Cost-Optimal Pathways to 75% Fuel Reduction in Remote Alaskan Villages: Preprint
Abstract
There are thousands of isolated, diesel-powered microgrids that deliver energy to remote communities around the world at very high energy costs. The Remote Communities Renewable Energy program aims to help these communities reduce their fuel consumption and lower their energy costs through the use of high penetration renewable energy. As part of this program, the REopt modeling platform for energy system integration and optimization was used to analyze cost-optimal pathways toward achieving a combined 75% reduction in diesel fuel and fuel oil consumption in a select Alaskan village. In addition to the existing diesel generator and fuel oil heating technologies, the model was able to select from among wind, battery storage, and dispatchable electric heaters to meet the electrical and thermal loads. The model results indicate that while 75% fuel reduction appears to be technically feasible it may not be economically viable at this time. When the fuel reduction target was relaxed, the results indicate that by installing high-penetration renewable energy, the community could lower their energy costs by 21% while still reducing their fuel consumption by 54%.
- Authors:
- Publication Date:
- Research Org.:
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Sponsoring Org.:
- U.S. Department of Interior
- OSTI Identifier:
- 1225935
- Report Number(s):
- NREL/CP-7A40-64491
- DOE Contract Number:
- AC36-08GO28308
- Resource Type:
- Conference
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 29 ENERGY PLANNING, POLICY, AND ECONOMY; REopt; microgrid; remote community; renewable energy; diesel renewable hybrid power systems
Citation Formats
Simpkins, Travis, Cutler, Dylan, Hirsch, Brian, Olis, Dan, and Anderson, Kate. Cost-Optimal Pathways to 75% Fuel Reduction in Remote Alaskan Villages: Preprint. United States: N. p., 2015.
Web. doi:10.1109/SusTech.2015.7314334.
Simpkins, Travis, Cutler, Dylan, Hirsch, Brian, Olis, Dan, & Anderson, Kate. Cost-Optimal Pathways to 75% Fuel Reduction in Remote Alaskan Villages: Preprint. United States. https://doi.org/10.1109/SusTech.2015.7314334
Simpkins, Travis, Cutler, Dylan, Hirsch, Brian, Olis, Dan, and Anderson, Kate. 2015.
"Cost-Optimal Pathways to 75% Fuel Reduction in Remote Alaskan Villages: Preprint". United States. https://doi.org/10.1109/SusTech.2015.7314334. https://www.osti.gov/servlets/purl/1225935.
@article{osti_1225935,
title = {Cost-Optimal Pathways to 75% Fuel Reduction in Remote Alaskan Villages: Preprint},
author = {Simpkins, Travis and Cutler, Dylan and Hirsch, Brian and Olis, Dan and Anderson, Kate},
abstractNote = {There are thousands of isolated, diesel-powered microgrids that deliver energy to remote communities around the world at very high energy costs. The Remote Communities Renewable Energy program aims to help these communities reduce their fuel consumption and lower their energy costs through the use of high penetration renewable energy. As part of this program, the REopt modeling platform for energy system integration and optimization was used to analyze cost-optimal pathways toward achieving a combined 75% reduction in diesel fuel and fuel oil consumption in a select Alaskan village. In addition to the existing diesel generator and fuel oil heating technologies, the model was able to select from among wind, battery storage, and dispatchable electric heaters to meet the electrical and thermal loads. The model results indicate that while 75% fuel reduction appears to be technically feasible it may not be economically viable at this time. When the fuel reduction target was relaxed, the results indicate that by installing high-penetration renewable energy, the community could lower their energy costs by 21% while still reducing their fuel consumption by 54%.},
doi = {10.1109/SusTech.2015.7314334},
url = {https://www.osti.gov/biblio/1225935},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Wed Oct 28 00:00:00 EDT 2015},
month = {Wed Oct 28 00:00:00 EDT 2015}
}