DOE Data Explorer title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Technical Potential for Hydropower Capacity at Non-powered Dams

Abstract

In the last decade, retrofits of existing nonpowered dams (NPDs) have made up the largest share of capacity increases for US hydropower. Accurate estimates of potential capacity and generation at NPDs help identify sites that may be worth investing in detailed feasibility analyses and design exploration. This dataset consists of NPDs in the conterminous US with at least 100kW of theoretical potential based on earlier resource assessments. Historical daily streamflow (modeled or from USGS gauge records), hydraulic head (based on historical observations or primary purpose and dam height) are the main inputs for HydroGenerate which determines design flow and turbine efficiencies and then calculates nominal capacity, daily generation, and capacity factor. These outputs are summarized on a monthly basis (i.e., generation (MWh) and capacity factor averaged for each month from January to December) and overall (i.e., nominal capacity, average annual generation (MWh), and average annual capacity factor). A total of 4.1 GW capacity is estimated across all 2,564 NPDs included in the dataset.

Authors:
; ; ; ; ;
  1. Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Oak Ridge National Laboratory
Publication Date:
Other Number(s):
1
DOE Contract Number:  
AC05-00OR22725
Research Org.:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Water Power Technologies Office
Subject:
13 HYDRO ENERGY
OSTI Identifier:
2474803
DOI:
https://doi.org/10.21951/2474803

Citation Formats

Hansen, Carly H., Gallego Calderon, Juan, Bastidas Pacheco, Camilo, Davis, Cleve, Mendadhala, Rohit, and Russell, Glenn. Technical Potential for Hydropower Capacity at Non-powered Dams. United States: N. p., 2024. Web. doi:10.21951/2474803.
Hansen, Carly H., Gallego Calderon, Juan, Bastidas Pacheco, Camilo, Davis, Cleve, Mendadhala, Rohit, & Russell, Glenn. Technical Potential for Hydropower Capacity at Non-powered Dams. United States. doi:https://doi.org/10.21951/2474803
Hansen, Carly H., Gallego Calderon, Juan, Bastidas Pacheco, Camilo, Davis, Cleve, Mendadhala, Rohit, and Russell, Glenn. 2024. "Technical Potential for Hydropower Capacity at Non-powered Dams". United States. doi:https://doi.org/10.21951/2474803. https://www.osti.gov/servlets/purl/2474803. Pub date:Tue Oct 01 00:00:00 EDT 2024
@article{osti_2474803,
title = {Technical Potential for Hydropower Capacity at Non-powered Dams},
author = {Hansen, Carly H. and Gallego Calderon, Juan and Bastidas Pacheco, Camilo and Davis, Cleve and Mendadhala, Rohit and Russell, Glenn},
abstractNote = {In the last decade, retrofits of existing nonpowered dams (NPDs) have made up the largest share of capacity increases for US hydropower. Accurate estimates of potential capacity and generation at NPDs help identify sites that may be worth investing in detailed feasibility analyses and design exploration. This dataset consists of NPDs in the conterminous US with at least 100kW of theoretical potential based on earlier resource assessments. Historical daily streamflow (modeled or from USGS gauge records), hydraulic head (based on historical observations or primary purpose and dam height) are the main inputs for HydroGenerate which determines design flow and turbine efficiencies and then calculates nominal capacity, daily generation, and capacity factor. These outputs are summarized on a monthly basis (i.e., generation (MWh) and capacity factor averaged for each month from January to December) and overall (i.e., nominal capacity, average annual generation (MWh), and average annual capacity factor). A total of 4.1 GW capacity is estimated across all 2,564 NPDs included in the dataset.},
doi = {10.21951/2474803},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Oct 01 00:00:00 EDT 2024},
month = {Tue Oct 01 00:00:00 EDT 2024}
}