wiki.openmod-initiative.org - User contributions [en]

ESO-X

2018-06-29T12:59:00Z

Clara Heuberger:

{{Model
|Full_Model_Name=Electricity Systems Optimisation Framework
|Acronym=ESO, ESO-X, ESO-XEL
|author_institution=Imperial College London
|authors=Clara F. Heuberger
|contact_persons=Clara F. Heuberger
|contact_email=c.heuberger14@imperial.ac.uk
|website=https://zenodo.org/record/1048943, https://zenodo.org/record/1212298
|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.
|Primary outputs=number/type of new power generation+storage units, cost, carbon intensity, utilisation, wind/solar curtailment
|User documentation=https://zenodo.org/record/1048943
|Code documentation=https://zenodo.org/record/1048943
|Number of developers=1
|Number of users=5
|open_source_licensed=Yes
|license=MIT license (MIT)
|model_source_public=Yes
|Link to source=https://zenodo.org/record/1048943
|data_availability=all
|open_future=No
|modelling_software=GAMS; CPLEX
|processing_software=R
|GUI=No
|model_class=power system model
|sectors=Electricity,
|technologies=Renewables, Conventional Generation
|Energy carrier (Gas)=Natural gas
|Energy carriers (Solid)=Biomass, Coal, Lignite, Uranium
|Energy carriers (Renewable)=Hydro, Sun, Wind
|Storage (Electricity)=Battery, CAES, PHS
|Storage (Gas)=No
|Storage (Heat)=No
|Market models=energy and carbon
|decisions=dispatch, investment
|Changes in efficiency=fixed
|georegions=UK
|georesolution=single-node (ESONE: 29 nodes)
|timeresolution=Hour
|Additional dimensions (Economical)=ancillary services (reserve, inertia)
|math_modeltype=Optimization
|math_modeltype_shortdesc=MILP
|math_objective=minimise total system cost
|deterministic=scenario analysis
|is_suited_for_many_scenarios=Yes
|number_of_variables=240,000
|montecarlo=No
|computation_time_minutes=10
|computation_time_hardware=Intel i7-4770 CPU, 3.4GHz, 8GB RAM
|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_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

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
|Model input file format=No
|Model file format=No
|Model output file format=No
}}

ESO-X

2018-05-25T12:36:38Z

Clara Heuberger: Created page with "{{Model |Full_Model_Name=Electricity Systems Optimisation Framework |Acronym=ESO, ESO-X, ESO-XEL |author_institution=Imperial College London |authors=Clara F. Heuberger |conta..."

{{Model
|Full_Model_Name=Electricity Systems Optimisation Framework
|Acronym=ESO, ESO-X, ESO-XEL
|author_institution=Imperial College London
|authors=Clara F. Heuberger
|contact_persons=Clara F. Heuberger
|contact_email=c.heuberger14@imperial.ac.uk
|website=https://zenodo.org/record/1048943, https://zenodo.org/record/1212298
|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.
|Primary outputs=number/type of new power generation+storage units, cost, carbon intensity, utilisation, wind/solar curtailment
|User documentation=https://zenodo.org/record/1048943
|Code documentation=https://zenodo.org/record/1048943
|Number of developers=1
|Number of users=5
|open_source_licensed=Yes
|license=MIT license (MIT)
|model_source_public=Yes
|Link to source=https://zenodo.org/record/1048943
|data_availability=all
|open_future=No
|modelling_software=GAMS; CPLEX
|processing_software=R
|GUI=No
|sectors=Electricity,
|technologies=Renewables, Conventional Generation
|Energy carrier (Gas)=Natural gas
|Energy carriers (Solid)=Biomass, Coal, Lignite, Uranium
|Energy carriers (Renewable)=Hydro, Sun, Wind
|Storage (Electricity)=Battery, CAES, PHS
|Storage (Gas)=No
|Storage (Heat)=No
|Market models=energy and carbon
|decisions=dispatch, investment
|Changes in efficiency=fixed
|georegions=UK
|georesolution=single-node (ESONE: 29 nodes)
|timeresolution=Hour
|Additional dimensions (Economical)=ancillary services (reserve, inertia)
|math_modeltype=Optimization
|math_modeltype_shortdesc=MILP
|math_objective=minimise total system cost
|deterministic=scenario analysis
|is_suited_for_many_scenarios=Yes
|number_of_variables=240,000
|montecarlo=No
|computation_time_minutes=10
|computation_time_hardware=Intel i7-4770 CPU, 3.4GHz, 8GB RAM
|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_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

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

|Model input file format=No
|Model file format=No
|Model output file format=No
}}

File:ESO logo name2.pdf

2018-05-25T12:11:27Z

Clara Heuberger: Logo of ESO model framework

== Summary ==
Logo of ESO model framework

== Licensing ==
{{license_ownwork_default}}