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| | |Primary outputs=Annualized cost, Installed capacities and generation and storage profiles | | |Primary outputs=Annualized cost, Installed capacities and generation and storage profiles |
| | |User documentation=http://www2.centre-cired.fr/IMG/pdf/cired_wp_2020_80_shirizadeh_quirion_perrier.pdf | | |User documentation=http://www2.centre-cired.fr/IMG/pdf/cired_wp_2020_80_shirizadeh_quirion_perrier.pdf |
| | + | |Code documentation=https://github.com/BehrangShirizadeh/EOLES_elecRES |
| | |open_source_licensed=Yes | | |open_source_licensed=Yes |
| | |license=Creative Commons Attribution Share-Alike 4.0 (CC-BY-SA-4.0) | | |license=Creative Commons Attribution Share-Alike 4.0 (CC-BY-SA-4.0) |
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| | |Link to source=https://github.com/BehrangShirizadeh/EOLES_elecRES | | |Link to source=https://github.com/BehrangShirizadeh/EOLES_elecRES |
| | |data_availability=all | | |data_availability=all |
| − | |open_future=Yes | + | |open_future=No |
| | + | |modelling_software=GAMS |
| | |GUI=No | | |GUI=No |
| | |model_class=Electricity System Model | | |model_class=Electricity System Model |
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| | |Storage (Gas)=Yes | | |Storage (Gas)=Yes |
| | |Storage (Heat)=No | | |Storage (Heat)=No |
| | + | |User behaviour=Inelastic demand - Optimization from social planner perspective |
| | |Market models=Electricity market | | |Market models=Electricity market |
| | |decisions=dispatch, investment | | |decisions=dispatch, investment |
| | + | |Changes in efficiency=fixed |
| | + | |georesolution=Coutry |
| | |timeresolution=Hour | | |timeresolution=Hour |
| | |network_coverage=transmission | | |network_coverage=transmission |
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| | |Additional dimensions (Other)=load curtailment, storage loss and etc. | | |Additional dimensions (Other)=load curtailment, storage loss and etc. |
| | |math_modeltype=Optimization, Simulation | | |math_modeltype=Optimization, Simulation |
| − | |math_modeltype_shortdesc=linear optimization of dispatch and investment, solved in CPLEX solver of GAMS | + | |math_modeltype_shortdesc=Simultaneous optimization of dispatch and investment (linar programming), solved in CPLEX solver of GAMS |
| | |math_objective=investment cost and operational costs (fixed and variable) minimization | | |math_objective=investment cost and operational costs (fixed and variable) minimization |
| | |deterministic=Deterministic; Perfect foresight; Sensitivity analysis ; Robust decision making | | |deterministic=Deterministic; Perfect foresight; Sensitivity analysis ; Robust decision making |
| | |is_suited_for_many_scenarios=No | | |is_suited_for_many_scenarios=No |
| | |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_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 |
| | }} | | }} |
Latest revision as of 10:54, 31 December 2020
|
Energy Optimization for Low Emission Systems - renewable electricity
by CIRED
Authors:
Behrang Shirizadeh, Quentin Perrier, Philippe Quirion
Contact:
Behrang Shirizadeh
|
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
|
| Model Scope |
Model type and solution approach |
| Model class
|
Electricity System Model
|
| Sectors
|
Electricity Sector
|
| Technologies
|
Renewables
|
| Decisions
|
dispatch, investment
|
| Regions
|
|
| Geographic Resolution
|
Coutry
|
| Time resolution
|
Hour
|
| Network coverage
|
transmission
|
|
| Model type
|
Optimization, Simulation
|
|
|
Simultaneous optimization of dispatch and investment (linar programming), solved in CPLEX solver of GAMS
|
| Variables
|
|
| Computation time
|
minutes
|
| Objective
|
investment cost and operational costs (fixed and variable) minimization
|
| Uncertainty modeling
|
Deterministic; Perfect foresight; Sensitivity analysis ; Robust decision making
|
| Suited for many scenarios / monte-carlo
|
No
|
|
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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