• Energy Research
• 7. Clean energy close
• FR close
• English close

L'empreinte carbone des technologies numériques est une préoccupation depuis plusieurs années. Cela concerne principalement la consommation électrique des datacenters; beaucoup de fournisseurs dans le domaine du cloud s'engagent à n'utiliser que des sources d'énergie renouvelables. Cependant, cette approche néglige la phase de fabrication des composants des infrastructures numériques. Nous considérons dans ce travail de recherche la question du dimensionnement des énergies renouvelables pour une infrastructure de type cloud géographiquement distribuée autour de la planète, considérant l'impact carbone à la fois de l'électricité issue du réseau électrique local en fonction de la location de sa production, et de la fabrication des panneaux photovoltaïques et des batteries pour la part renouvelable de l'alimentation des ressources. Nous avons modélisé ce problème de minimisation de l'impact carbone d'une telle infrastructure cloud sous la forme d'un programme linéaire. La solution est le dimensionnement optimal d'une fédération de cloud sur une année complète en fonction des localisations des datacenters, des traces réelles des travaux à exécuter et valeurs d'irradiation solaire heure par heure. Nos résultats montrent une réduction de l'impact carbone de 30% comparés à la même architecture cloud totalement alimentée par des énergies renouvelables et 85% comparés à un modèle qui n'utiliserait qu'une alimentation via le réseau local d'électricité. The carbon footprint of IT technologies has been a significant concern in recent years. This concern mainly focuses on the electricity consumption of data centers; many cloud suppliers commit to using 100% of renewable energy sources. However, this approach neglects the impact of device manufacturing. We consider in this work the question of dimensioning the renewable energy sources of a geographically distributed cloud with considering the carbon impact of both the grid electricity consumption in the considered locations and the manufacturing of solar panels and batteries. We design a linear program to optimize cloud dimensioning over one year, considering worldwide locations for data centers, real-life workload traces, and solar irradiation values. Our results show a carbon footprint reduction of about 30% compared to a cloud fully supplied by solar energy and of 85% compared to the 100% grid electricity model. Données computationnelles ou de simulation: En tenant compte des données en entrée (description de la fédération de centres de données, fichiers de configuration appropriés, conditions météorologiques, etc.), le logiciel est capable de proposer un dimensionnement optimal pour la fédération des datacenters à faible émission de carbone distribuée à l'échelle mondiale : surface des panneaux photovoltaïques et capacité des batteries pour chaque datacenter de la fédération. Des scripts sont disponibles pour mettre en forme les solutions proposées. Simulation or computational data: Considering given inputs (datacenter federation, appropriate configuration files, weather conditions, etc.), the software is able to propose an optimal sizing for the globally distributed low carbon cloud federation: surface area of solar panels, battery capacity for each data center location. . Scripts are available to shape the optimal configuration. Audience: Research, Policy maker UpdatePeriodicity: as needed

Comprehensive and reliable information on anthropogenic sources of greenhouse gas emissions is required to track progress towards keeping warming well below 2°C as agreed upon in the Paris Agreement. Here we provide a dataset on anthropogenic GHG emissions 1970-2019 with a broad country and sector coverage. We build the dataset from recent releases from the “Emissions Database for Global Atmospheric Research” (EDGAR) for CO2 emissions from fossil fuel combustion and industry (FFI), CH4 emissions, N2O emissions, and fluorinated gases and use a well-established fast-track method to extend this dataset from 2018 to 2019. We complement this with information on net CO2 emissions from land use, land-use change and forestry (LULUCF) from three available bookkeeping models.

Project: GCOS Earth Heat Inventory - A study under the Global Climate Observing System (GCOS) concerted international effort to update the Earth heat inventory (EHI), and presents an updated international assessment of ocean warming estimates, and new and updated estimates of heat gain in the atmosphere, cryosphere and land over the period from 1960 to present. Summary: The file “GCOS_EHI_1960-2020_Earth_Heat_Inventory_Ocean_Heat_Content_data.nc” contains a consistent long-term Earth system heat inventory over the period 1960-2020. Human-induced atmospheric composition changes cause a radiative imbalance at the top-of-atmosphere which is driving global warming. Understanding the heat gain of the Earth system from this accumulated heat – and particularly how much and where the heat is distributed in the Earth system - is fundamental to understanding how this affects warming oceans, atmosphere and land, rising temperatures and sea level, and loss of grounded and floating ice, which are fundamental concerns for society. This dataset is based on a study under the Global Climate Observing System (GCOS) concerted international effort to update the Earth heat inventory published in von Schuckmann et al. (2020), and presents an updated international assessment of ocean warming estimates, and new and updated estimates of heat gain in the atmosphere, cryosphere and land over the period 1960-2020. The dataset also contains estimates for global ocean heat content over 1960-2020 for different depth layers, i.e., 0-300m, 0-700m, 700-2000m, 0-2000m, 2000-bottom, which are described in von Schuckmann et al. (2022). This version includes an update of heat storage of global ocean heat content, where one additional product (Li et al., 2022) had been included to the initial estimate. The Earth heat inventory had been updated accordingly, considering also the update for continental heat content (Cuesta-Valero et al., 2023).

Within an overall project to assess the ability of the agricultural sector to contribute to bioenergy production, we set out here to examine the economic and technological viability of a bioenergy facility in an uncertain economic context, using the stochastic viability approach. We consider two viability constraints: the facility demand for lignocellulosic feedstock has to be satisfied each year and the associated supply cost has to be lower than de profitability threshold of the facility. We assess the viability probability of various supplying strategies consisting in contracting a given share of the feedstock demand with perennial dedicated crops at the initial time and then in making up each year with annual dedicated crops or wood. The demand constraints and agricultural prices scenarios over the time horizon are introduced in an agricultural and forest biomass supply model, which in turns determines the supply cost per MWh and computes the viability probabilities of the various contract strategies. A sensibility analysis to agricultural prices at initial time is performed. Results show that when they are around or under the median (of the 1993–2007 prices), the strategy consisting in contracting 100% of the feedstock supply with perennial dedicated crops is the best one.

This work deals with an integrated solar PV/T hybrid air collector suitable for applications such as heating and drying of hay and industrial products. The main purpose of this study is to evaluate and optimize the thermal and electrical performances of this PV component and its integration system. A 2D mathematical model describing the thermal behaviour of the component is presented. The simulation values are compared to the measured data obtained under steady conditions on a solar PV/T air collector. Then, a description is provided regarding the in situ experimental studies which are carried out on three various models of the same component, in order to validate the thermal model previously developed. As further steps, various simulations will be performed on a drying system under a full building integration setup. 25th European Photovoltaic Solar Energy Conference and Exhibition / 5th World Conference on Photovoltaic Energy Conversion, 6-10 September 2010, Valencia, Spain; 5150-5152

The aim of this work was to gain insight and knowledge with the basic concepts of the Bitcoinnetwork and its relation to energy consumption. Precisely, the ambition of this literature-based research was to identify key determinants of the network ́s energy intensiveness. After an extensive review of the relevant literature on the topic, key principles of Bitcoin ́s electricity consumption were derived.With regards to environmental sustainability concerns, it was found that the network ́sproperties, in theory, allow for improvements of the unit economics of renewable energy production facilities and renewable intensive energy grids. Such applications, however, are dependent on the unforecastable market dynamics of the Bitcoin price.In a contextualization approach it was attempted to categorize the electricity consumption levels of the Bitcoin network based on the services offered with similar but not comparable entities.This approach finds that energy consumption does not provide a conclusive and instructive comparative parameter to determine whether energy consumption levels of the Bitcoin network.The paper concludes with advocating for location dependent policy approaches that encourage the strategic deployment of Bitcoin mining hardware to minimize environmental and economic opportunity cost.

International audience; This paper presents the results of static and dynamic crushing of wood sandwich tubes made with a birch core and carbon woven skins. 3 tubes (external diameter 64 mm, length 120 mm) were crushed in static and 3 others in dynamic under a drop tower. The results reveal very interesting energy absorption performance of such structures. The Specific Energy Absorption is 50.4 J/g in static and 66.6 J/g in dynamic, the energy absorbed is 5427 J in static and 7045 J in dynamic (170 kg launched at 4.2 m). These characteristics, which are quite comparable with those of tubes made of composite materials or aluminum alloys, show that these sandwiches with birch core are serious candidates for energy absorption with renewable materials.

Article written in view of the conference / debate organised by IPEMED, on 2 December 2015, alongside the COP21.

Les changements d’affectation des sols (CAS) induits par les biocarburants et les émissions associées de gaz à effet de serre (GES) sont estimés par des modèles. Les estimations varient dans une large mesure dans les études disponibles et montrent une forte sensibilité à certaines hypothèses ainsi qu’à la valeur de paramètres clés. Elles suggèrent généralement que le biodiésel et l’éthanol, mais aussi que les biocarburants européens et américains, induisent des CAS et des émissions de GES différentes, mais il n’y a pas de consensus sur le type ni sur l’origine du biocarburant qui pourrait générer le moins d’impact sur ces critères. Nous montrons dans ce papier que l’hypothèse de modélisation Armington des échanges, couramment utilisée dans les modèles, contribue à expliquer ces différences. A partir de données observées et de l’utilisation du modèle d’équilibre partiel MATSIM-LUCA, nous montrons que les CAS découlant du développement des biocarburants sont hautement dépendant des hypothèses posées sur le commerce : l’approche dite du « marché mondial intégré », reposant sur l’hypothèse de produits homogènes, tend à gommer les différences dans les estimations de CAS induits par les biocarburants entre le biodiésel et l’éthanol et entre le biocarburant européen et américain, en comparaison à l’approche Armington qui considère que les produits sont différents selon leur origine et donc moins substituables. Available estimates of biofuel-induced land use change (LUC) and corresponding greenhouse gas (GHG) emissions vary on a wide range while estimations obtained from each model are highly sensitive to certain assumptions and key parameter values. Available studies often suggest that biodiesel and ethanol and/or EU and US biofuels would lead to different LUC and GHG emissions but they don’t agree on the type and/or the origin of the biofuel which would induce the least LUC and GHG emissions. In this paper we investigate the reasons behind this feature. We show that the Armington modeling of trade flows, which is currently used in models, contributes to this pattern. Using both observed data and the partial equilibrium model MATSIM-LUCA, we show that LUC stemming from the development of biofuels is highly dependent on assumptions made on trade: the Integrated World Market (IWM) approach, which relies on the homogenous product assumption, tends to erase differences in estimates of induced LUC from biodiesel and ethanol and from EU and US biofuels as compared to the Armington approach, that postulates that product are differentiated according to their origin and thus less substitutable.