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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

Article Abstract To better allocate funds in the new EU research framework programme Horizon Europe, an assessment of current and past efforts is crucial. In this paper we develop and apply a multi-method qualitative and computational approach to provide a catalogue of climate crisis mitigation technologies on the EU level between 2014 and 2020. Using the approach, we observed no public EU-level funding for multiple technologies prioritised by the EU, such as low-carbon production and use of cement and chemicals, electric battery, and a number of industrial decarbonisation processes. We observed a rising trend in the funding of solar power and onshore wind, the adjacent to them power-to-X technology, as well as recycling. At the same time, the shares of funding into fuel cell, biofuel, demand-side energy management, microgrids, and waste management show a decline trend. With note of the exploratory character of the present paper, we propose that the EU Horizon 2020 funding of clean technologies only partially reflected the expectations of key institutionalised EU actors due to the existence of many non-funded prioritised technologies.

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.

Cette modélisation présente la partie de contrôle d'une pile à combustible à membrane d'échange de protons (PEMFC). L'objectif du modèle de représenter la chaîne de contrôle de la tension de la PEMFC. Ce modèle est basé sur une représentation macroscopique énergétique (REM) de la pile à combustible, puis conduit à une structure dite de contrôle maximal (SCM). La SCM est une inversion étape par étape de la REM (structure de contrôle basée sur un modèle d'inversion). Le processus de conception de la commande est basé sur une définition explicite du problème. Par exemple, les entrées de réglage, les objectifs du système ou les contraintes sont mises en évidence pour organiser la commande. De plus, le SMC montre les endroits où les capteurs sont nécessaires et où les contrôleurs sont requis. Malheureusement, le SCM n'est qu'une structure de contrôle théorique. Par conséquent, une structure réaliste nécessite certaines simplifications, ce qui conduit à une structure de contrôle dite pratique. This modeling presents the control part of a proton exchange membrane fuel cell (PEMFC). The objective of the model is to represent the voltage control chain of the PEMFC. This model is based on an energetic macroscopic representation (EMR) of the fuel cell and then leads to a so-called maximum control structure (MCS). The SCM is a step-by-step inversion of the REM (control structure based on an inversion model). The control design process is based on an explicit problem definition. For example, control inputs, system objectives, or constraints are highlighted to organize the control. In addition, the MSC shows where sensors are needed and where controllers are required. Unfortunately, the SCM is only a theoretical control structure. Therefore, a realistic structure requires some simplifications, which leads to a so-called practical control structure. AdditionnalInformation: Modèle créé lors d'une thèse en génie électrique, par Loïc Boulon (ses recherches portent sur la modélisation et le contrôle des véhicules électriques hybrides, des sources d'énergie et d'énergie et de puissance, et des systèmes de piles à combustible). Il a travaillé en vue de l'obtention du diplôme de doctorat à l'Université de Franche-Comté, Belfort, France, où sa thèse de doctorat a été réalisée en collaboration avec le FEMTO-ST, Belfort, et les Laboratoires L2EP, Lille. Co-encadrement par Daniel Hissel (PU au laboratoire FEMTO-ST), Alain Bouscayrol (PU au laboratoire L2EP) et Marie-Célie Péra (PU au laboratoire FEMTO-ST) AdditionnalInformation: Model created during a thesis in electrical engineering, by Loïc Boulon (his research focuses on the modeling and control of hybrid electric vehicles, energy and power sources, and fuel cell systems). He worked towards his Ph.D. degree at the University of Franche-Comté, Belfort, France, where his dissertation was complet in collaboration with the FEMTO-ST, Belfort, and the L2EP Laboratories, Lille. Co-supervision by Daniel Hissel (PU at FEMTO-ST laboratory), Alain Bouscayrol (PU at L2EP laboratory) and Marie-Célie Péra (PU at FEMTO-ST laboratory) Données computationnelles ou de simulation: - Langage de programmation : Langage Matlab - Plate-forme/OS : Microsoft Windows, Mac OS, Linux,... - Version : MATLAB R2018a - Statut de développement : Actif Simulation or computational data: - Programming language: Matlab language - Platform/OS: Microsoft Windows, Mac OS, Linux,... - Version: MATLAB R2018a - Development status: Active Project: Thèse de doctorat en génie électrique - Loïc Boulon, Contrat doctoral (Region Bourgogne Franche-Comté) Label: PHE (Plateforme Hydrogène-Energie) Audience: Research

This article proposes an effective evolutionary hybrid optimization method for identifying unknown parameters in photovoltaic (PV) models based on the northern goshawk optimization algorithm (NGO) and pattern search (PS). The chaotic sequence is used to improve the exploration capability of the NGO algorithm technique while evading premature convergence. The suggested hybrid algorithm, chaotic northern goshawk, and pattern search (CNGPS), takes advantage of the chaotic NGO algorithm’s effective global search capability as well as the pattern search method’s powerful local search capability. The effectiveness of the recommended CNGPS algorithm is verified through the use of mathematical test functions, and its results are contrasted with those of a conventional NGO and other effective optimization methods. The CNGPS is then used to extract the PV parameters, and the parameter identification is defined as an objective function to be minimized based on the difference between the estimated and experimental data. The usefulness of the CNGPS for extraction parameters is evaluated using three distinct PV models: SDM, DDM, and TDM. The numerical investigates illustrate that the new algorithm may produce better optimum solutions and outperform previous approaches in the literature. The simulation results display that the novel optimization method achieves the lowest root mean square error and obtains better optima than existing methods in various solar cells.

Three distinct dataset used to forecast the development of marine energy in Europe in the upcoming three decades: - European offshore wind farms - tidal energy converter deployements in Europe - wave energy converter deployements in Europe

This data set includes transport measurements data of zinc oxide (ZnO) and indium tin oxide (ITO) thin films. The measurements were done using the van der Pauw / Hall effect technique in all configurations and with respect to the current intensity, thanks to the Carel Platform of the LMOPS laboratory. ITO was optimized to be used as a back contact in all-oxide solar cells and ZnO as a top window layer.

EMSO-Western Ligurian Site (European Multidisciplinary See floor Observatory and water column, Western Ligurian Site) is a second generation permanent submarine observatory deployed offshore of Toulon, France, as a follow up of the pioneering ANTARES neutrino telescope located nearby. This submarine network is part of KM3NeT (https://www.km3net.org/) which has a modular topology designed to connect up to 120 neutrino detection units, i.e. ten times more than ANTARES. The Earth and Sea Science (ESS) instrumentation connected to KM3NeT is based on two complementary components: an Instrumented Interface Module (MII) and an autonomous mooring line (ALBATROSS). The Module Interface Instrumented "MII" was deployed in May 2019 and cabled to the MEUST Node#1. Node#1 is cabled to the shore via the KM3NeT cable. The MII provides data for temperature, conductivity/salinity, pressure, particles proxy (deduced from beam attenuation measured with a CSTAR transmissiometer). The MII collects data through an acoustic link from the instrumented mooring line ALBATROSS (https://doi.org/10.17882/74513) deployed at a distance of 2-3 kilometers. These data are being transferred daily for near real time visualisation.

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).