Abstract
Hydropower is one of the most mature forms of renewable energy generation. The United States (US) has almost 103 GW of installed, with 80 GW of conventional generation and 23 GW of pumped hydropower [1]. Moreover, the potential for future development on Non-Powered Dams is up to 10 GW. With the US setting its goals to become carbon neutral [2], more renewable energy in the form of hydropower needs to be integrated with the grid. Currently, there are no publicly available tool that can estimate the hydropower potential for existing hydropower dams or other non-powered dams. The HydroGenerate is an open-source python library that has the capability of estimating hydropower generation based on flow rate either provided by the user or received from United States Geological Survey (USGS) water data services. The tool calculates the efficiency as a function of flow based on the turbine type either selected by the user or estimated based on the “head” provided by the user.
- Developers:
-
Mitra, Bhaskar [1] ; Gallego-Calderon, Juan [1] ; Elliott, Shiloh [1] ; Mosier, Thomas [1] ; Bastidas Pacheco, Camilo
- Idaho National Lab. (INL), Idaho Falls, ID (United States)
- Release Date:
- 2021-10-19
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Programming Languages:
-
Python
- Version:
- 3.6 or newer
- Licenses:
-
BSD 3-clause "New" or "Revised" License
- Sponsoring Org.:
-
USDOE Office of Energy Efficiency and Renewable Energy (EERE)Primary Award/Contract Number:AC07-05ID14517
- Code ID:
- 66918
- Research Org.:
- Idaho National Laboratory (INL), Idaho Falls, ID (United States)
- Country of Origin:
- United States
- Keywords:
- hydropower; time-series; potential; power; flow rate
Citation Formats
Mitra, Bhaskar, Gallego-Calderon, Juan F., Elliott, Shiloh N., Mosier, Thomas M., and Bastidas Pacheco, Camilo J.
Hydrogenerate: Open Source Python Tool To Estimate Hydropower Generation Time-series.
Computer Software.
https://github.com/IdahoLabResearch/HydroGenerate.
USDOE Office of Energy Efficiency and Renewable Energy (EERE).
19 Oct. 2021.
Web.
doi:10.11578/dc.20211112.1.
Mitra, Bhaskar, Gallego-Calderon, Juan F., Elliott, Shiloh N., Mosier, Thomas M., & Bastidas Pacheco, Camilo J.
(2021, October 19).
Hydrogenerate: Open Source Python Tool To Estimate Hydropower Generation Time-series.
[Computer software].
https://github.com/IdahoLabResearch/HydroGenerate.
https://doi.org/10.11578/dc.20211112.1.
Mitra, Bhaskar, Gallego-Calderon, Juan F., Elliott, Shiloh N., Mosier, Thomas M., and Bastidas Pacheco, Camilo J.
"Hydrogenerate: Open Source Python Tool To Estimate Hydropower Generation Time-series." Computer software.
October 19, 2021.
https://github.com/IdahoLabResearch/HydroGenerate.
https://doi.org/10.11578/dc.20211112.1.
@misc{
doecode_66918,
title = {Hydrogenerate: Open Source Python Tool To Estimate Hydropower Generation Time-series},
author = {Mitra, Bhaskar and Gallego-Calderon, Juan F. and Elliott, Shiloh N. and Mosier, Thomas M. and Bastidas Pacheco, Camilo J.},
abstractNote = {Hydropower is one of the most mature forms of renewable energy generation. The United States (US) has almost 103 GW of installed, with 80 GW of conventional generation and 23 GW of pumped hydropower [1]. Moreover, the potential for future development on Non-Powered Dams is up to 10 GW. With the US setting its goals to become carbon neutral [2], more renewable energy in the form of hydropower needs to be integrated with the grid. Currently, there are no publicly available tool that can estimate the hydropower potential for existing hydropower dams or other non-powered dams. The HydroGenerate is an open-source python library that has the capability of estimating hydropower generation based on flow rate either provided by the user or received from United States Geological Survey (USGS) water data services. The tool calculates the efficiency as a function of flow based on the turbine type either selected by the user or estimated based on the “head” provided by the user.},
doi = {10.11578/dc.20211112.1},
url = {https://doi.org/10.11578/dc.20211112.1},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20211112.1}},
year = {2021},
month = {oct}
}