• Energy Research
• 2021-2025close
• 7. Clean energy close
• 12. Responsible consumption close

The dataset includes 90 global food system and land use scenarios developed with the model BioBaM-GHG 2.0. The scenarios have been developed for assessing the global potential of forest regeneration for climate mitigation to 2050 under various food system pathways, i.e. diets, crop yield developments, land requirements for energy crops, and two variants of grassland use. The scenarios include the following data on country level: Land use and land-use change, cropland area by crop group, grazing area by quality classes, crop production by crop groups, crop consumption by crop groups and use types, crop wastes (losses), net imports/exports, production and consumption of animal products, grass supply and demand, GHG emissions from land-use change, GHG emissions from agricultural activities, and total cumulated GHG emissions. The main model result in this context, cumulative carbon sequestration from forest regeneration until 2050, is calculated as difference between the parameters "GHG emissions from land use change (cumulative) (Mt CO2e)" and "GHG emissions from land use change excluding C stock changes from natural succession (cumulative) (Mt CO2e)". Please refer to the related publication "Exploring the option space for land system futures at regional to global scales: The diagnostic agro-food, land use and greenhouse gas emission model BioBaM-GHG 2.0" (Kalt et al., 2021 - currently under review at Ecological Modelling) for further information. This work was funded by the Austrian Science Fund (FWF) within project P29130-G27 GELUC.

Lawrence Berkeley National Laboratory (Berkeley Lab) estimates hourly project-level generation data for utility-scale solar projects and hourly county-level generation data for residential and non-residential distributed photovoltaic (PV) systems in the seven organized wholesale markets and 10 additional Balancing Areas. To encourage its broader use, Berkeley Lab has made this data file public here at OEDI, covering the years 2012-2020. The public project-level dataset is updated annually with data from the previous calendar year. For more information about the research project, including a technical report, briefing material, visualizations, and additional data, please visit the project homepage linked in this submission.

2019FEREC-STD-98 Dataset for the solar PV, boost converter, hydro storage system, fuzzy logic and simulation results

This data set provides detailed numerical data on the Life Cycle Inventory (LCI) analysis for assessing and comparing the environmental impact related to the treatment of sewage sludge (SS) in open windrow facilities for the production of compost and in an anaerobic digester for the production of biogas. The collection of the data is included in eleven tables, in which the machinery and duration for biogas and compost production procedures, as well as comparative data regarding the environmental impact category emissions for both scenarios are shown . Furthermore, electricity consumption and embodied energy for each scenario compared to other studies are given. The system boundary conditions were determined with the use of the Gabi software.

This dataset contains raw model results from 2 different scenario's as part of a publication on the the Power-to-H3 concept for system integration in a neigbhourhood energy- and water system. It is being made public to act as supplementary data for publication(s) and the PhD thesis of Els van der Roest. Also, it might be used by other researhers. The dataset was created during model runs in the period between January 2019 - April 2019. Abstract of the paper: In the transition from fossil to renewable energy, the energy system should become clean, while remaining reliable and affordable. Because of the intermittent nature of both renewable energy production and energy demand, an integrated system approach is required that includes energy conversion and storage. We propose a concept for a neighbourhood where locally produced renewable energy is partly converted and stored in the form of heat and hydrogen, accompanied by rainwater collection, storage, purification and use (Power-to-H3). A model is developed to create an energy balance and perform a techno-economic analysis, including an analysis of the avoided costs within the concept. The results show that a solar park of 8.7 MWp combined with rainwater collection and solar panels on roofs, can supply 900 houses over the year with heat (20 TJ) via an underground heat storage system as well as with almost half of their water demand (36,000 m3) and 540 hydrogen electric vehicles can be supplied with hydrogen (90 tonnes). The production costs for both hydrogen (8.7 €/kg) and heat (26 €/GJ) are below the current end user selling price in the Netherlands (10 €/kg and 34 €/GJ), making the system affordable. When taking avoided costs into account, the prices could decrease with 20–26%, while at the same time avoiding 3600 tonnes of CO2 a year. These results make clear that it is possible to provide a neighbourhood with all these different utilities, completely based on solar power and rainwater in a reliable, affordable and clean way.

We present a multi-temporal, multi-modal remote-sensing dataset for predicting how active wildfires will spread at a resolution of 24 hours. The dataset consists of 13.607 images across 607 fire events in the United States from January 2018 to October 2021. For each fire event, the dataset contains a full time series of daily observations, containing detected active fires and variables related to fuel, topography and weather conditions. Documentation WildfireSpreadTS_Documentation.pdf includes further details about the dataset, following Gebru et al.'s "Datasheets for Datasets" framework. This documentation is similar to the supplementary material of the associated NeurIPS paper, excluding only information about experimental setup and results. For full details, please refer to the associated paper. Code: Getting started Get started working with the dataset at https://github.com/SebastianGer/WildfireSpreadTS. The code includes a PyTorch Dataset and Lightning DataModule to allow for easy access. We recommend converting the GeoTIFF files provided here to HDF5 files (bigger files, but much faster). The necessary code is also available in the repository. This work is funded by Digital Futures in the project EO-AI4GlobalChange. The computations were enabled by resources provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS) at C3SE partially funded by the Swedish Research Council through grant agreement no. 2022-06725.

Solar radiation over and under sea ice was measured by an autonomous platform, installed on drifting sea ice in the Arctic Ocean during the SV TARA drift campaign in 2007 (station name 2007R24). The resulting time series describes radiation measurements as a function of place and time between 28 April 2007 and 05 September 2007 in sample intervals of 30 minutes. The radiation measurements have been performed with spectral radiometers. All data are given in full spectral resolution interpolated to 1.0 nm, and integrated over the entire wavelength range (broadband, total: 320 to 950 nm) and photosynthetically active radiation (PAR, 400 to 700 nm). Two sensors, solar irradiance and upward reflected solar irradiance, were mounted on a on a platform about 2 m above the sea ice surface. The third sensor was mounted 1.4 m underneath the sea ice measuring the downward transmitted irradiance. The data set has been processed and contains quality flags for different kinds for erroneous data. Flag values are the sum of individual error codes. The value of 0 refers to no error. Quality flag, sun: If the suns position is close to the horizon, the radiometers measure a very noisy signal. Radiometer measurements and variables which are computed from them are flagged +1 if the sun elevation is below 10 degrees; +2 if the broad band albedo exceeds the threshold 1.05 .

Abstract Energy storage systems (ESS) are widely used in active distribution networks (ADN) to smoothen the drastic fluctuation of renewable energy sources (RES). In order to enhance the scalability and flexibility of ESS, a virtual energy storage system (VESS), which is composed of battery energy storage system (BESS), RES as well as flexible loads (FL), is developed in this paper to realize the functionalities of ESS in more cost-effective way in ADN. Aiming at achieving voltage regulation, dynamic pricing strategies based on system voltage condition are designed for VESS. A distributed real-time power management model containing dynamic pricing strategies is proposed to accomplish the voltage regulation and economic power sharing in VESS. Moreover, a set of distributed algorithms, over time-varying unbalanced directed networks, are designed for dynamic pricing strategies and optimal power management model. Furthermore, the convergence property, optimality and system voltage stability are explained by detailed mathematical analysis. Three various case studies which were ran on a real time digital simulator (OPAL-RT OP5600) were designed to validate the effectiveness of the strategy. Finally, simulation results show that the economic power dispatch and voltage regulation are achieved among VESS simultaneously, even in the presence of time-varying directed and unbalanced communication networks.

Abstract With the rapid advancement in wearable electronics, energy harvesting devices based on triboelectric nanogenerators (TENGs) have been intensively investigated for providing sustainable power supply for them. However, the fabrication of wearable TENGs still remains great challenges, such as flexibility, breathability and washability. Here, a route to develop a new kind of woven-structured triboelectric nanogenerator (WS-TENG) with a facile, low-cost, and scalable electrospinning technique is reported. The WS-TENG is fabricated with commercial stainless-steel yarns wrapped by electrospun polyamide 66 nanofiber and poly(vinylidenefluoride-co-trifluoroethylene) nanofiber, respectively. Triggered by diversified friction materials under a working principle of freestanding mode, the open-circuit voltage, short-circuit current and maximum instantaneous power density from the WS-TENG can reach up to 166 V, 8.5 µA and 93 mW/m2, respectively. By virtue of high flexibility, desirable breathability, washability and excellent durability, the fabricated WS-TENG is demonstrated to be a reliable power textile to light up 58 light-emitting diodes (LED) connected serially, charge commercial capacitors and drive portable electronics. A smart glove with stitched WS-TENGs is made to detect finger motion in different circumstances. The work presents a new approach for self-powered textiles with potential applications in biomechanical energy harvesting, wearable electronics and human motion monitoring.