|
|
| Line 28: |
Line 28: |
| | |deterministic=stochastic optimization, moeling-to-generate alternatives | | |deterministic=stochastic optimization, moeling-to-generate alternatives |
| | |is_suited_for_many_scenarios=Yes | | |is_suited_for_many_scenarios=Yes |
| − | |number_of_variables=depends on input dataset
| + | |computation_time_minutes=5 |
| − | |computation_time_minutes=< 5 mins | + | |
| | |computation_time_comments=varies with chosen solver | | |computation_time_comments=varies with chosen solver |
| | |citation_references=Hunter, K., Sreepathi, S., DeCarolis, J. F. (2013). Modeling for insight using tools for energy model optimization and analysis (TEMOA). Energy Economics, 40, 339-349. | | |citation_references=Hunter, K., Sreepathi, S., DeCarolis, J. F. (2013). Modeling for insight using tools for energy model optimization and analysis (TEMOA). Energy Economics, 40, 339-349. |
Revision as of 16:14, 4 August 2015
|
Tools for Energy Model Optimization and Analysis
by NC State University
Authors:
Joe DeCarolis, Kevin Hunter, Binghui Li, Sarat Sreepathi
Contact:
Joe DeCarolis
|
Tools for Energy Model Optimization and Analysis (Temoa) is an open source framework used to conduct analysis with a bottom-up, technology rich energy system model.
Based on Python (Pyomo). Using SQLite for data processing.
Website / Documentation
Download
|
 Open Source
GNU General Public License version 2.0 (GPL-2.0)
Directly downloadable
Input data shipped
|
| Model Scope |
Model type and solution approach |
| Model class
|
energy system optimization model
|
| Sectors
|
all
|
| Technologies
|
Renewables, Conventional Generation
|
| Decisions
|
investment
|
| Regions
|
U.S., currently
|
| Geographic Resolution
|
single region
|
| Time resolution
|
Multi year
|
| Network coverage
|
|
|
| Model type
|
Optimization
|
|
|
String representation "The model objec … gy commodities." is too long.
|
| Variables
|
|
| Computation time
|
5 minutes (varies with chosen solver)
|
| Objective
|
Cost minimization
|
| Uncertainty modeling
|
stochastic optimization, moeling-to-generate alternatives
|
| Suited for many scenarios / monte-carlo
|
Yes
|
|
References
Scientific references
Hunter, K., Sreepathi, S., DeCarolis, J. F. (2013). Modeling for insight using tools for energy model optimization and analysis (TEMOA). Energy Economics, 40, 339-349.
https://dx.doi.org/10.1016/j.eneco.2013.07.014
Example research questions
1. How does uncertainty in technology-specific characteristics (e.g., capital cost of solar PV) affect outcomes of interest (e.g., fuel prices, fossil fuel consumption, air emissions)?
2. Which technologies and fuels appear to be robust options given uncertainty in future climate
policy and rates of technology learning?
3. How much flexibility exists in energy system design and at what cost?
◀ back to model list
