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Dynamic Earth Energy Storage: Terawatt-year, Grid-scale Energy Storage Using Planet Earth as a Thermal Battery (GeoTES): Phase I Project Final Report

Sep 02, 2022
18.25 MB
Publicly accessible
Final report for the DOE GTO funded research on geologic thermal energy storage (GeoTES), or commonly known as reservoir thermal energy storage (RTES). The results described in this report shed light on various aspects of RTES including project siting, operational performance, m...
Authors
Atkinson, T. et al Idaho National Laboratory
geothermalenergyrtesgeotesstoragethermalmodelingtoughmoosegeochemistrytesthermal energy storageearth energy storagegrid-scalegeologicreservoir

District Geothermal Energy Systems with Seasonal Underground Thermal Storage: Load Profiles, Modeling Workflow, and Techno-Economic Results for U.S. Screening

Sep 27, 2025
135.99 MB
Curated
This dataset accompanies the paper "Geothermal district energy systems coupled with seasonal underground thermal energy storage: a U.S. techno-economic screening by climate and geology." It contains the data and scripts required to reproduce the study's results across ten U.S. cit...
Authors
Mello, S. et al National Laboratory of the Rockies
geothermalenergydistrict energy systemsunderground thermal energy storageutesatesrtesground heat exchangerghesutratecho-economic analysisenergy modelingcomstockbuilding load profileslcoeaquifer storageseasonal storageu.s. citiespythonmodelscriptsmodel datasubsurface simulationgeotescold utes

Dynamic Earth Energy Storage: Terawatt-Year, Grid-Scale Energy Storage using Planet Earth as a Thermal Battery (GeoTES): Seedling Project Final Report

May 31, 2019
4.48 MB
Curated
Grid-scale energy storage has been identified as a needed technology to support the continued build-out of intermittent renewable energy resources. As of April 2017, the U.S. had approximately 24.2 GW of energy storage on line, compared to 1,081 GW of installed generation capacity...
Authors
McLing, T. et al Idaho National Laboratory
energy storagegeothermal energythermal energy storagegeotestestemperatureporositymodelingsteamrankine cycleflue gasheatrecoverythermalhydrologylinear stabilitydirect usegoethermalbrinegrid stabilizationinjection testweber formationweber sandstonertes

Reactive Transport Simulations of High-Tempertature Geologic Thermal Energy Storage (GeoTES) in Deep Saline Formations I/O Files

Dec 31, 2021
702.26 MB
Publicly accessible
Simulation input and output files, post-processed figures and excel tables, and tecplot layout files for generating figures. These simulations were run with TOUGHREACT V4.12 by Lawrence Berkeley National Laboratory in 2021. This work was completed as part of the geologic thermal e...
Authors
Spycher, N. and Doughty, C. Lawrence Berkeley National Laboratory
energytesgeotesthermal energy storagereactive transporttoughreactgeothermalsimulationmodelinghigh-temperatureinput filesoutput filesdeep saline formationshigh tempgeologicrtesinputoutput

Dataset and SUTRA model used to evaluate Reservoirs for Thermal Energy Storage in the Portland Basin, Oregon.

Jun 29, 2020
Size unavailable
Publicly accessible
This is a link to the open access, published dataset and modeling that supports a feasibility study of Reservoir Thermal Energy Storage (RTES) in the Portland Basin, Oregon, USA. Citation: Burns, E.R., 2020, SUTRA model used to evaluate Saline or Brackish Aquifers as Reservoirs f...
Authors
Bershaw, J. et al Portland State University
geothermalenergymodelrtesportland basinoregonsutratessimulationviscositydensityflowstimulationhydraulicaquifersalinebrackishporositywell-spacingwell datadistrict heatingheatingcoolingthermal efficiencyheat fluxtemperaturevolumepumping rate

Machine Learning-Assisted High-Temperature Reservoir Thermal Energy Storage Optimization: Numerical Modeling and Machine Learning Input and Output Files

Apr 15, 2022
113.79 MB
Publicly accessible
This data set includes the numerical modeling input files and output files used to synthesize data, and the reduced-order machine learning models trained from the synthesized data for reservoir thermal energy storage site identification. In this study, a machine-learning-assiste...
Authors
Jin, W. et al Idaho National Laboratory
reservoir thermal energy storagestochastic simulationgeotesmachine learningmodelingtesht-rtescharacterizationnumerical modelstochastichydrogeologic formationsimulated datasimulation datahigh-temperaturethermal energy storageoptimizationartificial neural network regressionannneural networkoperation scenariosseasonal-cyclepareto frontsseasonal operationcontinuous operationfalconmoose