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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

This data set is no longer current – The most current data and all historical data sets can be found at https://data.nrel.gov/submissions/244 This database represents a list of community solar projects identified through various sources as of Dec 2021. The list has been reviewed but errors may exist and the list may not be comprehensive. Errors in the sources e.g. press releases may be duplicated in the list. Blank spaces represent missing information. NREL invites input to improve the database including to - correct erroneous information - add missing projects - fill in missing information - remove inactive projects. Updated information can be submitted to the contact(s) located on the current data set page linked at the top.

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.

Batteries have the potential to significantly reduce greenhouse gas emissions from on-road transportation. However, environmental and social impacts of producing lithium-ion batteries, particularly cathode materials, and concerns over material criticality are frequently highlighted as barriers to widespread electric vehicle adoption. Circular economy strategies, like reuse and recycling, can reduce impacts and secure regional supplies. To understand the potential for circularity, we undertake a dynamic global material flow analysis of pack-level materials that includes scenario analysis for changing battery cathode chemistries and electric vehicle demand. Results are produced regionwise and through the year 2040 to estimate the potential global and regional circularity of lithium, cobalt, nickel, manganese, iron, aluminum, copper, and graphite, although the analysis is focused on the cathode materials. Under idealized conditions, retired batteries could supply 60% of cobalt, 53% of lithium, 57% of manganese, and 53% of nickel globally in 2040. If the current mix of cathode chemistries evolves to a market dominated by NMC 811, a low cobalt chemistry, there is potential for 85% global circularity of cobalt in 2040. If the market steers away from cathodes containing cobalt, to an LFP-dominated market, cobalt, manganese, and nickel become less relevant and reach circularity before 2040. For each market to benefit from the recovery of secondary materials, recycling and manufacturing infrastructure must be developed in each region. This data was collected through various sources, including from EV Volumes, International Energy Agency, Argonne National Lab, and published articles. A model was created with R to process the data.  R is required to open the models.

The rapid development of advanced metering infrastructure provides a new data source—building electrical load profiles with high temporal resolution. Electric load profile characterization can generate useful information to enhance building energy modeling and provide metrics to represent patterns and variability of load profiles. Such characterizations can be used to identify changes to building electricity demand due to operations or faulty equipment and controls. In this study, we proposed a two-path approach to analyze high temporal resolution building electrical load profiles: (1) time-domain analysis and (2) frequency-domain analysis. The commonly adopted time-domain analysis can extract and quantify the distribution of key parameters characterizing load shape such as peak-base load ratio and morning rise time, while a frequency-domain analysis can identify major periodic fluctuations and quantify load variability. We implemented and evaluated both paths using whole-year 15-minute interval smart meter data of 188 commercial office building in Northern California. The results from these two paths are consistent with each other and complementary to represent full dynamics of load profiles. The time- and frequency-domain analyses can be used to enhance building energy modeling by: (1) providing more realistic assumptions about building operation schedules, and (2) validating the simulated electric load profiles using the developed variability metrics against the real building load data.

Purpose This study aims to consider environmental sustainability, a global challenge under the preview of sustainable development goals, highlighting the significance of knowledge economy in attaining sustainable aggregate demand behavior globally. For this purpose, 155 countries that have data available from 1995 to 2021 were selected. The purpose of selecting these countries is to test the global responsibility of the knowledge economy to attain environmental sustainability. Design/methodology/approach Results are estimated with the help of panel quantile regression. The empirical existence of aggregate demand-based environmental Kuznets curve (EKC) was tested using non-linear tests. Moreover, principal component analysis has been incorporated to construct the knowledge economy index. Findings U-shaped aggregate demand-based EKC at global level is validated. However, environmental deterioration increases with an additional escalation after US$497.945m in aggregate demand. As a determinant, the knowledge economy is reducing CO2 emissions. The knowledge economy has played a significant role in global responsibility, shifting the EKC downward and extending the CO2 reduction phase for every selected country. Further, urbanization, energy intensity, financial development and trade openness significantly deteriorate the environmental quality. Originality/value This study contains the empirical existence of aggregate demand-based EKC. The role of the knowledge economy is examined through an index which is calculated by using four pillars of the knowledge economy (technology, innovations, education and institutions). This study is based on a combined panel of all the countries for which the data was available.

Abstract Molten salt reactors (MSRs) are a class of next-generation nuclear reactors that have received recent industrial and research interest. A generalized species transport solver was implemented in the Virtual Environment for Reactor Applications (VERA) computing suite to extend this tool to analyze liquid-fueled MSRs. This core simulator has been extended to model the transport of fission product gases into a collection of circulating gas bubbles with the purpose of removing the gases. This paper presents the governing species transport equation, along with various nuclear source terms. Development of the source term for phase migration is discussed, along with a simplified interfacial area tracking method. Finally, a case study on a simplified MSR loop is presented in which modeling parameters were varied to assess their impact on gas removal. The steady state results show that parameters such as bubble diameter, gas injection rate and mass transfer coefficient have a low to moderate effect on the fraction of xenon in the core region. Removal efficiency has the greatest effect on the fraction in the core region. After the pump bowl, bubble diameter has a minor effect on the fraction of xenon in the gas void. These results point out that increasing parameters such as mass transfer coefficient, gas injection rate, and removal efficiency drives the xenon into the circulating gas void, while decreasing bubble diameter also drives xenon into the gas void by increasing interfacial area.

As is the case with several other mechanical power takeoffs (PTOs), the mechanical-motion-rectifier-based PTO consists of components, such as one-way clutches, gears, a ball screw, mechanical couplings, and a generator. Equivalent circuit models have been created in this article to describe the dry frictions, viscous damping, and mechanical compliances in these components, so the nonideal efficiency and nonlinear force of the PTO can be predicted in electrical simulations by integrating these subcircuit models. The circuit model is simplified, and its parameters are categorized as dc and ac unknowns. The dc and ac tests on the PTO are performed sequentially to extract two sets of parameters through linear regression or nonlinear curve fitting. Then, the model is validated through its prediction capability over 25 test conditions on input forces, output voltages, and efficiencies, with correlation coefficients of 0.9, 0.98, and 0.981, respectively.

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