AI Article Synopsis

  • The U.S. is rapidly expanding renewable energy, but there's a need for long-term hourly datasets on electricity production to ensure grid reliability and resource adequacy.
  • Recent datasets of simulated meteorological variables lack coverage in the energy domain, prompting the creation of a new dataset that spans 43 years of wind and solar power production data at the plant level.
  • This new dataset aligns well with existing reports, showing minimal bias and offering a valuable resource for studying the impacts of climate change and evaluating renewable energy systems on a broader scale.

Article Abstract

As renewable energy continues its rapid expansion in the Unites States, multi-decadal hourly datasets of electricity production are needed to asses reliability and resource adequacy of power grids. Recent years have seen the release of grid-cell-level simulated meteorological variables, however these are not extended to the power domain, are not developed from a dynamically consistent numerical weather model, and only cover a historical baseline of less than a decade. To fill this gap, this work provides a dataset of 43 years of coincident plant-level wind and solar power production data. The dataset is designed to be aggregated to appropriate scales of interest for bulk system studies such as Balancing Authorities (BAs), states, and nodes of a production cost model. The dataset covers every plant in the contiguous U.S. that is reported in the U.S. Energy Information Administration (EIA) Form 860 as of 2020. When compared with the EIA-923 monthly generation, we find minimal bias (less than 5%). When compared with BA-reported hourly generation, we find low bias in solar (less than 7%), and slight underdispersion in wind. This coincident multi-decadal historical dataset provides a documented and evaluated multi-resource baseline for studies on reliability, resource adequacy, climate change impacts, and characterization of emergent climate threats on renewable resources.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11470116PMC
http://dx.doi.org/10.1038/s41597-024-03894-wDOI Listing

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Article Synopsis
  • The U.S. is rapidly expanding renewable energy, but there's a need for long-term hourly datasets on electricity production to ensure grid reliability and resource adequacy.
  • Recent datasets of simulated meteorological variables lack coverage in the energy domain, prompting the creation of a new dataset that spans 43 years of wind and solar power production data at the plant level.
  • This new dataset aligns well with existing reports, showing minimal bias and offering a valuable resource for studying the impacts of climate change and evaluating renewable energy systems on a broader scale.
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Evaluation of precipitation across the contiguous United States, Alaska, and Puerto Rico in multi-decadal convection-permitting simulations.

Sci Rep

January 2024

Environmental Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Building 240, Lemont, IL, 60439, USA.

This study is an early effort to generate a multi-decadal convection-permitting regional climate dataset that covers nearly the entire North American continent. We assessed a 20 year dynamically downscaled regional climate simulation at a 4 km spatial resolution with explicit convection across the contiguous United States (CONUS), Alaska, and Puerto Rico. Specifically, we evaluated the model's performance in representing mean, 95th percentile, and extreme precipitation across regions.

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In 2016, the National Oceanic and Atmospheric Administration deployed the first iteration of an operational National Water Model (NWM) to forecast the water cycle in the continental United States. With many versions, an hourly, multi-decadal historic simulation is made available to the public. In all released to date, the files containing simulated streamflow contain a snapshot of model conditions across the entire domain for a single timestep which makes accessing  time series a technical and resource-intensive challenge.

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