. 141 245–71 [5] http://www.lorc.dk/offshore-wind-farms-map/horns-rev-1 http://www.ens.d

Observation of Dirac plasmons in a topological insulator

Plasmons are the quantized collective oscillations of electrons in metals and doped semiconductors. The plasmons of ordinary, massive electrons are since a long time basic ingredients of research in plasmonics and in optical metamaterials. Plasmons of massless Dirac electrons were instead recently observed in a purely two-dimensional electron system (2DEG)like graphene, and their properties are promising for new tunable plasmonic metamaterials in the terahertz and the mid-infrared frequency range. Dirac quasi-particles are known to exist also in the two-dimensional electron gas which forms at the surface of topological insulators due to a strong spin-orbit interaction. Therefore,one may look for their collective excitations by using infrared spectroscopy. Here we first report evidence of plasmonic excitations in a topological insulator (Bi2Se3), that was engineered in thin micro-ribbon arrays of different width W and period 2W to select suitable values of the plasmon wavevector k. Their lineshape was found to be extremely robust vs. temperature between 6 and 300 K, as one may expect for the excitations of topological carriers. Moreover, by changing W and measuring in the terahertz range the plasmonic frequency vP vs. k we could show, without using any fitting parameter, that the dispersion curve is in quantitative agreement with that predicted for Dirac plasmons.Comment: 11 pages, 3 figures, published in Nature Nanotechnology (2013

Classical and Quantum Behavior in Mean-Field Glassy Systems

In this talk I review some recent developments which shed light on the main connections between structural glasses and mean-field spin glass models with a discontinuous transition. I also discuss the role of quantum fluctuations on the dynamical instability found in mean-field spin glasses with a discontinuous transition. In mean-field models with pairwise interactions in a transverse field it is shown, in the framework of the static approximation, that such instability is suppressed at zero temperature.Comment: 9 Pages (including 5 Figures), Revtex, Proceedings of the XIV Sitges Conference, June 1996 (Barcelona) Spai