Cost and performance metrics for individual energy storage technologies which track: cost to procure, install, and connect an energy storage system; associated operational and maintenance costs; and end-of life costs. Technologies are: lithium-ion (lithium iron phosphate and nickel manganese cobalt chemistries); vanadium redox flow; lead acid; pumped storage hydropower; compressed air energy storage; and hydrogen. 2020 data and 2030 estimates of costs and performance are available.
HyMARC assembles expertise in hydrogen science, large-scale computational modeling, and state-of-the-art characterization tools to accelerate discovery of solid-state materials for on-board vehicular hydrogen storage. Some data and tools are shared here. Focus on solid state hydrogen storage materials.
Rights: Energy Technologies Institute Open Licence for Materials
Various scenarios for the UK's power fleet composition in 2030 and 2040 were developed. Dispatch modelling in Plexos was carried out by Baringa on these fleets to investigate the role gas fed plants might have in future. This includes the ability to study load factors, stop/starts etc, and together with concomitant pricing, provide a picture of investment remuneration. The effect of key drivers is studied e.g. gas price. This spreadsheet provides the modelling results for 2030 for Scenarios 3 and 4.
Rights: Energy Technologies Institute Open Licence for Materials
Various scenarios for the UK's power fleet composition in 2030 and 2040 were developed. Dispatch modelling in Plexos was carried out by Baringa on these fleets to investigate the role gas fed plants might have in future. This includes the ability to study load factors, stop/starts etc, and together with concomitant pricing, provide a picture of investment remuneration. The effect of key drivers is studied e.g. gas price.
Rights: Energy Technologies Institute Open Licence for Materials
Various scenarios for the UK's power fleet composition in 2030 and 2040 were developed. Dispatch modelling in Plexos was carried out by Baringa on these fleets to investigate the role gas fed plants might have in future. This includes the ability to study load factors, stop/starts etc, and together with concomitant pricing, provide a picture of investment remuneration. The effect of key drivers is studied e.g. gas price. This spreadsheet contains baseline, and scenario 1,2 and 3 results for aeverage power generation mix, winter mix amd modelled generation duration.
Rights: Energy Technologies Institute Open Licence for Materials
Various scenarios for the UK's power fleet composition in 2030 and 2040 were developed. Dispatch modelling in Plexos was carried out by Baringa on these fleets to investigate the role gas fed plants might have in future. This includes the ability to study load factors, stop/starts etc, and together with concomitant pricing, provide a picture of investment remuneration. The effect of key drivers is studied e.g. gas price. The purpose of this spreadsheet is to provide:
The pathway results (capacity, generation ,costs) for all scenarios. The pathway results are the output of the capacity optimisation model (LT: Long-Term)
Levelised cost of electricity and value of capacity for the Base Case (Model LT: Long-Term)
Detailed dispatch results for the Base Case for a spot year (2030). The dispatch results have been simulated with the full year dispatch model (ST: Short-Term)
Dispatch results for two spot years (2030, 2050) for the rest of the scenarios. These results were simulated with the capacity optimisation model (LT)
Comparisons between the Base Case and sensitivities
Comparisons between Base Case and other GB scenarios
Rights: Energy Technologies Institute Open Licence for Materials
Various scenarios for the UK's power fleet composition in 2030 and 2040 were developed. Dispatch modelling in Plexos was carried out by Baringa on these fleets to investigate the role gas fed plants might have in future. This includes the ability to study load factors, stop/starts etc., and together with concomitant pricing, provide a picture of investment remuneration. The effect of key drivers is studied e.g. gas price. This spreadsheet contains the data supporting the report "Hydrogen Turbines Follow On - Scenario 5 Results Pack - Power sector CCS and H2 Turbine Asset Modelling"
Data collected at the UKGEOS (UK Geoenergy Observatories) facilities including drilling data packs, ongoing monitoring data and experiment results. The data from UKGEOS will apply to geothermal energy, hydrogen, carbon capture and storage, and storage solutions for wind, solar and tidal energy can reduce our carbon emissions.
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