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| | |timeresolution=Multi year | | |timeresolution=Multi year |
| | |math_modeltype=Optimization | | |math_modeltype=Optimization |
| − | |math_modeltype_shortdesc=The model objective is to minimize the present cost of energy supply by deploying and utilizing energy technologies and commodities over time to meet a set of exogenously specified end-use demands.Technologies are linked to one another in a network via model constraints representing the allowable flow of energy commodities. | + | |math_modeltype_shortdesc=The model objective is to minimize the present cost of energy supply by deploying and utilizing energy technologies and commodities over time to meet a set of exogenously specified end-use demands. |
| | |math_objective=Cost minimization | | |math_objective=Cost minimization |
| | |deterministic=stochastic optimization, moeling-to-generate alternatives | | |deterministic=stochastic optimization, moeling-to-generate alternatives |
Latest revision as of 16:15, 4 August 2015
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Tools for Energy Model Optimization and Analysis
by NC State University
Authors:
Joe DeCarolis, Kevin Hunter, Binghui Li, Sarat Sreepathi
Contact:
Joe DeCarolis
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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
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 Open Source
GNU General Public License version 2.0 (GPL-2.0)
Directly downloadable
Input data shipped
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| Model Scope |
Model type and solution approach |
| Model class
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energy system optimization model
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| Sectors
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all
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| Technologies
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Renewables, Conventional Generation
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| Decisions
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investment
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| Regions
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U.S., currently
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| Geographic Resolution
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single region
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| Time resolution
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Multi year
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| Network coverage
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| Model type
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Optimization
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The model objective is to minimize the present cost of energy supply by deploying and utilizing energy technologies and commodities over time to meet a set of exogenously specified end-use demands.
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| Variables
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| Computation time
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5 minutes (varies with chosen solver)
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| Objective
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Cost minimization
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| Uncertainty modeling
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stochastic optimization, moeling-to-generate alternatives
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| Suited for many scenarios / monte-carlo
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Yes
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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?
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