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Fruto da parceria entre o FGVces, a COPPE/UFRJ, a ABStartups, a EDP e a Statkraft, e viabilizado pelo Programa P&D ANEEL (código do projeto PD-07267-0010/2018), o estudo inédito Impacto do Ecossistema de Startups no Setor Elétrico Brasileiro foi iniciado em maio de 2018 e tem como objetivo compreender o ecossistema de startups do segmento de tecnologias limpas e analisar seu impacto no setor elétrico brasileiro. Os resultados obtidos com o estudo são subsídios ao desenvolvimento de políticas públicas para o fortalecimento do ecossistema de tecnologias limpas no Brasil. O estudo também contribui para aproximar grandes empresas e startups e, assim, dar escala às suas inovações.

[Departement_IRSTEA]Eaux [ADD1_IRSTEA]Hydrosystèmes et risques naturels; International audience; Understanding changes in glacier mass balances is essential for investigating climate changes. However, glacier‐wide mass balances determined from geodetic observations do not provide a relevant climatic signal as they depend on the dynamic response of the glaciers. In situ point mass balance measurements provide a direct signal but show a strong spatial variability that is difficult to assess from heterogeneous in situ measurements over several decades. To address this issue, we propose a nonlinear statistical model that takes into account the spatial and temporal changes in point mass balances. To test this model, we selected four glaciers in different climatic regimes (France, Bolivia, India, and Norway) for which detailed point annual mass balance measurements were available over a large elevation range. The model extracted a robust and consistent signal for each glacier. We obtained explained variances of 87.5, 90.2, 91.3, and 75.5% on Argentière, Zongo, Chhota Shigri, and Nigardsbreen glaciers, respectively. The standard deviations of the model residuals are close to measurement uncertainties. The model can also be used to detect measurement errors. Combined with geodetic data, this method can provide a consistent glacier‐wide annual mass balance series from a heterogeneous network. This model, available to the whole community, can be used to assess the impact of climate change in different regions of the world from long‐term mass balance series.

International audience; Streamflow time series are commonly derived from stage-discharge rating curves, but the uncertainty of the rating curve and resulting streamflow series are poorly understood. While different methods to quantify uncertainty in the stage-discharge relationship exist, there is limited understanding of how uncertainty estimates differ between methods due to different assumptions and methodological choices. We compared uncertainty estimates and stage-discharge rating curves from seven methods at three river locations of varying hydraulic complexity. Comparison of the estimated uncertainties revealed a wide range of estimates, particularly for high and low flows. At the simplest site on the Isère River (France), full width 95% uncertainties for the different methods ranged from 3 to 17% for median flows. In contrast, uncertainties were much higher and ranged from 41 to 200% for high flows in an extrapolated section of the rating curve at the Mahurangi River (New Zealand) and 28 to 101% for low flows at the Taf River (United Kingdom), where the hydraulic control is unstable at low flows. Differences between methods result from differences in the sources of uncertainty considered, differences in the handling of the time-varying nature of rating curves, differences in the extent of hydraulic knowledge assumed, and differences in assumptions when extrapolating rating curves above or below the observed gaugings. Ultimately, the selection of an uncertainty method requires a match between user requirements and the assumptions made by the uncertainty method. Given the significant differences in uncertainty estimates between methods, we suggest that a clear statement of uncertainty assumptions be presented alongside streamflow uncertainty estimates.

International audience; Stage-discharge rating curves are used to relate streamflow discharge to continuously measured river stage readings to create a continuous record of streamflow discharge. The stage-discharge relationship is estimated and refined using discrete streamflow measurements over time, during which both the discharge and stage are measured. There is uncertainty in the resulting rating curve due to multiple factors including the curve-fitting process, assumptions on the form of the model used, fluvial geomorphology of natural channels, and the approaches used to extrapolate the rating equation beyond available observations. This rating curve uncertainty leads to uncertainty in the streamflow timeseries, and therefore to uncertainty in predictive models that use the streamflow data. Many different methods have been proposed in the literature for estimating rating curve uncertainty, differing in mathematical rigor, in the assumptions made about the component errors, and in the information required to implement the method at any given site. This study describes the results of an international experiment to test and compare streamflow uncertainty estimation methods from 7 research groups across 9 institutions. The methods range from simple LOWESS fits to more complicated Bayesian methods that consider hydraulic principles directly. We evaluate these different methods when applied to three diverse gauging stations using standardized information (channel characteristics, hydrographs, and streamflow measurements). Our results quantify the resultant spread of the stage-discharge Stage-discharge rating curves are developed by relating measured streamflow discharges to concurrent measured river stage readings. Once developed, a rating curve can be used to convert a continuous stage record into a continuous record of streamflow. The stage-discharge relation is estimated and refined using discrete streamflow measurements over time, during which both the discharge and stage are measured. Besides the uncertainties inherently associated with a discharge measurement, there is uncertainty in the resulting rating curve due to multiple factors including the curve-fitting process, assumptions on the form of the model used, fluvial geomorphology of natural channels, and the approaches used to extrapolate the rating equation beyond available observations. A number of different methods have been tested for estimating rating curve uncertainty, differing in mathematical complexity, in the assumptions made about the component errors, and in the information required to implement the method at any given site. This study compares several methods that range from simple regression fits to more complicated Bayesian methods that consider hydraulic principles directly. We evaluate these different methods when applied to three gaging stations using the same information (channel characteristics, hydrographs, and streamflow measurements). We quantify the resultant spread of the stage-discharge curves and compare the level of uncertainty attributed to the streamflow records by the different methods.

International audience; Stage-discharge rating curves are used to relate streamflow discharge to continuously measured river stage readings to create a continuous record of streamflow discharge. The stage-discharge relationship is estimated and refined using discrete streamflow measurements over time, during which both the discharge and stage are measured. There is uncertainty in the resulting rating curve due to multiple factors including the curve-fitting process, assumptions on the form of the model used, fluvial geomorphology of natural channels, and the approaches used to extrapolate the rating equation beyond available observations. This rating curve uncertainty leads to uncertainty in the streamflow timeseries, and therefore to uncertainty in predictive models that use the streamflow data. Many different methods have been proposed in the literature for estimating rating curve uncertainty, differing in mathematical rigor, in the assumptions made about the component errors, and in the information required to implement the method at any given site. This study describes the results of an international experiment to test and compare streamflow uncertainty estimation methods from 7 research groups across 9 institutions. The methods range from simple LOWESS fits to more complicated Bayesian methods that consider hydraulic principles directly. We evaluate these different methods when applied to three diverse gauging stations using standardized information (channel characteristics, hydrographs, and streamflow measurements). Our results quantify the resultant spread of the stage-discharge curves and compare the level of uncertainty attributed to the streamflow records by each different method. We provide insight into the sensitivity of streamflow uncertainty bounds to the choice of uncertainty estimation method, and discuss the implications for model uncertainty assessment.

The present paper summarizes analyses of a two-body floating wave energy converter (WEC) to determine the mooring tension and the effect of the mooring system on energy capture. Also, the effect of the power take-off (PTO) is assessed. An axisymmetric Wavebob-type WEC is chosen as the object of investigation. However, the PTO system is modeled in a simplified manner as ideal linear damping and spring terms that couple the motions of the two bodies. The analysis is performed using SIMO, which is a time domain simulation tool that accommodates the simulation of multibody systems with hydrodynamic interactions. In SIMO, docking cone features between the two bodies allow movement as per actual operation, and fenders are applied to represent end stops. Six alternative mooring configurations are applied to investigate the effect of mooring on power capture. Mooring analysis is performed to determine the necessary capacity of mooring lines for each configuration to carry the tension due to the WEC motion in extreme conditions. Hydrodynamic loads are determined using WAMIT. We assumed that the WEC will be operated to capture wave power at the Yeu site in France. The analysis is performed for several regular and irregular wave conditions according to wave data available for that site. Simulations are performed to study the effect of the PTO system, end stops settings and several mooring configurations on power capture.

With Corrigendum in Renewable Energy, 2015, Vol. 74, pp. 955-957; International audience; The aim of this study is to estimate the mean annual power absorption of a selection of eight Wave Energy Converters (WECs) with different working principles. Based on these estimates a set of power performance measures that can be related to costs are derived. These are the absorbed energy per characteristic mass [kWh/kg], per characteristic surface area [MWh/m$^2$], and per root mean square of Power Take Off (PTO) force [kWh/N].The methodology relies on numerical modelling. For each device, a numerical Wave-to-Wire (W2W) model is built based on the equations of motion. Physical effects are modelled according to the state-of-the-art within hydrodynamic modelling practice. Then, the W2W models are used to calculate the power matrices of each device and the mean annual power absorption at five different representative wave sites along the European Coast, at which the mean level of wave power resource ranges between 15 and 88 kW per metre of wave front. Uncertainties are discussed and estimated for each device.Computed power matrices and results for the mean annual power absorption are assembled in a summary sheet per device. Comparisons of the selected devices show that, despite very different working principles and dimensions, power performance measures vary much less than the mean annual power absorption. With the chosen units, these measures are all shown to be of the order of 1.