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42 results for “Energy Communities”
Costs and Benefits of Energy Communities - Collection of Literature
<p>The files contain references to studies of different impacts of energy communities, based on the collection reviewed in Berka & Creamer (2018) and with some additions. The typology of impacts differs from that used by Berka and Creamer.</p>
Supplementary material for the publication: J.D. Nixon, K. Bhargava and E. Gaura, Energy Performance Gap in Community-Based Solar Energy Interventions: Lessons from two Rwandan Refugee Camps, 2020
<p>The dataset deposited here was prepared under the EPSRC-funded <a href="http://heed-refugee.coventry.ac.uk/">Humanitarian Engineering and Energy for Displacement</a> research project (EP/P029531/1). The project aimed to understand energy needs of displaced communities, create an evidence base on the usage of different energy interventions and provide recommendations for improved design of future energy interventions to better meet the needs of people. </p> <p>As part of the project, we deployed a Standalone Solar System for a Community Hall in Nyabiheke camp, Rwanda, and a PV-battery Microgrid in Kigeme camp, Rwanda. The microgrid supplies power to a playground and two nursery buildings. It powers a total of 20 CPE (each with 3 LEDs) and 10 sockets. The standalone system at Hall powers 7 CPE (with 3 LEDs each) and 4 sockets. The aim of the study was to (a) understand the energy consumption behaviour, light usage and other enabled uses within the set location in each camp (b) create an evidence base on the value of energy and its benefits in displaced contexts (c) identify best practice in the construction, control and operation of the respective systems as a shared energy resource.</p> <p>The system data used for the performance analysis for this study (July 2019 and March 2020) is deposited here along with the metadata. The results from analysis are presented in a paper titled '<strong>Energy Performance Gap in Community-Based Solar Energy Interventions: Lessons from two Rwandan Refugee Camps</strong>' (currently under submission). The scripts for analysis can be found at our Github account <a href="https://github.com/cogent-computing">Cogent Labs</a> under HEED-Microgrid and HEED-Hall repositories.</p>
Dataset: Analysis of Multidimensional Energy Poverty in the Carmen Soler Community - Limpio, Republic of Paraguay
<p><i><strong>"Analysis of Multidimensional Energy Poverty in the Carmen Soler Community - Limpio, Republic of Paraguay"</strong></i></p><p><i>CHILECON 2023 - </i><a href="https://site.ieee.org/chilesur/ieee-chilecon-2023/"><i>https://site.ieee.org/chilesur/ieee-chilecon-2023/</i></a></p><p>---</p><p>En el marco del trabajo de referencia, los autores ponemos a disposición de los lectores la base de datos utilizada para el cálculo del Índice de Pobreza Energética Multidimensional (MEPI) para el caso de estudio. </p><ol><li>MEPI_CarmenSoler_Data_2018_CHILECON2023.xlsx</li></ol><p>En el archivo, podrán encontrar los extraídos de los resultados de la encuesta realizada en el 2018 por un equipo de investigadores paraguayos (En el artículo podrán encontrar más información). Además de los datos, podrán ver todos los pasos y cálculos llevados a cabo para obtener los resultados obtenidos. </p><p>El material fue puesto a disposición de todos los interesados para fines académicos y científicos. </p><p>Atte. </p><p>Los autores. </p><p>---</p>
Business Models in Energy Communities: an analysis through legal lenses
<p>This research explores energy communities (EC) and their business models’ attributes. We develop a conceptual framework, which combines and extends the social, economic, environmental, and technological dimensions of value generation to include the legal dimension. The latter has been considered only implicitly in previous studies on this sector. Applying this framework to forty business cases of energy communities allows to identify six business model (BM) archetypes representative of ECs. This study can encourage and support new ventures in this sector to model their strategy and comply with the requirements.</p>
Costless renewable energy distribution model based on cooperative game theory for energy communities considering its members' active contributions
<p>This dataset was used in the case study of the following publication:</p> <p> - Luis Gomes, Zita Vale, "Costless renewable energy distribution model based on cooperative game theory for energy communities considering its members’ active contributions," Sustainable Cities and Society, Volume 101, 2024, 105060, ISSN 2210-6707, <a href="https://doi.org/10.1016/j.scs.2023.105060">https://doi.org/10.1016/j.scs.2023.105060</a> </p> <p><em>(if you used this dataset in your publications, please send us your information so we can add your publication to the list above)</em></p> <p> </p> <p>The dataset is composed by energy generation, consumption, and forecast (for generation, and for consumption) expressed in Wh. The data considers an energy community of 10 prosumers in 30 days.</p> <p>The dataset also has energy prices that have been collected from MIBEL (Iberian Electricity Market).</p> <p> </p> <p>We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Zenodo publication to cite this work.</p>
Papers and KPIs for the Evaluation of Renewable Energy Communities' Performance
<p>This database contains the methodology used to explore and identify the papers that containes KPIs related to the evaluation of RECs performance. This methodology is divided into three phases: </p> <p>1.1) <em>Papers Exploration – </em>Comprehensive search of papers in the field of RECs using Scopus and Web Of Science databases;</p> <p>1.2) <em>Papers Screening</em> – Initial screening of collected literature based on research domain and accessibility;</p> <p>1.3) <em>Papers Eligibility</em> – Further filtering papers by extracting those that explicitly define KPIs through mathematical formulations in the context of the RECs.</p> <p> In the <em>Papers Exploration</em> step, the authors conducted a systematic review of the state-of-the-art of literature on performance metrics in the context of the renewable energy community. The search was conducted in March 2024 using the search engines Scopus and Web Of Science (the used queries are detailed explain in thte database). The output of this phase is a large database of the most recent and relevant studies, cataloged by the following information: authors, article title, abstract, author keywords, index keywords, and year of publication. At this stage, only journal articles and research works published after 2010 were considered. In the <em>Papers Screening</em> phase, the articles are further filtered by the authors screening manually all papers based on keywords, titles, and abstracts, removing articles not relevant to the context of the RECs. In addition, articles for which it was not possible to access the full text are excluded. In the <em>Papers Eligibility</em> phase, the articles are entirely read to identify those articles that directly address the use of performance metrics. The eligibility criterion used by the reviewers’ team refers to the explicit definition of KPIs through mathematical formulas combined with their direct usage to evaluate RECs’ performances. The main objective of this phase is therefore to identify those articles that explicitly define and use KPIs, so that they can later be collected and labeled, based on their definition and usage.<br><br></p> <p>In additions, KPIs are extracted from the papers deemed elegible generating Tables A1, A2, A3 and A4. In these tables, similar KPIs are aggregated together in one single mathematical definition based on the methodology described in <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4991932">Key Performance Indicators for Renewable Energy Communities: A Comprehensive Review by Lorenzo Giannuzzo, Minuto Francesco Demetrio, Daniele Salvatore Schiera, Samuele Branchetti, Carlo Petrovich, Angelo Frascella, Nicola Gessa, Andrea Lanzini :: SSRN</a></p>
FRESH:COM Dynamic Participation in Local Energy Communities with Peer-to-Peer Trading
<p>This repository contains input data used for the case study in 'Dynamic Participation in Local Energy Communities with Peer-to-Peer Trading'</p>
Inflation Reduction Act Energy Communities
<p>The Inflation Reduction Act of 2022 (IRA) became law on August 8, 2022. Under the law, new qualifying renewable and/or carbon-free electricity generation projects constructed in certain areas of the US, called energy communities, are eligible for bonus worth an additional 10% to the value of the production tax credit or a 10 percentage point increase in the value of the investment tax credit. The IRA does not explicitly map or list these specific communities. Instead, eligible communities are defined by a series of qualifications:</p> <ol> <li>a brownfield site,</li> <li>a metropolitan statistical area (MSA) or non-metropolitan statistical area with either (a) 0.17% or greater employment <em>or</em> (b) 25% or greater local tax revenues related to the extraction, processing, transport, or storage of coal, oil, or natural gas; <em>and</em> an unemployment rate at or above the national average for the previous year, or</li> <li>a census tract containing or adjacent to (a) a coal mine closed after December 31, 1999 or (b) a coal-fired electric generating unit retired after December 31, 2009.</li> </ol> <p>These maps and data layers contain GIS data for coal mines, coal-fired power plants, fossil energy related employment, and brownfield sites. Each record represents a point, tract or metropolitan statistical area and non-metropolitan statistical area with attributes including plant type, operating information, GEOID, etc. The input data used includes:</p> <ol> <li>Brownfields – Source: <a href="https://www.epa.gov/frs/geospatial-data-download-service">EPA</a>. No analysis was performed on this data layer. However, tract polygon layers have a column denoting brownfield presence (0 for no brownfield site, 1 if the tract contains a brownfield somewhere within the polygon).</li> <li>Eligible Employment MSAs (“Final_Employment_Qualifying_MSAs”) – Source: US Census <a href="https://www.census.gov/programs-surveys/cbp.html">County Business Patterns</a>. MSAs and non-MSA regions with employment over 0.17% in the fossil fuel industry (defined here as NAICS codes 211, 2121, 213, 23712, 324, 4247, and 486) and unemployment greater than or equal to 3.9% (the average national unemployment rate in 2021, according to the Bureau of Labor Statistics).</li> </ol> <p>--Possibly Eligible MSAs (“FossilFuel_Employment_Qualifying_MSAs”) are MSA and non-MSA regions that meet or exceed the 0.17% employment in the fossil fuel industry threshold but do not exceed the unemployment threshold.</p> <p>--Relevant columns include:</p> <p> a) SUM_nhgis0: Total employment in 2020.</p> <p> b) SUM_nhgis1: Total unemployment in 2020.</p> <p> c) P_Unemp: Percent unemployment in 2020.</p> <p> d) Q_Unemp: Boolean column indicating if the MSA or non-MSA’s unemployment rate is at or above the national average of 3.9%.</p> <p> e) FF_Qual: Boolean column indicating if the MSA or non-MSA had employment in the fossil fuel industry at or above 0.17% in the past 11 years.</p> <p> f) final_Qual: Boolean column indicating if an MSA or non-MSA qualifies for both unemployment rate and fossil fuel employment under the IRA.</p> <ol> <li>Retired Power Plants – Source: EIA via <a href="https://hifld-geoplatform.opendata.arcgis.com/maps/ee0263bd105d41599be22d46107341c3/about">HFLID</a>. Qualifying power plants were selected by use of coal in at least one generator, and if they were retired (RET_DATE) on or after January 1, 2010. This data goes through December 2021.</li> </ol> <p>--Adjacent tract data was derived by Cecelia Isaac using ESRI ArcGIS Pro.</p> <ol> <li>Abandoned Coal Mines – Source: <a href="https://www.msha.gov/mine-data-retrieval-system">MSHA</a>. Mines labeled “Abandoned”, “Abandoned and Sealed” or “NonProducing” between January 1, 2000 and September 2022.</li> </ol> <p>--Adjacent tract data was derived by Cecelia Isaac using ESRI ArcGIS Pro.</p> <p>5) US State Borders– Source: <a href="https://data2.nhgis.org/main">IPUMS NHGIS</a>.</p> <p> </p> <p>Also included here are polygon shapefiles for Onshore <a href="https://zenodo.org/record/5021146#.Y0XbRnbMK39">Wind and Solar Candidate Project Areas</a> from <a href="https://repeatproject.org/">Princeton REPEAT</a>. These files have been updated to include columns related to the energy communities.</p> <p>New columns include:</p> <ol> <li>CoalPlantTract: Boolean column indicating if the CPA is within a tract that qualifies because of a retired coal plant.</li> <li>CoalMineTract: Boolean column indicating if the CPA is within a tract that qualifies because of a closed coal mine.</li> <li>FossilFuelEmp: Boolean column indicating if the CPA is within an MSA or non-MSA with greater than or equal to 0.17% employment in the fossil fuel industry.</li> <li>UnempQualification: Boolean column indicating if the CPA is within an MSA or non-MSA with greater than or equal to 0.17% employment in the fossil fuel industry.</li> <li>MSA_non_to: The code of the MSA or non-MSA area that contains the CPA.</li> <li>P_Unemp: The percent unemployment of the MSA or non-MSA that contains the CPA in 2021.</li> </ol>
Triggering Sustainable Biogas Energy Communities through Social Innovation- ISABEL ---- Social Innovation and Community energy best preactices, methods and tools across Europe ----Semi-structured interviews from communities
<p>Having identified through the literature review various success and failure factors for social innovation applied to community energy projects, ISABEL has further conducted 18 semi-structured interviews of a range of stakeholders. The interviewee sample was a convenience sample of participants in existing projects and thus, inevitably, they are able to speak more to successful than unsuccessful projects and they likely have had less exposure to obstacles to the success of their projects. T The interviews have focused on identifying answers to the questions: <em>What were the key success factors? What obstacles were overcome? How? Participants were also asked to specify the type of renewable energy and community energy model. </em></p>
HEMStoEC: Home Energy Management Systems to Energy Communities DataSet
<p>The building sector is responsible for about 1/3 of all final energy consumed in the world. It is also responsible for about 30 % of CO<sup>2</sup> emissions from the end-use sector when accounting for indirect emissions from the use of electricity and heat in buildings. Focusing on EU, the use of electricity to satisfy the loads of lighting and most electrical appliances represents about 14.5 % of the energy consumed in residential sector, excluding heating and cooling systems, and 24.8 % including the latter. Therefore, we are in the presence of a sector that has a significant weight in the final energy consumption figures. Thus, innovative energy initiatives should contribute towards reducing energy consumption, reducing the effects on the climate, and achieving greater energy efficiency. These initiatives begin to emerge to a certain extent from small consumers, as they become more aware of environmental issues, either isolated or grouped in an energy community, where generated or stored energy is shared between stakeholders. In addition, energy markets go through a transition period and begin to give way, recognize, and promote the emerging role of prosumers (producers+consumers).</p> <p>It is within this context that this dataset is introduced. It allows, for a single prosumer, to:</p> <ol> <li>Test and validate different control strategies for home energy management systems;</li> <li>Design forecasting energy consumption models;</li> <li>Design forecasting PV energy generation models;</li> <li>Test and validate different non-invasive load monitoring (NILM) algorithms;</li> <li>Design forecasting thermal comfort models, as well as test and validate control strategies for Heating, Ventilation and Air Conditioning (HVAC) systems.</li> </ol> <p>Additionally, for a community of 4 houses, it allows to:</p> <ol> <li>Test and validate different control strategies for the community energy management system;</li> <li>Design forecasting community energy consumption models;</li> <li>Test and validate transfer learning strategies for NILM.</li> </ol> <p>The data, spanning more than three years, is stored in Matlab -v7 format, . This allows to be read by other languages, such as python.</p>
Metabolizable energy and biomass of plants consumed by caribou (Rangifer tarandus) in tundra communities of northern Alaska and deer (Odocoileus spp.) in forests and grasslands of Washington, United States of America
<p>A ubiquitous interaction operates at the base of food webs in many terrestrial ecosystems of the world, creating the foundation for bottom-up regulation of consumers. This interaction plays out as follows. Populations of herbivores deplete plant biomass by foraging. Increasing herbivore population size intensifies this depletion, which in turn, creates a negative feedback regulating herbivore population growth. Large herbivores and the plants they consume offer a useful system for studying this interaction because populations of large herbivores are often regulated by density dependence, defined as the reduction in the per-capita growth rate that occurs as populations grow. Diminished body mass of individuals has been repeatedly observed in high-density populations, implicating plant-mediated, diminished nutrition as the primary cause of density dependence. However, there is no general explanation for why these nutritional deficiencies occur. The data deposited here were used to demonstrate fit new model of the feedbacks from plant biomass to herbivores. The model shows how reduced nutrition of herbivores can result from increased dilution of metabolizable energy in the plant tissue they consume as populations grow even when a large fraction of the consumable plant biomass remains uneaten. This result provides a tidy, mechanistic explanation for bottom-up control of population dynamics of primary consumers in a "green world." </p>
Data from: Fungal energy channeling sustains soil animal communities across forest types and regions
Open the record for dataset details and reuse information.
Intermediate habitat fragmentation buffers droughts: How individual energy dynamics mediate mammal community response to stressors
Open the record for dataset details and reuse information.
Metabolizable energy and biomass of plants consumed by caribou (Rangifer tarandus) in tundra communities of northern Alaska and deer (Odocoileus spp.) in forests and grasslands of Washington, United States of America
Open the record for dataset details and reuse information.
Community Land Model version 4.5 (CLM4.5) simulations of water, energy, and carbon fluxes for Saddle vegetation communities, 2008 - 2013
Single point simulations of CLM4.5 that include (1) forcing data that were input to the model and subsequent (2) model output for simulations that approximate conditions in fellfield, dry meadow, moist meadow, wet meadow, and snowbed vegetation communities. Forcing data were generated with observed atmospheric conditions from Tvan, Saddle precipitation, and incoming shortwave radiation measured from the AmeriFlux tower site (US-NR1) from 2008-2013. Wintertime precipitation inputs were modified to approximate average snow depth for each vegetation community observed across the Saddle grid. Land models, like CLM, provide a cohesive framework to investigate biogeophysical and biogeochemical effects of environmental change on ecosystem processes. We used CLM4.5 to investigate if a global-scale model can represent local-scale patterns of water, energy, and carbon fluxes in a heterogeneous mountain environment. Specifically, we were interested in generating testable projections of potential ecosystem responses to climate change. Model output includes half-hourly data on fluxes of energy, water, and carbon, as well as vegetation carbon stocks and edaphic conditions. We also conducted sensitivity analyses to look at ecosystem responses to modifications intended to extend growing season length by decreasing snow albedo and warming air temperatures (black sand and M-A warm, respectively). Information on the variables, units, and data are included as attributed in the network Common Data Form (NetCDF) files for this dataset. For users unfamiliar with using NetCDF files, we have included R scripts that write (forcing data) and read (model output) .nc files include in this data archive. More information about NetCDF files is available at http://www.unidata.ucar.edu/software/netcdf/docs/index.html.
Data from: High rates of carbon and dinitrogen fixation suggest a critical role of benthic pioneer communities in the energy and nutrient dynamics of coral reefs
<p>1. Following coral mortality in tropical reefs, pioneer communities dominated by filamentous and crustose algae efficiently colonize substrates previously occupied by coral tissue. This phenomenon is particularly common after mass coral mortality following prolonged bleaching events associated with marine heatwaves.</p> <p>2. Pioneer communities play an important role for the biological succession and reorganization of reefs after disturbance. However, their significance for critical ecosystem functions previously mediated by corals, such as the efficient cycling of carbon (C) and nitrogen (N) within the reef, remains uncertain.</p> <p>3. We used 96 carbonate tiles to simulate the occurrence of bare substrates after disturbance in a coral reef of the central Red Sea. We measured rates of C and dinitrogen (N<sub>2</sub>) fixation of pioneer communities on these tiles monthly over an entire year. Coupled with elemental and stable isotope analyses, these measurements provide insights into macronutrient acquisition, export, and the influence of seasonality.</p> <p>4. Pioneer communities exhibited high rates of C and N<sub>2</sub> fixation within 4 – 8 weeks after the introduction of experimental bare substrates. Ranging from 13 to 25 μmol C cm<sup>−2</sup> d<sup>−1</sup> and 8 to 54 nmol N cm<sup>−2</sup> d<sup>−1</sup>, respectively, C and N<sub>2</sub> fixation rates were comparable to reported values for established Red Sea coral reefs. This similarity indicates that pioneer communities may quickly compensate for the loss of benthic productivity by corals. Notably, between 40 and 85% of fixed organic C was exported into the environment, constituting a vital source of energy for the coral reef food web.</p> <p>5. Our findings suggest that benthic pioneer communities may play a crucial, yet overlooked role in the C and N dynamics of oligotrophic coral reefs by contributing to the input of new C and N after coral mortality. While not substituting other critical ecosystem functions provided by corals (e.g. structural habitat complexity and coastal protection), pioneer communities likely contribute to maintaining coral reef nutrient cycling through the accumulation of biomass and import of macronutrients following coral loss.</p>
Long-term nitrogen addition alters the community and energy channel but not diversity of soil nematodes in a subtropical forest
Summary <ol> <li>Research has indicated that increases in nitrogen (N) deposition can greatly affect ecosystem processes and functions. There is limited information about the effects of long-term N addition on soil nematodes and their functional composition, although nematodes are the most abundant multicellular animals on Earth.</li> <li>We conducted a field experiment in 2004 with four levels of N addition (0, 60, 120, and 240 kg N ha<sup>-1 </sup>yr<sup>-1</sup>) in a subtropical <i>Cunninghamia lanceolata</i> forest. Soil samples with three depths (0-20, 20-40 and 40-60 cm) were collected and the community structure, diversity and trophic groups of soil nematodes were determined in 2014.</li> <li>N addition significantly increased the abundance of bacterial- and fungal-feeding nematodes, but decreased the abundance of plant-feeding nematodes at the 0-20 cm soil layer. Accordingly, the plant parasite index and enrichment index decreased but the basal index and channel index increased, which weaken the importance of the plant-based energy channel, but enhance the importance of the fungal-based energy channel. N addition had no effects on the diversity of soil nematodes in three soil depths. Structural equation modeling analysis indicated that N loading directly changed plant-feeding (total <i>r<sup>2</sup></i>=0.42) nematodes, or indirectly affected bacterial- (<i>r<sup>2</sup></i>=0.43), fungal- (<i>r<sup>2</sup></i>=0.31) and plant-feeding nematodes via change soil nutrients, soil water content and pH.</li> <li>These findings suggest that N addition can change the community structure and energy channels soil nematodes, which would affect soil processes and food web functions in forest soils under future environmental change scenarios.</li> </ol>
Sample Records. Citizen Science and Citizen Energy Communities: A Systematic Review of Potential Alliances for SDGs
<p>Sample Records. Citizen Science and Citizen Energy Communities: A Systematic Review of Potential Alliances for SDGs</p>
SI for QUESTDB: a database of highly-accurate excitation energies for the electronic structure community
<p>Cartesian coordinates of each molecule (in bohr), Python code associated with the algorithm employed to compute the extrapolated FCI excitation energies and their associated error bars (as well as additional examples for smaller systems), a detailed discussion of each molecule of the QUEST\#5 subset including comparisons with literature data, Excel spreadsheet gathering all benchmark data and additional statistical analyses for various molecular and excitation subsets.</p>
Optimal Control of Renewable Energy Communities with Controllable Assets: consumption and production profiles
<p>consumption and production profiles for cases I and II used for computing simulations in Optimal Control of Renewable Energy Communities with Controllable Assets</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.