Abstract

The U.S. Energy Information Administration (EIA) conducts a regular survey (form EIA-923) to collect annual and monthly net generation for more than ten thousand U.S. power plants. Approximately 90% of the ~1,500 hydroelectric plants included in this data release are surveyed at annual resolution only and thus lack actual observations of monthly generation. For each of these plants, EIA imputes monthly generation values using the combined monthly generating pattern of other hydropower plants within the corresponding census division. The imputation method neglects local hydrology and reservoir operations, rendering the monthly data unsuitable for various research applications. Here we present an alternative approach to disaggregate each unobserved plant’s reported annual generation using proxies of monthly generation—namely historical monthly reservoir releases and average river discharge rates recorded downstream of each dam. Evaluation of the new dataset demonstrates substantial and robust improvement over the current imputation method, particularly if reservoir release data are available. The new dataset—named RectifHyd—provides an alternative to EIA-923 for U.S. scale, plant-level, monthly hydropower net generation (2001–2020). RectifHyd may be used to support power system studies or analyze within-year hydropower generation behavior at various spatial scales.

Measurement(s)

Hydroelectric Power Generation

Technology Type(s)

Disaggregation informed by hydrological data

Details

Title
Revised monthly energy generation estimates for 1,500 hydroelectric power plants in the United States
Author
Turner, Sean W. D. 1 ; Voisin, Nathalie 2   VIAFID ORCID Logo  ; Nelson, Kristian 1 

 Pacific Northwest National Laboratory, Richland, USA (GRID:grid.451303.0) (ISNI:0000 0001 2218 3491) 
 Pacific Northwest National Laboratory, Richland, USA (GRID:grid.451303.0) (ISNI:0000 0001 2218 3491); University of Washington, Seattle, USA (GRID:grid.34477.33) (ISNI:0000000122986657) 
Publication year
2022
Publication date
2022
Publisher
Nature Publishing Group
e-ISSN
20524463
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2731952175
Copyright
© Battelle Memorial Institute 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.