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| | |montecarlo=No | | |montecarlo=No |
| | |citation_references=Shirizadeh, B., Perrier, Q. & Quirion, P. (2022) How sensitive are optimal fully renewable systems to technology cost uncertainty? The Energy Journal, Vol 43, No. 1 | | |citation_references=Shirizadeh, B., Perrier, Q. & Quirion, P. (2022) How sensitive are optimal fully renewable systems to technology cost uncertainty? The Energy Journal, Vol 43, No. 1 |
| − | |citation_doi=https://doi.org/10.5547/01956574.43.1.bshi | + | |citation_doi=10.5547/01956574.43.1.bshi |
| | |Model input file format=No | | |Model input file format=No |
| | |Model file format=No | | |Model file format=No |
| | |Model output file format=No | | |Model output file format=No |
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Latest revision as of 10:54, 31 December 2020
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Energy Optimization for Low Emission Systems - renewable electricity
by CIRED
Authors:
Behrang Shirizadeh, Quentin Perrier, Philippe Quirion
Contact:
Behrang Shirizadeh
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EOLES_elecRES is a dispatch and investment model that minimizes the annualized power
generation and storage costs, including the cost of connection to the grid. It includes six
power generation technologies: offshore and onshore wind power, solar photovoltaics
(PV), run-of-river and lake-generated hydro-electricity, and biogas combined with opencycle gas turbines. It also includes three energy storage technologies: pump-hydro
storage (PHS), batteries and methanation combined with open-cycle gas turbines.
Based on GAMS. Using for data processing.
Download
|
 Open Source
Creative Commons Attribution Share-Alike 4.0 (CC-BY-SA-4.0)
Directly downloadable
Input data shipped
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| Model Scope |
Model type and solution approach |
| Model class
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Electricity System Model
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| Sectors
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Electricity Sector
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| Technologies
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Renewables
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| Decisions
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dispatch, investment
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| Regions
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| Geographic Resolution
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Coutry
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| Time resolution
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Hour
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| Network coverage
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transmission
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| Model type
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Optimization, Simulation
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|
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Simultaneous optimization of dispatch and investment (linar programming), solved in CPLEX solver of GAMS
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| Variables
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| Computation time
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minutes
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| Objective
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investment cost and operational costs (fixed and variable) minimization
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| Uncertainty modeling
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Deterministic; Perfect foresight; Sensitivity analysis ; Robust decision making
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| Suited for many scenarios / monte-carlo
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No
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References
Scientific references
Shirizadeh, B., Perrier, Q. & Quirion, P. (2022) How sensitive are optimal fully renewable systems to technology cost uncertainty? The Energy Journal, Vol 43, No. 1
https://dx.doi.org/10.5547/01956574.43.1.bshi
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