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| | |website=https://zenodo.org/record/1048943, https://zenodo.org/record/1212298 | | |website=https://zenodo.org/record/1048943, https://zenodo.org/record/1212298 |
| | |logo=ESO logo name2.pdf | | |logo=ESO logo name2.pdf |
| − | |text_description=The Electricity Systems Optimisation (ESO) framework contains a suite of power system capacity expansion and unit commitment models at different levels of spatial and temporal resolution and modelling complexity. Available for download is the single-node model with long-term capacity expansion from 2015 to 2050 in 5 yearly time steps and at hourly discretisation including endogenous technology cost learning (ESO-XEL) as perfect foresight and myopic foresight planning option. | + | |text_description=The Electricity Systems Optimisation (ESO) framework contains a suite of power system capacity expansion and unit commitment models at different levels of spatial and temporal resolution and modelling complexity. Available for download is the single-node model with long-term capacity expansion from 2015 to 2050 in 5 yearly time steps and at hourly discretisation including endogenous technology cost learning (ESO-XEL) as perfect foresight and myopic foresight planning option. |
| | |Primary outputs=number/type of new power generation+storage units, cost, carbon intensity, utilisation, wind/solar curtailment | | |Primary outputs=number/type of new power generation+storage units, cost, carbon intensity, utilisation, wind/solar curtailment |
| | |User documentation=https://zenodo.org/record/1048943 | | |User documentation=https://zenodo.org/record/1048943 |
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| | |processing_software=R | | |processing_software=R |
| | |GUI=No | | |GUI=No |
| − | |sectors=Electricity, | + | |model_class=power system model |
| | + | |sectors=Electricity, |
| | |technologies=Renewables, Conventional Generation | | |technologies=Renewables, Conventional Generation |
| | |Energy carrier (Gas)=Natural gas | | |Energy carrier (Gas)=Natural gas |
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| | |computation_time_hardware=Intel i7-4770 CPU, 3.4GHz, 8GB RAM | | |computation_time_hardware=Intel i7-4770 CPU, 3.4GHz, 8GB RAM |
| | |computation_time_comments=depends on scenario, e.g., amount of storage capacity | | |computation_time_comments=depends on scenario, e.g., amount of storage capacity |
| − | |citation_references=Heuberger CF, Rubin ES, Staffell I, Shah N, Mac Dowell Nclose, 2017, Power capacity expansion planning considering endogenous technology cost learning, APPLIED ENERGY, Vol: 204, Pages: 831-845, ISSN: 0306-2619 | + | |citation_references=Heuberger CF, Rubin ES, Staffell I, Shah N, Mac Dowell Nclose, 2017, Power capacity expansion planning considering endogenous technology cost learning, APPLIED ENERGY, Vol: 204, Pages: 831-845, ISSN: 0306-2619 |
| | |citation_doi=https://doi.org/10.1016/j.apenergy.2017.07.075 | | |citation_doi=https://doi.org/10.1016/j.apenergy.2017.07.075 |
| | |report_references=Heuberger CF, Staffell I, Shah N, Mac Dowell N, 2017, The changing costs of technology and the optimal investment timing in the power sector | | |report_references=Heuberger CF, Staffell I, Shah N, Mac Dowell N, 2017, The changing costs of technology and the optimal investment timing in the power sector |
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| | Heuberger CF, Staffell I, Shah N, Mac Dowell N, 2017, Valuing Flexibility in CCS Power Plants, IEAGHG Technical Report, http://www.ieaghg.org/exco_docs/2017-09.pdf | | Heuberger CF, Staffell I, Shah N, Mac Dowell N, 2017, Valuing Flexibility in CCS Power Plants, IEAGHG Technical Report, http://www.ieaghg.org/exco_docs/2017-09.pdf |
| − |
| |
| | |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 12:59, 29 June 2018
|
Electricity Systems Optimisation Framework
by Imperial College London
Authors:
Clara F. Heuberger
Contact:
Clara F. Heuberger
|
The Electricity Systems Optimisation (ESO) framework contains a suite of power system capacity expansion and unit commitment models at different levels of spatial and temporal resolution and modelling complexity. Available for download is the single-node model with long-term capacity expansion from 2015 to 2050 in 5 yearly time steps and at hourly discretisation including endogenous technology cost learning (ESO-XEL) as perfect foresight and myopic foresight planning option.
Based on GAMS; CPLEX. Using R for data processing.
https://zenodo.org/record/1212298 Website / Documentation
|
 Open Source
MIT license (MIT)
Directly downloadable
Input data shipped
|
| Model Scope |
Model type and solution approach |
| Model class
|
power system model
|
| Sectors
|
Electricity
|
| Technologies
|
Renewables, Conventional Generation
|
| Decisions
|
dispatch, investment
|
| Regions
|
UK
|
| Geographic Resolution
|
single-node (ESONE: 29 nodes)
|
| Time resolution
|
Hour
|
| Network coverage
|
|
|
| Model type
|
Optimization
|
|
|
MILP
|
| Variables
|
240,000
|
| Computation time
|
10 minutes (depends on scenario, e.g., amount of storage capacity)
|
| Objective
|
minimise total system cost
|
| Uncertainty modeling
|
scenario analysis
|
| Suited for many scenarios / monte-carlo
|
Yes
|
|
References
Scientific references
Heuberger CF, Rubin ES, Staffell I, Shah N, Mac Dowell Nclose, 2017, Power capacity expansion planning considering endogenous technology cost learning, APPLIED ENERGY, Vol: 204, Pages: 831-845, ISSN: 0306-2619
https://dx.doi.org/https://doi.org/10.1016/j.apenergy.2017.07.075
Reports produced using the model
Heuberger CF, Staffell I, Shah N, Mac Dowell N, 2017, The changing costs of technology and the optimal investment timing in the power sector
Heuberger CF, Mac Dowell N, 2018, Real-World Challenges with a Rapid Transition to 100% Renewable Power Systems, Joule, Vol: 2, Pages: 367-370
Heuberger CF, Staffell I, Shah N, Mac Dowell N, 2018, Impact of myopia and disruptive events in power systems planning, Nature Energy, doi:10.1038/s41560-018-0159-3
Heuberger CF, Staffell I, Shah N, Mac Dowell N, 2017, A systems approach to quantifying the value of power generation and energy storage technologies in future electricity networks, COMPUTERS & CHEMICAL ENGINEERING, Vol: 107, Pages: 247-256, ISSN: 0098-1354
Heuberger CF, Staffell I, Shah N, Mac Dowell N, 2017, Valuing Flexibility in CCS Power Plants, IEAGHG Technical Report, http://www.ieaghg.org/exco_docs/2017-09.pdf
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