Difference between revisions of "Open Energy System Models"

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{{about||sources of the open data required for open modeling|open energy system databases}}
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''Note: This article is based on the Wikipedia article on Open energy system models that was written mostly by Robbie Morrison and edited by others (see Wikipedia history).''
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{{use dmy dates|date=May 2016}}
 
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{{use American English|date=May 2016}}
 
  
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{{broader|energy modeling}}
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'''Open energy system models''' are energy system models that are open source. Similarly open energy system data employs open data methods to produce and distribute datasets primarily for use by open energy system models.
  
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'''Open energy system models''' are [[energy system]] [[Energy modeling|models]] that are [[open source software|open source]].{{efn|
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Energy system models are used to explore future energy systems and are often applied to questions involving energy and climate policy. The models themselves vary widely in terms of their type, design, programming, application, scope, level of detail, sophistication, and shortcomings.<ref name="pye-and-bataille-2016">{{cite journal
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The terminology is not settled.  These models can also be known as '''open energy models''' or '''open source energy system models''' or some combination thereof.
 
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}}  Similarly open energy system data employs [[open data]] methods to produce and distribute [[dataset]]s primarily for use by open energy system models.
 
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Energy system models are used to explore future energy systems and are often applied to questions involving [[energy policy|energy]] and [[Politics of global warming|climate policy]]. The models themselves vary widely in terms of their type, design, programming, application, scope, level of detail, sophistication, and shortcomings.<ref name="pye-and-bataille-2016">{{cite journal
 
 
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</ref>{{rp|238}} For many models, some form of [[mathematical optimization]] is used to inform the solution process.
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</ref>{{rp|238}} For many models, some form of [[mathematical optimization]] is used to inform the solution process.
  
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Several drivers favor the development of open models and open data. There is an increasing interest in making [[public policy]] energy models more transparent to improve their acceptance by policymakers and the public.<ref name="acatech-etal-2016">
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Several drivers favor the development of open models and open data. There is an increasing interest in making public policy energy models more transparent to improve their acceptance by policymakers and the public.<ref name="acatech-etal-2016">
 
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</ref>  There is also a desire to leverage the benefits that open data and [[open-source software development|open software development]] can bring, including reduced duplication of effort, better sharing of ideas and information, improved quality, and wider engagement and adoption.<ref name="bazilian-etal-2012"/>  Model development is therefore usually a [[collaborative software development model|team effort]] and constituted as either an academic project, a commercial venture, or a genuinely inclusive community initiative.
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</ref>  There is also a desire to leverage the benefits that open data and open software development can bring, including reduced duplication of effort, better sharing of ideas and information, improved quality, and wider engagement and adoption.<ref name="bazilian-etal-2012"/>  Model development is therefore usually a team effort and constituted as either an academic project, a commercial venture, or a genuinely inclusive community initiative.
  
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This article does not cover projects which simply make their [[source code]] or [[spreadsheet]]s available for public download, but which omit a recognized [[free software license|free and open source software license]].  The absence of a license agreement creates a state of legal uncertainty whereby potential users cannot know which limitations the owner may want to enforce in the future.<ref name="morin-etal-2012">
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This article does not cover projects which simply make their source code or spreadsheets available for public download, but which omit a recognized free and open source software license.  The absence of a license agreement creates a state of legal uncertainty whereby potential users cannot know which limitations the owner may want to enforce in the future.<ref name="morin-etal-2012">
 
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== General considerations ==
 
== General considerations ==
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{{see also|Energy modeling|Open-source software development}}
 
  
 
=== Organization ===
 
=== Organization ===
  
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An open energy system modeling project typically comprises a [[codebase]], [[dataset]]s, and [[software documentation]] and perhaps [[Scientific literature|scientific publications]].<ref name="bazilian-etal-2012">
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An open energy system modeling project typically comprises a codebase, datasets, and software documentation and perhaps scientific publications.<ref name="bazilian-etal-2012">
 
{{cite journal
 
{{cite journal
 
  | last1 = Bazilian | first1 = Morgan
 
  | last1 = Bazilian | first1 = Morgan
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  | access-date = 2016-06-17
 
  | access-date = 2016-06-17
 
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}}
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</ref>  The project repository may be hosted on an institutional server or on a public [[Comparison of open source software hosting facilities|code-hosting site]], such as [[GitHub]].  Some projects release only their [[codebase]], while others ship some or all of their [[dataset]]s as well.  Projects may also offer [[Electronic mailing list|email lists]], [[Online chat|chat rooms]], and [[Internet forum|web forums]] to aid collaboration.
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</ref>  The project repository may be hosted on an institutional server or on a public code-hosting site, such as GitHub.  Some projects release only their codebase, while others ship some or all of their datasets as well.  Projects may also offer email lists, chat rooms, and web forums to aid collaboration.
  
 
The majority of projects are based within university research groups, either singingly or as academic collaborations.
 
The majority of projects are based within university research groups, either singingly or as academic collaborations.
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|}
  
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=== DIETER ===
 
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{| class="infobox" style="width: 28em"
 
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|-
 
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! style="width: 35%" | Project
 
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| style="width: 55%" | DIETER
 
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|-
 
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! Host
 
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| [[German Institute for Economic Research|DIW Berlin]]
 
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|-
 
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! Status
 
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| active
 
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|-
 
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! Scope/type
 
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| dispatch and investment
 
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|-
 
−
! Code license
 
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| [[MIT license|MIT]]
 
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|-
 
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! Data license
 
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| [[MIT license|MIT]]
 
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|-
 
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! Website
 
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| {{url|http://www.diw.de/dieter}}
 
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|}
 
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DIETER stands for Dispatch and Investment Evaluation Tool with Endogenous Renewables.  DIETER is a dispatch and investment model. It was first used to study the role of [[Grid energy storage|power storage]] and other flexibility options in a future [[Greenfield project|greenfield]] setting with high shares of renewable generation.  DIETER is being developed at the [[German Institute for Economic Research]] (DIW), [[Berlin]], Germany.  The [[codebase]] and [[Data (computing)|datasets]] for Germany can be downloaded from the project website.  The basic model is fully described in a DIW working paper and a journal article.<ref name="zerrahn-and-schill-2015">
 
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{{cite book
 
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| last1 = Zerrahn | first1 = Alexander
 
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| last2 = Schill | first2 = Wolf-Peter
 
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| title = A greenfield model to evaluate long-run power storage requirements for high shares of renewables — DIW discussion paper 1457
 
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| year = 2015
 
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| publisher = German Institute for Economic Research (DIW)
 
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| location = Berlin, Germany
 
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| issn = 1619-4535
 
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| url = http://www.diw.de/documents/publikationen/73/diw_01.c.498475.de/dp1457.pdf
 
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| access-date = 2016-07-07
 
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}}
 
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</ref><ref>{{Cite journal|last=Zerrahn|first=Alexander|last2=Schill|first2=Wolf-Peter|title=Long-run power storage requirements for high shares of renewables: review and a new model|url=https://doi.org/10.1016/j.rser.2016.11.098|journal=Renewable and Sustainable Energy Reviews|doi=10.1016/j.rser.2016.11.098}}</ref>  DIETER is written in [[General Algebraic Modeling System|GAMS]] and was developed using the [[CPLEX]] commercial solver.
 
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DIETER is framed as a pure [[Linear programming|linear]] (no integer variables) cost minimization problem.  In the initial formulation, the decision variables include the investment in and dispatch of generation, storage, and [[Energy demand management|DSM]] capacities in the German wholesale and balancing electricity markets.  Later model extensions include [[vehicle-to-grid]] interactions and prosumage of solar electricity.<ref>{{Cite journal|last=Schill|first=Wolf-Peter|last2=Niemeyer|first2=Moritz|last3=Zerrahn|first3=Alexander|last4=Diekmann|first4=Jochen|date=2016-06-01|title=Bereitstellung von Regelleistung durch Elektrofahrzeuge: Modellrechnungen für Deutschland im Jahr 2035|url=https://link.springer.com/article/10.1007/s12398-016-0174-7|journal=Zeitschrift für Energiewirtschaft|language=de|volume=40|issue=2|pages=73–87|doi=10.1007/s12398-016-0174-7|issn=0343-5377}}</ref><ref>{{Cite journal|last=Schill|first=Wolf-Peter|last2=Zerrahn|first2=Alexander|last3=Kunz|first3=Friedrich|date=2017-06-01|title=Prosumage of solar electricity: pros, cons, and the system perspective|url=https://doi.org/10.5547/2160-5890.6.1.wsch|journal=Economics of Energy & Environmental Policy|language=en-US|volume=6|issue=1|doi=10.5547/2160-5890.6.1.wsch|issn=2160-5882}}</ref>
 
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The first study using DIETER examines the power storage requirements for renewables uptake ranging from 60% to 100%.  Under the baseline scenario of 80% (the lower bound German government target for 2050), [[Grid energy storage|grid storage]] requirements remain moderate and other options on both the supply side and demand side offer flexibility at low cost.  Nonetheless storage plays an important role in the provision of reserves.  Storage becomes more pronounced under higher shares of renewables, but strongly depends on the costs and availability of other flexibility options, particularly biomass availability.<ref>{{Cite journal|last=Schill|first=Wolf-Peter|last2=Zerrahn|first2=Alexander|title=Long-run power storage requirements for high shares of renewables: Results and sensitivities|url=https://doi.org/10.1016/j.rser.2017.05.205|journal=Renewable and Sustainable Energy Reviews|doi=10.1016/j.rser.2017.05.205}}</ref>
 
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{{clear}}
 
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=== Dispa-SET ===
 
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{| class="infobox" style="width: 28em"
 
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|-
 
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! style="width: 35%" | Project
 
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| style="width: 55%" | Dispa-SET
 
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|-
 
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! Host
 
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| [[European Commission|EC]] [[Joint Research Centre]]
 
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|-
 
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! Status
 
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| active
 
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|-
 
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! Scope/type
 
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| European transmission and dispatch
 
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|-
 
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! Code license
 
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| [[European Union Public Licence|EUPL{{nbsp}}1.1]]
 
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|-
 
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! Data license
 
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| [[European Union Public Licence|EUPL{{nbsp}}1.1]]
 
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|-
 
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! Website
 
−
| {{url|https://joinup.ec.europa.eu/software/dispaset/}}
 
−
|-
 
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! Repository
 
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| {{url|https://github.com/squoilin/Dispa-SET}}
 
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|-
 
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! Documentation
 
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| {{url|http://dispa-set.readthedocs.io/en/latest/}}
 
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|}
 
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Under development at the [[European Commission]]'s [[Joint Research Centre]] (JRC), [[Petten]], the Netherlands, {{nowrap|Dispa-SET}} is a unit commitment and dispatch model intended primarily for Europe.  It is written in [[Python (programming language)|Python]] (with [[Pyomo]]) and [[General Algebraic Modeling System|GAMS]] and uses Python for data processing.  A valid GAMS license is required.  The model is formulated as a [[Linear programming#Integer unknowns|mixed integer]] problem and JRC uses the proprietary [[CPLEX]] sover although open source libraries may also be deployed.  Technical descriptions are available for versions{{nbsp}}2.0{{nnbsp}}<ref name="hidalgo-gonzalez-etal-2014">
 
−
{{cite book
 
−
| last1 = Hidalgo González | first1 = Ignacio
 
−
| last2 = Quoilin | first2 = Sylvain
 
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| last3 = Zucker | first3 = Andreas
 
−
| date = 2014
 
−
| title = Dispa-SET 2.0: unit commitment and power dispatch model: description, formulation, and implementation — EUR 27015 EN
 
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| publisher = Publications Office of the European Union
 
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| location = Luxembourg
 
−
| doi = 10.2790/399921
 
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| isbn = 978-92-79-44690-0
 
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| url = http://publications.jrc.ec.europa.eu/repository/bitstream/JRC93780/report%20dispa-set%202.0%2020150108%20online.pdf
 
−
| access-date = 2017-03-01
 
−
}}  The DOI and ISBN refer to the online version.
 
−
</ref> and{{nbsp}}2.1.<ref name="quoilin-etal-2017">
 
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{{cite book
 
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| last1 = Quoilin | first1 = Sylvain
 
−
| last2 = Hidalgo González | first2 = Ignacio
 
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| last3 = Zucker | first3 = Andreas
 
−
| date = 2017
 
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| title = Modelling future EU power systems under high shares of renewables: the Dispa-SET 2.1 open-source model — EUR 28427 EN
 
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| publisher = Publications Office of the European Union
 
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| location = Luxembourg
 
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| doi = 10.2760/25400
 
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| isbn = 978-92-79-65265-3
 
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| url = http://publications.jrc.ec.europa.eu/repository/bitstream/JRC105452/dispaset2.1_technical_report.pdf
 
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| access-date = 2017-03-01
 
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}}
 
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</ref>  {{nowrap|Dispa-SET}} is hosted on [[GitHub]], together with a trial dataset, and third-party contributions are encouraged.  The [[codebase]] has been tested on Windows, macOS, and Linux.  Online documentation is available.<ref name="dispa-set-documentation">
 
−
{{cite web
 
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| title = Dispa-SET documentation
 
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| url = http://dispa-set.readthedocs.io/en/latest/
 
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| access-date = 2017-03-02
 
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}}  Automatically the latest version.
 
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</ref>
 
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−
The SET in the project name refers to the European Strategic Energy Technology Plan (SET-Plan), which seeks to make Europe a leader in energy technologies that can fulfill future (2020 and 2050) energy and climate targets.  Energy system modeling, in various forms, is central to this [[European Commission]] initiative.<ref name="set-plan-2016-two">
 
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{{cite journal
 
−
| author = <!-- staff writer, no by-line -->
 
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| date = November 2016
 
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| title = SET-Plan Update
 
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| journal = SETIS magazine
 
−
| number = 13
 
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| pages = 5–7
 
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| issn = 2467-382X
 
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| url = http://publications.jrc.ec.europa.eu/repository/bitstream/JRC103767/2016_no.13_modelling%20magazine_web-version.pdf
 
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| access-date = 2017-03-01
 
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}}
 
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</ref>
 
−
 
−
[[File:Rolling horizon.png|thumb|left|48{{nbsp}}hour rolling horizon optimization for any given 24{{nbsp}}hour day]]
 
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The model power system is managed by a single operator with full knowledge of the economic and technical characteristics of the generation units, the loads at each node, and the heavily simplified transmission network.  Demand is deemed fully [[Elasticity (economics)|inelastic]].  The system is subject to intra-period and inter-period [[Unit commitment problem in electrical power production|unit commitment]] constraints (the latter covering nuclear and thermal generation for the most part) and operated under [[economic dispatch]].<ref name="quoilin-etal-2017"/>{{rp|4}}  Hourly data is used and the simulation horizon is normally one year.  But to ensure the model remains tractable, two day rolling horizon optimization is employed.  The model advances in steps of one day, optimizing the next 48{{nbsp}}hours ahead but retaining results for just the first 24{{nbsp}}hours.<ref name="quoilin-etal-2017"/>{{rp|14–15}}
 
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Two related publications describe the role and representation of flexibility measures within power systems facing ever greater shares of [[variable renewable energy]] (VRE).<ref name="hidalgo-gonzalez-etal-2015">
 
−
{{cite report
 
−
| first1 = Ignacio | last1 = Hidalgo González
 
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| first2 = Pablo | last2 = Ruiz Castello
 
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| first3 = Alessandra | last3 = Sgobbi
 
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| first4 = Wouter | last4 = Nijs
 
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| first5 = Sylvain | last5 = Quoilin
 
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| first6 = Andreas | last6 = Zucker
 
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| first7 = Christian | last7 = Thiel
 
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| date = 2015
 
−
| title = Addressing flexibility in energy system models — EUR 27183 EN
 
−
| publisher = Publications Office of the European Union
 
−
| location = Luxembourg
 
−
| doi = 10.2790/925
 
−
| isbn = 978-92-79-47235-0
 
−
| url = http://publications.jrc.ec.europa.eu/repository/bitstream/JRC95354/addressing%20flexibility%20in%20energy%20system%20models%20%28online%29%2020150413.pdf
 
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| access-date = 2017-03-02
 
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}}  The DOI and ISBN refer to the online version.
 
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</ref><ref name="quoilin-etal-2015">
 
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{{cite conference
 
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| first1 = Sylvain | last1 = Quoilin
 
−
| first2 = Wouter | last2 = Nijs
 
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| first3 = Ignacio | last3 = Hidalgo González
 
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| first4 = Andreas | last4 = Zucker
 
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| first5 = Christian | last5 = Thiel
 
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| date = 19 May 2015
 
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| title = Evaluation of simplified flexibility evaluation tools using a unit commitment model
 
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| conference = 2015 12th International Conference on the European Energy Market (EEM)
 
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| pages = 1–5
 
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| doi = 10.1109/EEM.2015.7216757
 
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| isbn = 978-1-4673-6692-2
 
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| issn = 2165-4077
 
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}}
 
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</ref>  These flexibility measures comprise: dispatchable generation (with constraints on efficiency, ramp rate, part load, and up and down times), conventional storage (predominantly [[pumped-storage hydroelectricity|pumped-storage hydro]]), cross-boarder interconnectors, [[Energy demand management|demand side management]], renewables curtailment, last resort [[rolling blackout|load shedding]], and nascent [[power-to-X]] solutions (with X being gas, heat, or mobility).  The modeler can set a target for renewables and place caps on {{CO2}} and other pollutants.<ref name="quoilin-etal-2017"/>  Planned extensions to the software include support for simplified AC power flow{{nnbsp}}{{efn|The simplified AC power-flow method is also referred to as the DC load-flow method because the active power flow equation for fixed-frequency AC is ''analogous'' to [[Ohm's law]] applied to a resistor carrying DC current.<ref name="andersson-2008">
 
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{{cite book
 
−
| last1 = Andersson | first1 = Göran
 
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| date = 2008
 
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| title = Modelling and analysis of electric power systems: power flow analysis fault analysis power systems dynamics and stability
 
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| publisher = ETH Zurich
 
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| location = Zürich, Switzerland
 
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| url = http://www.eeh.ee.ethz.ch/uploads/tx_ethstudies/modelling_hs08_script_02.pdf
 
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| access-date = 2017-02-02
 
−
}}
 
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</ref>{{rp|59}}  For the purposes of optimization, the quadratic loss function is also piecewise linearized.
 
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}} (transmission is currently treated as a [[Flow network|transportation problem]]), new constraints (like [[Water cooling|cooling water]] supply), [[stochastic]] scenarios, and the inclusion of markets for [[Ancillary services (electric power)|ancillary{{nbsp}}services]].<ref name="dispa-set-documentation"/>
 
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{{nowrap|Dispa-SET}} has been or is being applied to case studies in Belgium, Bolivia, Greece, Ireland, and the Netherlands.  A 2014 Belgium study investigates [[Sensitivity analysis|what if]] scenarios for different mixes of nuclear generation, combined cycle gas turbine (CCGT) plant, and VRE and finds that the CCGT plants are subject to more aggressive cycling as renewable generation penetrates.<ref name="quoilin-etal-2014">
 
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<!-- alternative url: http://orbi.ulg.be/handle/2268/172402 -->
 
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{{cite conference
 
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| first1 = Sylvain | last1 = Quoilin
 
−
| first2 = Ignacio | last2 = Hidalgo González
 
−
| first3 = Andreas | last3 = Zucker
 
−
| first4 = Christian | last4 = Thiel
 
−
| date = September 2014
 
−
| title = Available technical flexibility for balancing variable renewable energy sources: case study in Belgium
 
−
| book-title = Proceedings of the 9th Conference on Sustainable Development of Energy, Water and Environment Systems
 
−
| url = http://orbi.ulg.be/bitstream/2268/172402/1/Paper%20SDEWES%20SQ140923.pdf
 
−
| access-date = 2017-03-02
 
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}}
 
−
</ref>
 
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{{clear}}
 
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=== EMLab-Generation ===
 
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{| class="infobox" style="width: 28em"
 
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|-
 
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! style="width: 35%" | Project
 
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| style="width: 55%" | EMLab-Generation
 
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|-
 
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! Host
 
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| [[Delft University of Technology]]
 
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|-
 
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! Status
 
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| active
 
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|-
 
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! Scope/type
 
−
| agent-based
 
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|-
 
−
! Code license
 
−
| [[Apache License|Apache 2.0]]
 
−
|-
 
−
! Website
 
−
| {{url|http://emlab.tudelft.nl/generation.html}}
 
−
|-
 
−
! Repository
 
−
| {{url|https://github.com/EMLab/emlab-generation}}
 
−
|}
 
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−
EMLab-Generation is an [[agent-based model]] covering two interconnected electricity markets – be they two adjoining countries or two groups of countries.  The software is being developed at the [http://emlab.tudelft.nl/ Energy Modelling Lab], [[Delft University of Technology]], [[Delft]], the Netherlands.  A factsheet is available.<ref name="emlab-factsheet">
 
−
{{cite book
 
−
| title = EMLab — Generation Factsheet
 
−
| publisher = Energy Modelling Lab, Delft University of Technology
 
−
| location = Delft, The Netherlands
 
−
| url = http://emlab.tudelft.nl/generation/emlab-generation-factsheet.pdf
 
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| access-date = 2016-07-09
 
−
}}
 
−
</ref>  And software documentation is available.<ref name="laurens-etal-2015">
 
−
{{cite book
 
−
| first1 = Laurens J | last1 = de Vries
 
−
| first2 = Émile JL | last2 = Chappin
 
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| first3 = Jörn C | last3 = Richstein
 
−
| title = EMLab-Generation: an experimentation environment for electricity policy analysis — Project report — Version 1.2
 
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| date = August 2015
 
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| publisher = Energy Modelling Lab, Delft University of Technology
 
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| location = Delft, The Netherlands
 
−
| url = http://emlab.tudelft.nl/generation/emlab-generation-report-1.2.pdf
 
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| access-date = 2016-07-09
 
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}}
 
−
</ref>  EMLab-Generation is written in [[Java (programming language)|Java]].
 
−
 
−
EMLab-Generation simulates the actions of [[Electric power industry|power companies]] investing in generation capacity and uses this to explore the long-term effects of various [[Energy policy|energy]] and [[Climate change mitigation|climate protection]] policies.  These policies may target renewable generation, {{CO2}} emissions, security of supply, and/or energy affordability.  The power companies are the main agents: they bid into power markets and they invest based on the [[net present value]] (NPV) of prospective power plant projects.  They can adopt a variety of technologies, using scenarios from the 2011 [[International Energy Agency|IEA]] [[World Energy Outlook]].<ref name="iea-2011">
 
−
{{cite book
 
−
| title = World energy outlook 2011
 
−
| year = 2011
 
−
| publisher = International Energy Agency (IEA)
 
−
| location = Paris, France
 
−
| isbn = 978-92-64-12413-4
 
−
| url = https://www.iea.org/publications/freepublications/publication/WEO2011_WEB.pdf
 
−
| access-date = 2016-07-09
 
−
}}
 
−
</ref>  The agent-based methodology enables different sets of assumptions to be tested, such as the heterogeneity of actors, the consequences of imperfect expectations, and the behavior of investors outside of ideal conditions.
 
−
 
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EMLab-Generation offers a new way of modeling the effects of public policy on electricity markets.  It can provide insights into actor and system behaviors over time – including such things as investment cycles, abatement cycles, delayed responses, and the effects of uncertainty and risk on investment decisions.
 
−
 
−
A 2014 study using EMLab-Generation investigates the effects of introducing floor and ceiling prices for {{CO2}} under the [[European Union Emission Trading Scheme|EU ETS]].  And in particular, their influence on the dynamic investment pathway of two interlinked electricity markets (loosely Great Britain and Central Western Europe).  The study finds a common, moderate {{CO2}} auction reserve price results in a more continuous decarbonisation pathway and reduces {{CO2}} price volatility.  Adding a ceiling price can shield consumers from extreme price shocks.  Such price restrictions should not lead to an overshoot of emissions targets in the long-run.<ref name="richstein-etal-2014">
 
−
{{cite journal
 
−
| first1 = Jörn C | last1 = Richstein
 
−
| first2 = Emile JL | last2 = Chappin
 
−
| first3 = Laurens J | last3 = de Vries
 
−
| year = 2014
 
−
| title = Cross-border electricity market effects due to price caps in an emission trading system: an agent-based approach
 
−
| journal = Energy Policy
 
−
| volume = 71
 
−
| pages = 139–158
 
−
| doi = 10.1016/j.enpol.2014.03.037
 
−
| url = http://www.sciencedirect.com/science/article/pii/S0301421514002043/pdfft?md5=f586bdc740bb1562a3e5aefc012d26e9&pid=1-s2.0-S0301421514002043-main.pdf
 
−
| access-date = 2016-07-07
 
−
}}
 
−
</ref>
 
−
 
−
{{clear}}
 
−
 
−
=== EMMA ===
 
−
 
−
{| class="infobox" style="width: 28em"
 
−
|-
 
−
! style="width: 35%" | Project
 
−
| style="width: 55%" | EMMA
 
−
|-
 
−
! Host
 
−
| [http://neon-energie.de/en/ Neon Neue Energieökonomik]
 
−
|-
 
−
! Status
 
−
| active
 
−
|-
 
−
! Scope/type
 
−
| electricity market
 
−
|-
 
−
! Code license
 
−
| [[Creative Commons license|{{nowrap|CC BY-SA 3.0}}]]
 
−
|-
 
−
! Data license
 
−
| [[Creative Commons license|{{nowrap|CC BY-SA 3.0}}]]
 
−
|-
 
−
! Website
 
−
| {{url|http://neon-energie.de/emma/}}
 
−
|}
 
−
 
−
EMMA is the European Electricity Market Model.  It is a techno-economic model covering the integrated Northwestern European power system.  EMMA is being developed by the energy economics consultancy Neon Neue Energieökonomik, [[Berlin]], Germany.  The [[source code]] and [[Data (computing)|datasets]] can be downloaded from the project website.  A manual is available.<ref name="hirth-2016">
 
−
{{cite book
 
−
| first = Lion | last = Hirth
 
−
| title = The European Electricity Market Model EMMA — Model documentation — Version 2016-04-12
 
−
| date = 12 April 2016
 
−
| publisher = Neon Neue Energieökonomik
 
−
| location = Berlin, Germany
 
−
| url = http://neon-energie.de/EMMA.pdf
 
−
| access-date = 2016-07-09
 
−
}}
 
−
</ref>  EMMA is written in [[General Algebraic Modeling System|GAMS]] and uses the [[CPLEX]] commercial solver.
 
−
 
−
EMMA models electricity dispatch and investment, minimizing the total cost with respect to investment, generation, and trades between market areas.  In economic terms, EMMA classifies as a [[partial equilibrium]] model of the wholesale [[electricity market]] with a focus on the supply-side.  EMMA identifies short-term or long-term optima (or equilibria) and estimates the corresponding capacity mix, hourly prices, dispatch, and cross-border trading.  Technically, EMMA is a pure [[Linear programming|linear program]] (no integer variables) with about two million {{nowrap|non-zero}} variables.  {{as of|2016}}, the model covers Belgium, France, Germany, the Netherlands, and Poland and supports conventional generation, renewable generation, and [[cogeneration]].<ref name="hirth-2016"/><ref name="hirth-2014">
 
−
{{cite book
 
−
| first = Leon | last = Hirth
 
−
| title = The economics of wind and solar variability: how the variability of wind and solar power affects their marginal value, optimal deployment, and integration costs — PhD thesis
 
−
| publisher = Technical University of Berlin
 
−
| location = Berlin, Germany
 
−
| doi = 10.14279/depositonce-4291
 
−
| url = https://depositonce.tu-berlin.de/bitstream/11303/4588/2/hirth_lion.pdf
 
−
| access-date = 2016-07-07
 
−
}}
 
−
</ref>
 
−
 
−
EMMA has been used to study the economic effects of the increasing penetration of [[variable renewable energy]] (VRE), specifically solar power and wind power, in the Northwestern European power system.  A 2013 study finds that increasing VRE shares will depress prices and, as a consequence, the competitive large-scale deployment of renewable generation will be more difficult to accomplish than many anticipate.<ref name="hirth-2013">
 
−
{{cite journal
 
−
| first = Lion | last = Hirth
 
−
| title = The market value of variable renewables: the effect of solar wind power variability on their relative price
 
−
| journal = Energy Economics
 
−
| volume = 38
 
−
| pages = 218–236
 
−
| year = 2013
 
−
| doi = 10.1016/j.eneco.2013.02.004
 
−
| url = http://www.neon-energie.de/Hirth-2013-Market-Value-Renewables-Solar-Wind-Power-Variability-Price.pdf
 
−
| access-date = 2016-07-09
 
−
}}
 
−
</ref>  A 2015 study estimates the welfare-optimal market share for wind and solar power.  For wind, this is 20%, three-fold more than at present.<ref name="hirth-2015">
 
−
{{cite journal
 
−
| first = Leon | last = Hirth
 
−
| title = The optimal share of variable renewables: how the variability of wind and solar power affects their welfare-optimal deployment
 
−
| year = 2015
 
−
| journal = The Energy Journal
 
−
| volume = 36
 
−
| number = 1
 
−
| pages = 127–162
 
−
| doi = 10.5547/01956574.36.1.6
 
−
| url = http://www.neon-energie.de/Hirth-2015-Optimal-Share-Variable-Renewables-Wind-Solar-Power-Welfare.pdf
 
−
| access-date = 2016-07-07
 
−
}}
 
−
</ref>
 
−
 
−
An independent 2015 study reviews the EMMA model and comments on the high assumed specific costs for renewable investments.<ref name="zerrahn-and-schill-2015"/>{{rp|6}}
 
−
 
−
{{clear}}
 
−
 
−
=== GENESYS ===
 
−
 
−
{| class="infobox" style="width: 28em"
 
−
|-
 
−
! style="width: 35%" | Project
 
−
| style="width: 55%" | GENESYS
 
−
|-
 
−
! Host
 
−
| [[RWTH Aachen University]]
 
−
|-
 
−
! Status
 
−
| active
 
−
|-
 
−
! Scope/type
 
−
| European electricity system
 
−
|-
 
−
! Code license
 
−
| [[GNU Lesser General Public License|LGPLv2.1]]
 
−
|-
 
−
! Data license
 
−
| [[GNU Lesser General Public License|LGPLv2.1]]
 
−
|-
 
−
! Website
 
−
| {{url|1=http://www.genesys.rwth-aachen.de/index.php?id=12&L=3}}
 
−
|}
 
−
 
−
GENESYS stands for Genetic Optimisation of a European Energy Supply System.  The software is being developed jointly by the [http://www.iaew.rwth-aachen.de/?lang=en Institute of Power Systems and Power Economics] (IAEW) and the [https://www2.isea.rwth-aachen.de/de Institute for Power Electronics and Electrical Drives] (ISEA), both of [[RWTH Aachen University]], [[Aachen]], Germany.  The project maintains a website where potential users can request access to the [[codebase]] and the [[Data (computing)|dataset]] for the 2050 base scenario only.<ref>
 
−
{{cite web
 
−
| title = The Project
 
−
| website = GENESYS project
 
−
| url = http://www.genesys.rwth-aachen.de/index.php?id=projekt&L=3
 
−
| access-date = 2016-07-09
 
−
}}
 
−
</ref>  Detailed descriptions of the software are available.<ref name="bussar-etal-2014"/><ref name="bussar-etal-2016"/>  GENESYS is written in [[C++]] and uses [[Boost (C++ libraries)|Boost]] libraries, the [[MySQL]] relational database, the [[Qt (software)|Qt{{nbsp}}4]] application framework, and optionally the [[CPLEX]] solver.
 
−
 
−
The GENESYS simulation tool is designed to optimize a future EUMENA (Europe, Middle East, and North Africa) power system and assumes a high share of renewable generation.  It is able to find an economically optimal distribution of generator, storage, and transmission capacities within a 21{{nbsp}}region EUMENA.  It allows for the optimization of this energy system in combination with an evolutionary method.  The optimization is based on a [[CMA-ES|covariance matrix adaptation evolution strategy]] (CMA-ES), while the operation is simulated as a hierarchical set-up of system elements which balance the load between the various regions at minimum cost using the [[network simplex algorithm]].  GENESYS ships with a set of input time series and a set of parameters for the year 2050, which the user can modify.
 
−
 
−
A future EUMENA energy supply system with a high share of renewable energy sources (RES) will need a strongly interconnected energy transport grid and significant energy storage capacities.  GENESYS was used to dimension the storage and transmission between the 21{{nbsp}}different regions.  Under the assumption of 100% self-supply, about {{val|2500|u=GW}} of RES in total and a storage capacity of about {{val|240000|u=GWh}} are needed, corresponding to 6% of the annual energy demand, and a HVDC transmission grid of {{val|375000|u=GW·km}}.  The combined cost estimate for generation, storage, and transmission, excluding distribution, is 6.87{{nbsp}}¢/kWh.<ref name="bussar-etal-2014">
 
−
{{cite journal
 
−
| first1 = Christian | last1 = Bussar
 
−
| first2 = Melchior | last2 = Moos
 
−
| first3 = Ricardo | last3 = Alvarez
 
−
| first4 = Philipp | last4 = Wolf
 
−
| first5 = Tjark | last5 = Thien
 
−
| first6 = Hengsi | last6 = Chen
 
−
| first7 = Zhuang | last7 = Cai
 
−
| first8 = Matthias | last8 = Leuthold
 
−
| first9 = Dirk Uwe | last9 = Sauer
 
−
| first10 = Albert | last10 = Moser
 
−
| title = Optimal allocation and capacity of energy storage systems in a future European power system with 100% renewable energy generation
 
−
| year = 2014
 
−
| journal = Energy Procedia
 
−
| volume = 46
 
−
| pages = 40–47
 
−
| doi = 10.1016/j.egypro.2014.01.156
 
−
| url = http://www.sciencedirect.com/science/article/pii/S1876610214001726/pdf?md5=7920a65166f703f26648e73f5ee8a7be&pid=1-s2.0-S1876610214001726-main.pdf
 
−
| access-date = 2016-07-07
 
−
}}
 
−
</ref>
 
−
 
−
A 2016 study looked at the relationship between storage and transmission capacity under high shares of renewable energy sources (RES) in an EUMENA power system.  It found that, up to a certain extent, transmission capacity and storage capacity can substitute for each other.  For a transition to a fully renewable energy system by 2050, major structural changes are required.  The results indicate the optimal allocation of photovoltaics and wind power, the resulting demand for storage capacities of different technologies (battery, pumped hydro, and hydrogen storage) and the capacity of the transmission grid.<ref name="bussar-etal-2016">
 
−
{{cite journal
 
−
| first1 = Christian | last1 = Bussar
 
−
| first2 = Philipp | last2 = Stöcker
 
−
| first3 = Zhuang | last3 = Cai
 
−
| first4 = Luiz | last4 = Moraes Jr
 
−
| first5 = Dirk | last5 = Magnor
 
−
| first6 = Pablo | last6 = Wiernes
 
−
| first7 = Niklas | last7 = van Bracht
 
−
| first8 = Albert | last8 = Moser
 
−
| first9 = Dirk Uwe | last9 = Sauer
 
−
| title = Large-scale integration of renewable energies and impact on storage demand in a European renewable power system of 2050 – Sensitivity study
 
−
| journal = Journal of Energy Storage
 
−
| volume = 6
 
−
| pages = 1–10
 
−
| year = 2016
 
−
| doi = 10.1016/j.est.2016.02.004
 
−
}}
 
−
</ref>
 
−
 
−
{{clear}}
 
−
 
−
=== NEMO ===
 
−
 
−
{| class="infobox" style="width: 28em"
 
−
|-
 
−
! style="width: 35%" | Project
 
−
| style="width: 55%" | NEMO
 
−
|-
 
−
! Host
 
−
| [[University of New South Wales]]
 
−
|-
 
−
! Status
 
−
| active
 
−
|-
 
−
! Scope/type
 
−
| Australian NEM market
 
−
|-
 
−
! Code license
 
−
| [[GNU General Public License|GPLv3]]
 
−
|-
 
−
! Website
 
−
| {{url|https://nemo.ozlabs.org}}
 
−
|-
 
−
! Repository
 
−
| {{url|1=http://git.ozlabs.org/?p=nemo.git}}
 
−
|-
 
−
! Documentation
 
−
| {{url|http://nbviewer.jupyter.org/urls/nemo.ozlabs.org/guide.ipynb}}
 
−
|}
 
−
 
−
NEMO, the National Electricity Market Optimiser, is a chronological dispatch model for testing and optimizing different portfolios of conventional and renewable electricity generation technologies.  It applies solely to the Australian [[National Electricity Market]] (NEM), which, despite its name, is limited to east and south Australia.  NEMO has been in development at the [http://www.ceem.unsw.edu.au/ Centre for Energy and Environmental Markets] (CEEM), [[University of New South Wales]] (UNSW), [[Sydney]], Australia since 2011.<ref name="nemo-website">
 
−
{{cite web
 
−
| title = NEMO
 
−
| website = OzLabs
 
−
| location = Australia
 
−
| url = https://nemo.ozlabs.org
 
−
| access-date = 2016-12-03
 
−
}}
 
−
</ref>  The project maintains a small website and runs an [[Electronic mailing list|email list]].  NEMO is written in [[Python (programming language)|Python]].  NEMO itself is described in two publications.<ref name="elliston-etal-2012"/>{{rp|sec{{nnbsp}}2}}<ref name="elliston-etal-2016"/>{{rp|sec{{nnbsp}}2}}  The data sources are also noted.<ref name="elliston-etal-2012"/>{{rp|sec{{nnbsp}}3}}  Optimizations are carried out using a single-objective evaluation function, with penalties.  The solution space of generator capacities is searched using the [[CMA-ES]] (covariance matrix adaptation evolution strategy) algorithm.  The timestep is arbitrary but one hour is normally employed.
 
−
 
−
NEMO has been used to explore generation options for the year 2030 under a variety of renewable energy (RE) and abated fossil fuel technology scenarios.  A 2012 study investigates the feasibility of a fully renewable system using [[concentrated solar power]] (CSP) with thermal storage, [[Wind power|windfarms]], [[photovoltaics]], existing [[hydroelectricity]], and [[biofuel]]led [[gas turbine]]s.  A number of potential systems, which also meet NEM reliability criteria, are identified.  The principal challenge is servicing peak demand on winter evenings following overcast days and periods of low wind.<ref name="elliston-etal-2012">
 
−
{{cite journal
 
−
| last1 = Elliston | first1 = Ben
 
−
| last2 = Diesendorf | first2 = Mark
 
−
| last3 = MacGill | first3 = Iain
 
−
| title = Simulations of scenarios with 100% renewable electricity in the Australian National Electricity Market
 
−
| date = June 2012
 
−
| journal = Energy Policy
 
−
| volume = 45
 
−
| pages = 606–613
 
−
| doi = 10.1016/j.enpol.2012.03.011
 
−
| issn = 0301-4215
 
−
| url = https://www.researchgate.net/publication/241756578_Simulations_of_scenarios_with_100_renewable_electricity_in_the_Australian_National_Electricity_Market
 
−
| access-date = 2016-12-19
 
−
}} Preprint URL given.  This paper does not mention NEMO explicitly.
 
−
</ref>  A 2014 study investigates three scenarios using coal-fired thermal generation with [[carbon capture and storage]] (CCS) and gas-fired gas turbines with and without capture.  These scenarios are compared to the 2012 analysis using fully renewable generation.  The study finds that "only under a few, and seemingly unlikely, combinations of costs can any of the fossil fuel scenarios compete economically with 100% renewable electricity in a carbon constrained world".<ref name="elliston-etal-2014">
 
−
{{cite journal
 
−
| last1 = Elliston | first1 = Ben
 
−
| last2 = MacGill | first2 = Iain
 
−
| last3 = Diesendorf | first3 = Mark
 
−
| title = Comparing least cost scenarios for 100% renewable electricity with low emission fossil fuel scenarios in the Australian National Electricity Market
 
−
| date = June 2014
 
−
| journal = Renewable Energy
 
−
| volume = 66
 
−
| pages = 196–204
 
−
| doi = 10.1016/j.renene.2013.12.010
 
−
| issn = 0960-1481
 
−
| url = http://images.smh.com.au/file/2013/09/04/4718532/coalpaper.pdf
 
−
}} Draft URL given.
 
−
</ref>{{rp|196}}  A 2016 study evaluates the incremental costs of increasing renewable energy shares under a range of greenhouse gas caps and carbon prices.  The study finds that incremental costs increase linearly from zero to 80% RE and then escalate moderately.  The study concludes that this cost escalation is not a sufficient reason to avoid renewables targets of 100%.<ref name="elliston-etal-2016">
 
−
<!-- alternative URL: http://www.sciencedirect.com/science/article/pii/S0960148116302646 -->
 
−
{{cite journal
 
−
| last1 = Elliston | first1 = Ben
 
−
| last2 = Riesz | first2 = Jenny
 
−
| last3 = MacGill | first3 = Iain
 
−
| title = What cost for more renewables? The incremental cost of renewable generation — An Australian National Electricity Market case study
 
−
| date = September 2016
 
−
| journal = Renewable Energy
 
−
| volume = 95
 
−
| pages = 127–139
 
−
| doi = 10.1016/j.renene.2016.03.080
 
−
| issn = 0960-1481
 
−
| url = http://ceem.unsw.edu.au/sites/default/files/documents/WhatCostMoreRenewables-preprint_0.pdf
 
−
| access-date = 2016-12-03
 
−
}} Preprint URL given.
 
−
</ref>
 
−
 
−
{{clear}}
 
−
 
−
=== {{anchor|ONSSET}} OnSSET ===
 
−
 
−
{| class="infobox" style="width: 28em"
 
−
|-
 
−
! style="width: 35%" | Project
 
−
| style="width: 55%" | OnSSET
 
−
|-
 
−
! Host
 
−
| [[Royal Institute of Technology|KTH Royal Institute of Technology]]
 
−
|-
 
−
! Status
 
−
| active
 
−
|-
 
−
! Scope/type
 
−
| cost-effective electrification
 
−
|-
 
−
! Code license
 
−
| [[MIT License|MIT]]
 
−
|-
 
−
! Website
 
−
| {{url|http://www.onsset.org}}
 
−
|-
 
−
! Forum
 
−
| {{url|https://www.reddit.com/r/optimuscommunity/comments/5qi5sv/onsset_qa/}}
 
−
|-
 
−
! Repository
 
−
| {{url|https://github.com/KTH-dESA/PyOnSSET}}
 
−
|-
 
−
! Datasets
 
−
| {{url|https://energydata.info}}
 
−
|}
 
−
 
−
OnSSET is the OpeN Source Spatial Electrification Toolkit.  OnSSET is being developed by the [https://www.kth.se/en/itm/inst/energiteknik/forskning/desa Energy Systems Analysis Group] (dESA), [[Royal Institute of Technology|KTH Royal Institute of Technology]], [[Stockholm]], Sweden.  The software is used to examine areas not served by grid-based electricity and identify the technology options and investment requirements that will provide least-cost access to electricity services.  OnSSET is designed to support the [[United Nations]]' [[Sustainable Development Goals|SDG{{nnbsp}}7]]: the provision of affordable, reliable, sustainable, and modern energy for all.  The [[Python (programming language)|Python]] implementation of the toolkit is known as PyOnSSET and was released on 26{{nbsp}}November 2016.  PyOnSSET does not ship with data, but suitable datasets are available from [[Open energy system databases#energydata.info|energydata.info]].  The project maintains a website and hosts a forum on [[Reddit]].<ref name="onsset-website">
 
−
{{cite web
 
−
| title = OnSSET: open source spatial electrification tool
 
−
| work = OnSSET
 
−
| location = Stockholm, Sweden
 
−
| url = http://www.onsset.org/
 
−
| access-date = 2017-03-08
 
−
}}
 
−
</ref><ref name="kth-onsset">
 
−
{{cite web
 
−
| title = OpeN Source Spatial Electrification Toolkit (OnSSET)
 
−
| website = Department of Energy Technology, KTH Royal Institute of Technology
 
−
| location = Stockholm, Sweden
 
−
| url = https://www.kth.se/en/itm/inst/energiteknik/forskning/desa/projects/sustainable-energy-f/open-source-spatial-electrification-toolkit-onsset-1.663655
 
−
| access-date = 2016-12-05
 
−
}}
 
−
</ref><ref name="mentis-etal-2015-presentation">
 
−
{{cite conference
 
−
| last1 = Mentis | first1 = Dimitrios
 
−
| last2 = Korkovelos | first2 = Alexandros
 
−
| last3 = Shahid Siyal | first3 = Shahid
 
−
| last4 = Paritosh | first4 = Deshpante
 
−
| last5 = Broad | first5 = Oliver
 
−
| last6 = Howells | first6 = Mark
 
−
| last7 = Rogner | first7 = Holger
 
−
| date = 13 November 2015
 
−
| title = Lighting up the world: the first global application of the open source, spatial electrification tool (OnSSET) — Presentation
 
−
| conference = 2015 International Workshop on Environment and Alternative Energy
 
−
| url = http://presentations.copernicus.org/EGU2016-14161_presentation.pptx
 
−
| access-date = 2017-03-07
 
−
}}
 
−
</ref>
 
−
 
−
[[File:Least cost electricity mapping for tanzania from onsset model.png|thumb|left|A least-cost electrification mapping for Tanzania]]
 
−
 
−
OnSSET can estimate, analyze, and visualize the most cost-effective electrification access options, be they [[Electrical grid|conventional grid]], mini-grid, or stand-alone.<ref name="nerini-etal-2016">
 
−
{{cite journal
 
−
| last1 = Nerini | first1 = Francesco Fuso
 
−
| last2 = Broad | first2 = Oliver
 
−
| last3 = Mentis | first3 = Dimitris
 
−
| last4 = Welsch | first4 = Manuel
 
−
| last5 = Bazilian | first5 = Morgan
 
−
| last6 = Howells | first6 = Mark
 
−
| date = 15 January 2016
 
−
| title = A cost comparison of technology approaches for improving access to electricity services
 
−
| journal = Energy
 
−
| volume = 95
 
−
| pages = 255–265
 
−
| doi = 10.1016/j.energy.2015.11.068
 
−
| issn = 0360-5442
 
−
}}
 
−
</ref>  The toolkit supports a range of conventional and renewable energy technologies, including photovoltaics, wind turbines, and [[small hydro]] generation.  {{as of|2017}}, [[bioenergy]] and hybrid technologies, such as [[Wind hybrid power systems|wind-diesel]], are being added.
 
−
 
−
OnSSET utilizes energy and geographic information, the latter may include settlement size and location, existing and planned transmission and generation infrastructure, economic activity, renewable energy resources, roading networks, and nighttime lighting needs.  The [[Geographic information system|GIS]] information can be supported using the proprietary [[ArcGIS]] package or an open source equivalent such as [[GRASS GIS|GRASS]] or [[QGIS]].<ref name="berndtsson-2016-msc">
 
−
{{cite thesis
 
−
| last = Berndtsson | first = Carl
 
−
| title = Open geospatial data for energy planning
 
−
| type = MSc
 
−
| date = 2016
 
−
| publisher = KTH School of Industrial Engineering and Management
 
−
| location = Stockholm, Sweden
 
−
| url = http://www.diva-portal.org/smash/get/diva2:927179/FULLTEXT02
 
−
| access-date = 2017-03-07
 
−
}}
 
−
</ref>
 
−
 
−
OnSSET has been used for case studies in [[Bolivia]],<ref name="arderne-2016-msc">
 
−
{{cite thesis
 
−
| last = Arderne | first = Christopher
 
−
| title = A climate, land-use, energy and water nexus assessment of Bolivia
 
−
| type = MSc
 
−
| date = June 2016
 
−
| publisher = KTH School of Industrial Engineering and Management
 
−
| location = Stockholm, Sweden
 
−
| url = http://kth.diva-portal.org/smash/get/diva2:946272/FULLTEXT01.pdf
 
−
| access-date = 2017-03-07
 
−
}}
 
−
</ref> [[Ethiopia]],<ref name="nerini-etal-2016"/><ref name="mentis-etal-2016">
 
−
{{cite journal
 
−
| last1 = Mentis | first1 = Dimitrios
 
−
| last2 = Andersson | first2 = Magnus
 
−
| last3 = Howells | first3 = Mark
 
−
| last4 = Rogner | first4 = Holger
 
−
| last5 = Siyal | first5 = Shahid
 
−
| last6 = Broad | first6 = Oliver
 
−
| last7 = Korkovelos | first7 = Alexandros
 
−
| last8 = Bazilian | first8 = Morgan
 
−
| date = July 2016
 
−
| title = The benefits of geospatial planning in energy access: a case study on Ethiopia
 
−
| journal = Applied Geography
 
−
| volume = 72
 
−
| pages = 1–13
 
−
| doi = 10.1016/j.apgeog.2016.04.009
 
−
| issn = 0143-6228
 
−
}}
 
−
</ref> [[Nigeria]],<ref name="nerini-etal-2016"/><ref name="mentis-etal-2015">
 
−
{{cite journal
 
−
| last1 = Mentis | first1 = Dimitrios
 
−
| last2 = Welsch | first2 = Manuel
 
−
| last3 = Fuso Nerini | first3 = Francesco
 
−
| last4 = Broad | first4 = Oliver
 
−
| last5 = Howells | first5 = Mark
 
−
| last6 = Bazilian | first6 = Morgan
 
−
| last7 = Rogner | first7 = Holger
 
−
| date = December 2015
 
−
| title = A GIS-based approach for electrification planning: a case study on Nigeria
 
−
| journal = Energy for Sustainable Development
 
−
| volume = 29
 
−
| pages = 142–150
 
−
| doi = 10.1016/j.esd.2015.09.007
 
−
| issn = 0973-0826
 
−
}}
 
−
</ref> and [[Tanzania]].<ref name="berndtsson-2016-msc"/>  OnSSET has also been applied in [[Afghanistan]], [[India]], [[Kenya]], and [[Zimbabwe]].  In addition, continental studies have been carried out for [[Sub-Saharan Africa]] and [[Latin America]].<ref name="desa-electrification-website">
 
−
{{cite web
 
−
| title = Universal electrification access
 
−
| work = United Nations Department of Economic and Social Affairs (UN DESA)
 
−
| location = New York, USA
 
−
| url = http://un-desa-modelling.github.io/electrification-paths-presentation/
 
−
| access-date = 2017-03-09
 
−
}}
 
−
</ref>  {{as of|2017}}, there are plans to apply OnSSET in developing Asia, to increase the resolution of the analysis, and to extend support for various productive uses of electricity.
 
−
 
−
OnSSET results have contributed to the [[International Energy Agency|IEA]] ''World Energy Outlook'' reports for 2014{{nnbsp}}<ref name="iea-weo-2014">
 
−
{{cite book
 
−
| author = International Energy Agency
 
−
| title = World Energy Outlook 2014
 
−
| date = 2014
 
−
| publisher = OECD/IEA
 
−
| location = Paris, France
 
−
| isbn = 978-92-64-20805-6
 
−
| url = https://www.iea.org/publications/freepublications/publication/WEO2014.pdf
 
−
| access-date = 2017-03-09
 
−
}}
 
−
</ref> and 2015{{nnbsp}}<ref name="iea-weo-2015">
 
−
{{cite book
 
−
| author = International Energy Agency
 
−
| title = World Energy Outlook 2015
 
−
| date = 2015
 
−
| publisher = OECD/IEA
 
−
| location = Paris, France
 
−
| isbn = 978-92-64-24366-8
 
−
}}
 
−
</ref> and the World Bank Global Tracking Framework report in 2015.<ref name="iea-world-bank-sustainable-energy-2015">
 
−
{{cite book
 
−
| author = International Energy Agency (IEA) and the World Bank
 
−
| title = Sustainable energy for all 2015: progress toward sustainable energy
 
−
| date = June 2015
 
−
| publisher = World Bank
 
−
| location = Washington DC, USA
 
−
| isbn = 978-1-4648-0690-2
 
−
| doi = 10.1596/978-1-4648-0690-2
 
−
| url = http://www.se4all.org/sites/default/files/GTF-2105-Full-Report.pdf
 
−
| access-date = 2017-03-09
 
−
}}  Licensed under Creative Commons {{nowrap|CC BY 3.0 IGO}}.
 
−
</ref>
 
−
 
−
{{clear}}
 
−
 
−
=== pandapower ===
 
−
 
−
{| class="infobox" style="width: 28em"
 
−
|-
 
−
! style="width: 35%" | Project
 
−
| style="width: 55%" | pandapower
 
−
|-
 
−
! Host
 
−
| {{plainlist|
 
−
* [[University of Kassel]]
 
−
* Fraunhofer Institute IWES
 
−
}}
 
−
|-
 
−
! Status
 
−
| active
 
−
|-
 
−
! Scope/type
 
−
| automated power system analysis
 
−
|-
 
−
! Code license
 
−
| [[BSD-new]]
 
−
|-
 
−
! Website
 
−
| {{url|http://www.uni-kassel.de/go/pandapower}}
 
−
|-
 
−
! Repository
 
−
| {{url|https://github.com/lthurner/pandapower}}
 
−
|-
 
−
! Documentation
 
−
| {{url|http://www.uni-kassel.de/go/pp_docs}}
 
−
|}
 
−
 
−
pandapower is a power system analysis and optimization program being jointly developed by the Energy Management and Power System Operation research group, [[University of Kassel]] and the Department for Distribution System Operation, [[Fraunhofer Institute]] for Wind Energy and Power Systems Technology (IWES), both of [[Kassel]], Germany.  The codebase is hosted on [[GitHub]].  The project maintains a website, an [[Electronic mailing list|emailing list]], and online documentation.  PDF documentation is also available.<ref name="thurner-etal-2016">
 
−
<!-- this PDF was obtained via google on 2 December 2016, but the URL later failed -->
 
−
{{cite book
 
−
| first1 = Leon | last1 = Thurner
 
−
| first2 = Alexander | last2 = Scheidler
 
−
| first3 = Julian | last3 = Dollichon
 
−
| first4 = Friederike | last4 = Meier
 
−
| title = pandapower: convenient power system modelling and analysis based on PYPOWER and pandas — Version 1.0.2
 
−
| date = 30 November 2016
 
−
| publisher = Fraunhofer IWES and Universität Kassel
 
−
| location = Kassel, Germany
 
−
}}
 
−
</ref>  pandapower is written in [[Python (programming language)|Python]].  It uses the [[pandas (software)|pandas]] library for data manipulation and analysis and the PYPOWER library{{nnbsp}}<ref name="pypower-website">
 
−
{{cite web
 
−
| title = PYPOWER
 
−
| website = Python Software Foundation
 
−
| location =  Beaverton, OR, USA
 
−
| url = https://pypi.python.org/pypi/PYPOWER
 
−
| access-date = 2016-12-02
 
−
}}
 
−
</ref> to solve for [[Power-flow study|power flow]].
 
−
 
−
pandapower supports the automated analysis and optimization of [[Electric power distribution|distribution]] and sub-transmission networks.  This allows a large of number of scenarios to be explored, based on different future grid configurations and technologies.  pandapower offers a collection of power system elements, including: lines, 2-winding transformers, 3-winding transformers, and ward-equivalents.  It also contains a switch model that allows the modeling of ideal bus-bus switches as well as bus-line/bus-trafo switches.  The software supports topological searching.  The network can be plotted, with or without geographical information, using the [[matplotlib]] library.
 
−
 
−
A 2016 article evaluates the usefulness of the software by undertaking several case studies with major distribution system operators (DSO).  These studies examine the integration of increasing levels of [[photovoltaics]] into existing distribution grids.  The authors conclude that being able to test a large number of detailed scenarios is essential for robust grid planning.  Notwithstanding, issues of data availability and problem dimensionality will continue to present challenges.<ref name="scheidler-etal-2016">
 
−
{{cite conference
 
−
| first1 = Alexander | last1 = Scheidler
 
−
| first2 = Leon | last2 = Thurner
 
−
| first3 = Markus | last3 = Kraiczy
 
−
| first4 = Martin | last4 = Braun
 
−
| title = Automated grid planning for distribution grids with increasing PV penetration
 
−
| date = 14–15 November 2016
 
−
| conference = 6th Solar Integration Workshop: International Workshop on Integration of Solar Power into Power Systems
 
−
| conference-url = http://solarintegrationworkshop.org/vienna2016/
 
−
| location = Vienna, Austria
 
−
| url = https://www.uni-kassel.de/eecs/fileadmin/datas/fb16/Fachgebiete/energiemanagement/Mitarbeitende/Scheidler__Thurner__Kraiczy__Braun_-_Automated_Grid_Planning_for_Distribution_Grids_with_Increasing_PV_Penetration.pdf
 
−
| access-date = 2016-12-02
 
−
}}
 
−
</ref>
 
−
 
−
{{clear}}
 
−
 
−
=== PowerMatcher ===
 
−
 
−
{| class="infobox" style="width: 28em"
 
−
|-
 
−
! style="width: 35%" | Project
 
−
| style="width: 55%" | PowerMatcher
 
−
|-
 
−
! Host
 
−
| [http://flexible-energy.eu/ Flexiblepower Alliance Network]
 
−
|-
 
−
! Status
 
−
| active
 
−
|-
 
−
! Scope/type
 
−
| smart grid
 
−
|-
 
−
! Code license
 
−
| [[Apache License|Apache 2.0]]
 
−
|-
 
−
! Website
 
−
| {{url|http://flexiblepower.github.io}}
 
−
|-
 
−
! Repository
 
−
| {{url|https://github.com/flexiblepower/powermatcher}}
 
−
|}
 
−
 
−
The PowerMatcher software implements a [[smart grid]] coordination mechanism which balances [[Distributed generation|distributed energy resources]] (DER) and flexible loads through autonomous [[bidding]].  The project is managed by the Flexiblepower Alliance Network (FAN) in [[Amsterdam]], the Netherlands.  The project maintains a website and the [[source code]] is hosted on [[GitHub]].  {{as of|2016|06}}, existing datasets are not available.  PowerMatcher is written in [[Java (programming language)|Java]].
 
−
 
−
Each device in the smart grid system – whether a washing machine, a wind generator, or an industrial turbine – expresses its willingness to consume or produce electricity in the form of a bid.  These bids are then collected and used to determine an equilibrium price.  The PowerMatcher software thereby allows high shares of renewable energy to be integrated into existing electricity systems and should also avoid any local overloading in possibly aging distribution networks.<ref name="kok-2013">
 
−
{{cite thesis
 
−
| first = Koen | last = Kok
 
−
| title = The PowerMatcher: smart coordination for the smart electricity grid
 
−
| type = PhD
 
−
| date = 13 May 2013
 
−
| publisher = [[Vrije Universiteit Amsterdam]]
 
−
| location = Amsterdam, The Netherlands
 
−
| url = http://dare.ubvu.vu.nl/bitstream/handle/1871/43567/dissertation.pdf
 
−
| access-date = 2016-07-08
 
−
}}
 
−
</ref>
 
−
 
−
{{clear}}
 
−
 
−
=== PyPSA ===
 
−
 
−
{| class="infobox" style="width: 28em"
 
−
|-
 
−
! style="width: 35%" | Project
 
−
| style="width: 55%" | PyPSA
 
−
|-
 
−
! Host
 
−
| [[Goethe University Frankfurt]]
 
−
|-
 
−
! Status
 
−
| active
 
−
|-
 
−
! Scope/type
 
−
| electric power systems
 
−
|-
 
−
! Code license
 
−
| [[GNU General Public License|GPLv3]]
 
−
|-
 
−
! Website
 
−
| {{url|http://www.pypsa.org}}
 
−
|-
 
−
! Repository
 
−
| {{url|https://github.com/FRESNA/PyPSA}}
 
−
|}
 
−
 
−
PyPSA stands for Python for Power System Analysis.  PyPSA is a free software toolbox for simulating and optimizing electric power systems.  It features variable wind and solar generation, electricity storage, and mixed alternating and direct current networks.  PyPSA is designed to scale well with large networks and long time series.  The project is managed by the [https://fias.uni-frankfurt.de/physics/schramm/renewable-energy-system-and-network-analysis/teaching/complex-renewable-energy-networks/ Frankfurt Institute of Advanced Studies] (FIAS), [[Goethe University Frankfurt]], [[Frankfurt]] Germany.  The project maintains a website.  The [[source code]] is hosted on [[GitHub]].  The project runs a [[Electronic mailing list|emailing list]].  A manual is available.<ref name="brown-etal-2016">
 
−
{{cite book
 
−
| last1 = Brown | first1 = Tom
 
−
| last2 = Hörsch | first2 = Jonas
 
−
| last3 = Schlachtberger | first3 = David
 
−
| title = PyPSA: Python for power system analysis — Version 0.7.1
 
−
| date = 2016
 
−
| url = http://pypsa.org
 
−
| access-date = 2016-12-17
 
−
}}
 
−
</ref>  PyPSA is written in [[Python (programming language)|Python]] and uses the [[Pyomo]] library.
 
−
 
−
{{clear}}
 
−
 
−
=== renpass ===
 
−
 
−
{| class="infobox" style="width: 28em"
 
−
|-
 
−
! style="width: 35%" | Project
 
−
| style="width: 55%" | renpass
 
−
|-
 
−
! Host
 
−
| [[University of Flensburg]]
 
−
|-
 
−
! Status
 
−
| active
 
−
|-
 
−
! Scope/type
 
−
| renewables pathways
 
−
|-
 
−
! Code license
 
−
| [[GNU General Public License|GPLv3]]
 
−
|-
 
−
! Website
 
−
| {{url|https://github.com/fraukewiese/renpass}}
 
−
<!-- alternative: {{https://www.uni-flensburg.de/index.php?id=18227}} -->
 
−
|}
 
−
 
−
renpass is an acronym for Renewable Energy Pathways Simulation System.  renpass is a simulation electricity model with high regional and temporal resolution, designed to capture existing systems and future systems with up to 100% renewable generation.  The software is being developed by the [http://www.znes-flensburg.de/index.php?id=165&L=1 Centre for Sustainable Energy Systems] (CSES or ZNES), [[University of Flensburg]], Germany.  The project runs a website, from where the [[codebase]] can be download.  renpass is written in [[R (programming language)|R]] and links to a [[MySQL]] database.  A PDF manual is available.<ref name="wiese-2014">
 
−
{{cite book
 
−
| first = Frauke | last = Wiese
 
−
| title = renpass: Renewable Energy Pathways Simulation System — Manual
 
−
| date = 16 November 2014
 
−
| url = https://github.com/fraukewiese/renpass/blob/master/docs/manual_renpass_11_2014.pdf
 
−
| access-date = 2017-03-13
 
−
}}
 
−
</ref>  renpass is also described in a PhD thesis.<ref name="wiese-2015">
 
−
<!-- alternative url: http://www.coastdat.de/imperia/md/content/coastdat/publications/dissertation_frauke_wiese_april2015_digitalversion.pdf -->
 
−
{{cite thesis
 
−
| first = Frauke | last = Wiese
 
−
| title = renpass: Renewable Energy Pathways Simulation System: Open source as an approach to meet challenges in energy modeling
 
−
| type = PhD
 
−
| year = 2015
 
−
| publisher = Shaker Verlag
 
−
| location = Aachen, Germany
 
−
| isbn = 978-3-8440-3705-0
 
−
| url = http://www.reiner-lemoine-stiftung.de/pdf/dissertationen/Dissertation_Frauke_Wiese.pdf
 
−
| access-date = 2016-07-12
 
−
}} University of Flensburg, Flensburg, Germany.
 
−
</ref>  {{as of|2015}}, renpass is being extended as renpassG!S, based on [[#oemof|oemof]].
 
−
 
−
renpass is an electricity dispatch model which minimizes system costs for each time step (optimization) within the limits of a given infrastructure (simulation).  Time steps are optionally 15 minutes or one hour.  The method assumes perfect foresight.  renpass supports the electricity systems found in Austria, Belgium, the Czech Republic, Denmark, Estonia, France, Finland, Germany, Latvia, Lithuania, Luxembourg, the Netherlands, Norway, Poland, Sweden, and Switzerland.
 
−
 
−
The optimization problem for each time step is to minimize the electricity supply cost using the existing power plant fleet for all regions.  After this regional dispatch, the exchange between the regions is carried out and is restricted by the grid capacity.  This latter problem is solved with a heuristic procedure rather than calculated deterministically.  The input is the merit order, the marginal power plant, the excess energy (renewable energy that could be curtailed), and the excess demand (the demand that cannot be supplied) for each region.  The exchange algorithm seeks the least cost for all regions, thus the target function is to minimize the total costs of all regions, given the existing grid infrastructure, storage, and generating capacities.  The total cost is defined as the residual load multiplied by the price in each region, summed over all regions.
 
−
 
−
A 2012 study uses renpass to examine the feasibility of a 100% renewable electricity system for the [[Baltic Sea]] region (Denmark, Estonia, Finland, Germany, Latvia, Lithuania, Poland, and Sweden) in the year 2050.  The base scenario presumes conservative renewable potentials and grid enhancements, a 20% drop in demand, a moderate uptake of storage options, and the deployment of biomass for flexible generation.  The study finds that a 100% renewable electricity system is possible, albeit with occasional imports from abutting countries, and that biomass plays a key role in system stability.  The costs for this transition are estimated at 50{{nbsp}}€/MWh.<ref name="bernhardi-etal-2012">
 
−
{{cite book
 
−
| last1 = Bernhardi | first1 = Nicolas
 
−
| last2 = Bökenkamp | first2 = Gesine
 
−
| last3 = Bons | first3 = Marian
 
−
| last4 = Borrmann | first4 = Rasmus
 
−
| last5 = Christ | first5 = Marion
 
−
| last6 = Grüterich | first6 = Lauren
 
−
| last7 = Heidtmann | first7 = Emilie
 
−
| last8 = Jahn | first8 = Martin
 
−
| last9 = Janssen | first9 = Tomke
 
−
| last10 = Lesch | first10 = Jonas
 
−
| last11 = Müller | first11 = Ulf Philipp
 
−
| last12 = Pelda | first12 = Johannes
 
−
| last13 = Stein | first13 = Isabelle
 
−
| last14 = Veddeler | first14 = Eike
 
−
| last15 = Voß | first15 = David
 
−
| last16 = Wienholt | first16 = Lukas
 
−
| last17 = Wiese | first17 = Frauke
 
−
| last18 = Wingenbach | first18 = Clemens
 
−
| title = Modeling sustainable electricity systems for the Baltic Sea region — Discussion paper 3
 
−
| date = November 2012
 
−
| publisher = Centre for Sustainable Energy Systems (CSES), University of Flensburg
 
−
| location = Flensburg, Germany
 
−
| issn = 2192-4597
 
−
| url = http://www.znes.fh-flensburg.de/fileadmin/templates/multiflex4/Downloads/Reports/Sustainable_electricity_System_Baltic_Region.pdf
 
−
| access-date = 2016-06-17
 
−
}}
 
−
</ref>  A 2014 study uses renpass to model Germany and its neighbors.<ref name="wiechers-etal-2014">
 
−
{{cite book
 
−
| first1 = Eva | last1 = Wiechers
 
−
| first2 = Hendrik | last2 = Böhm
 
−
| first3 = Wolf Dieter | last3 = Bunke
 
−
| first4 = Cord | last4 = Kaldemeyer
 
−
| first5 = Tim | last5 = Kummerfeld
 
−
| first6 = Martin | last6 = Söthe
 
−
| first7 = Henning | last7 = Thiesen
 
−
| title = Modelling sustainable electricity systems for Germany and neighbours in 2050
 
−
| year = 2014
 
−
| publisher = Centre for Sustainable Energy Systems (CSES), University of Flensburg
 
−
| location = Flensburg, Germany
 
−
}}
 
−
</ref>  A 2014 thesis uses renpass to examine the benefits of both a new cable between Germany and Norway and new [[Pumped-storage hydroelectricity|pumped storage]] capacity in [[Norway]], given 100% renewable electricity systems in both countries.<ref name="boekenkamp-2014">
 
−
{{cite thesis
 
−
| last = Bökenkamp | first = Gesine
 
−
| date = October 2014
 
−
| title = The role of Norwegian hydro storage in future renewable electricity supply systems in Germany: analysis with a simulation model
 
−
| type = PhD
 
−
| publisher = University of Flensburg
 
−
| location = Flensburg, German
 
−
| url = https://www.zhb-flensburg.de/fileadmin/content/spezial-einrichtungen/zhb/dokumente/dissertationen/boekenkamp/dissertation-boekenkamp.pdf
 
−
| access-date = 2016-07-12
 
−
}}
 
−
</ref>  Another 2014 study uses renpass to examine the German ''[[Energiewende]]'', the transition to a sustainable energy system for Germany.  The study also argues that the public trust needed to underpin such a transition can only be built through the use of transparent open source energy models.<ref name="wiese-etal-2014">
 
−
{{cite journal
 
−
| last1 = Wiese | first1 = Frauke
 
−
| last2 = Bökenkamp | first2 = Gesine
 
−
| last3 = Wingenbach | first3 = Clemens
 
−
| last4 = Hohmeyer | first4 = Olav
 
−
| title = An open source energy system simulation model as an instrument for public participation in the development of strategies for a sustainable future
 
−
| year = 2014
 
−
| publisher = Wiley Interdisciplinary Reviews: Energy and Environment
 
−
| volume = 3
 
−
| number = 5
 
−
| pages = 490–504
 
−
| issn = 2041-840X
 
−
| doi = 10.1002/wene.109
 
−
}}
 
−
</ref>
 
−
 
−
{{clear}}
 
−
 
−
=== SciGRID ===
 
−
 
−
{| class="infobox" style="width: 28em"
 
−
|-
 
−
! style="width: 35%" | Project
 
−
| style="width: 55%" | SciGRID
 
−
|-
 
−
! Host
 
−
| [[University of Oldenburg]]
 
−
|-
 
−
! Status
 
−
| active
 
−
|-
 
−
! Scope/type
 
−
| European transmission grid
 
−
|-
 
−
! Code license
 
−
| [[Apache License|Apache 2.0]]
 
−
|-
 
−
! Website
 
−
| {{url|http://www.scigrid.de}}
 
−
|}
 
−
 
−
SciGRID, short for Scientific Grid, is an open source model of the German and European [[Electric power transmission|electricity transmission networks]].  The research project is managed by [http://www.next-energy.de/en/ Next Energy] (officially the EWE Research Centre for Energy Technology) located at the [[University of Oldenburg]], [[Oldenburg (Oldenburg)|Oldenburg]], Germany.  The project maintains a website and an email newsletter.  SciGRID is written in [[Python (programming language)|Python]] and uses a [[PostgreSQL]] database.  The first release (v0.1) was made on 15{{nbsp}}June 2015.
 
−
 
−
SciGRID aims to rectify the lack of open research data on the structure of electricity transmission networks within Europe.  This lack of data frustrates attempts to build, characterise, and compare high resolution energy system models.  SciGRID utilizes transmission network data available from the [[OpenStreetMap]] project, available under the [[Open Database License]] (ODbL), to automatically author transmission connections.  SciGRID will not use data from closed sources.  SciGRID can also mathematically decompose a given network into a simpler representation for use in energy models.<ref name="matke-etal-2015">
 
−
{{cite conference
 
−
| first1 = Carsten | last1 = Matke
 
−
| first2 = Wided | last2 = Medjroubi
 
−
| first3 = David | last3 = Kleinhans
 
−
| title = SciGRID: an open source model of the European power transmission network — Poster
 
−
| year = 2015
 
−
| conference = Mathematics and Physics of Multilayer Complex Networks
 
−
| location = Dresden, Germany
 
−
| url = http://www.scigrid.de/publications/15_dresden_poster.pdf
 
−
| access-date = 2016-07-08
 
−
}}</ref><ref name="wiegmans-2015">
 
−
{{cite book
 
−
| last = Wiegmans | first = Bart
 
−
| title = Improving the topology of an electric network model based on Open Data
 
−
| type = MSc
 
−
| year = 2015
 
−
| publisher = Energy and Sustainability Research Institute, [[University of Groningen]]
 
−
| location = Groningen, The Netherlands
 
−
| url = http://www.scigrid.de/publications/16_1_BWiegmans_Master_Thesis_2015.pdf
 
−
| access-date = 2016-07-08
 
−
}}
 
−
</ref>
 
−
 
−
A related project is [https://github.com/bdw/GridKit GridKit], released under an [[MIT license]].  GridKit is being developed to investigate the possibility of a 'heuristic' analysis to augment the route-based analysis used in SciGRID.  Data is available for network models of the European and North-American high-voltage electricity grids.<ref name="wiegmans-47317">
 
−
{{cite journal
 
−
| last = Wiegmans | first = Bart
 
−
| title = GridKit: European and North-American extracts
 
−
| journal = Zenodo
 
−
| doi = 10.5281/zenodo.47317
 
−
| url = https://zenodo.org/record/47317
 
−
| access-date = 2016-12-06
 
−
}}
 
−
</ref>
 
−
 
−
{{clear}}
 
−
 
−
=== SIREN ===
 
−
 
−
{| class="infobox" style="width: 28em"
 
−
|-
 
−
! style="width: 35%" | Project
 
−
| style="width: 55%" | SIREN
 
−
|-
 
−
! Host
 
−
| [http://www.sen.asn.au/ Sustainable Energy Now]
 
−
|-
 
−
! Status
 
−
| active
 
−
|-
 
−
! Scope/type
 
−
| renewable generation
 
−
|-
 
−
! Code license
 
−
| [[Affero General Public License|AGPLv3]]
 
−
|-
 
−
! Website
 
−
| {{url|http://www.sen.asn.au/modelling_overview}}
 
−
|-
 
−
! Repository
 
−
| {{url|https://sourceforge.net/projects/sensiren/}}
 
−
|}
 
−
 
−
SIREN stands for SEN Integrated Renewable Energy Network Toolkit.  The project is run by Sustainable Energy Now, an [[Non-governmental organization|NGO]] based in [[Perth]], Australia.  The project maintains a website.  SIREN runs on Windows and the [[source code]] is hosted on [[SourceForge]].  The software is written in [[Python (computer language)|Python]] and uses the SAM model (System Advisor Model) from the US [[National Renewable Energy Laboratory]] to perform energy calculations.  SIREN uses hourly datasets to model a given geographic region.  Users can use the software to explore the location and scale of renewable energy sources to meet a specified electricity demand.  SIREN utilizes a number of open or publicly available data sources: maps can be created from [[OpenStreetMap]] tiles and weather datasets can be created using [[NASA]] MERRA-2 satellite data.{{efn|name=merra-2|
 
−
MERRA-2 stands for Modern-Era Retrospective analysis for Research and Applications, Version 2.  The [[Remote sensing|remote-sensed]] data is provided unencumbered by the [[NASA]] [[Goddard Space Flight Center]] research laboratory.
 
−
}}<ref name="bosilovich-etal-2016">
 
−
{{cite book
 
−
| first1 = Michael G | last1 = Bosilovich
 
−
| first2 = Rob | last2 = Lucches
 
−
| first3 = M | last3 = Suarez
 
−
| title = MERRA-2: File specification — GMAO Office Note No. 9 (Version 1.1)
 
−
| date = 12 March 2016
 
−
| publisher = Global Modeling and Assimilation Office (GMAO), Earth Sciences Division, NASA Goddard Space Flight Center
 
−
| location = Greenbelt, Maryland, USA
 
−
| url = https://gmao.gsfc.nasa.gov/pubs/docs/Bosilovich785.pdf
 
−
| access-date = 2016-07-08
 
−
}}
 
−
</ref>
 
−
 
−
A 2016 study using SIREN to analyze Western Australia's South-West Interconnected System (SWIS) finds that it can transition to 85% renewable energy (RE) for the same cost as new coal and gas.  In addition, 11.1{{nbsp}}million tonnes of {{CO2}}eq emissions would be avoided.  The modeling assumes a carbon price of [[Australian dollar|AUD]]{{nbsp}}$30/t{{CO2}}.  Further scenarios examine the goal of 100% renewable generation.<ref name="rose-2016">
 
−
{{cite book
 
−
| first = Ben | last = Rose
 
−
| title = Clean electricity Western Australia 2030: modelling renewable energy scenarios for the South West Integrated System
 
−
| date = April 2016
 
−
| publisher = Sustainable Energy Now
 
−
| location = West Perth, WA, Australia
 
−
| url = https://d3n8a8pro7vhmx.cloudfront.net/sen/pages/134/attachments/original/1464007346/RE_Scenarios_for_SWIS_2030_Study_-_April_2016_BR.pdf?1464007346
 
−
| access-date = 2016-07-08
 
−
}}
 
−
</ref>
 
−
 
−
{{clear}}
 
−
 
−
=== SWITCH ===
 
−
 
−
{| class="infobox" style="width: 28em"
 
−
|-
 
−
! style="width: 35%" | Project
 
−
| style="width: 55%" | SWITCH
 
−
|-
 
−
! Host
 
−
| [[University of Hawai'i]]
 
−
|-
 
−
! Status
 
−
| active
 
−
|-
 
−
! Scope/type
 
−
| optimal planning
 
−
|-
 
−
! Code license
 
−
| [[Apache License|Apache 2.0]]
 
−
|-
 
−
! Website
 
−
| {{url|http://switch-model.org}}
 
−
|-
 
−
! Repository
 
−
| {{url|https://github.com/switch-model}}
 
−
|}
 
−
 
−
SWITCH is a loose acronym for solar, wind, conventional and hydroelectric generation, and transmission.  SWITCH is an optimal planning model for power systems with large shares of renewable energy.  SWITCH is being developed by the Department of Electrical Engineering, [[University of Hawai'i]], [[Mānoa]], [[Hawaii]], USA.  The project runs a small website and hosts its [[codebase]] and [[Data (computing)|datasets]] on [[GitHub]].  SWITCH is written in [[Pyomo]], an optimization components library programmed in [[Python (programming language)|Python]].  It can use either the open source [[GLPK]] solver or the commercial [[CPLEX]] and [[Gurobi]] solvers.
 
−
 
−
SWITCH is a power system model, focused on renewables integration.  It can identify which generator and transmission projects to build in order to satisfy electricity demand at the lowest cost over a several year period while also reducing {{CO2}} emissions.  SWITCH utilizes multi-stage [[Stochastic linear program|stochastic linear optimization]] with the objective of minimizing the present value of the cost of power plants, transmission capacity, fuel usage, and an arbitrary per-tonne {{CO2}} charge (to represent either a carbon tax or a certificate price), over the course of a multi-year investment period.  It has two major sets of decision variables.  First, at the start of each investment period, SWITCH selects how much generation capacity to build in each of several geographic load zones, how much power transfer capability to add between these zones, and whether to operate existing generation capacity during the investment period or to temporarily mothball it to avoid fixed operation and maintenance costs.  Second, for a set of sample days within each investment period, SWITCH makes hourly decisions about how much power to generate from each dispatchable power plant, store at each [[Pumped-storage hydroelectricity|pumped hydro]] facility, or transfer along each transmission interconnector.  The system must also ensure enough generation and transmission capacity to provide a planning reserve margin of 15% above the load forecasts.  For each sampled hour, SWITCH uses electricity demand and renewable power production based on actual measurements, so that the weather-driven correlations between these elements remain intact.
 
−
 
−
Following the optimization phase, SWITCH is used in a second phase to test the proposed investment plan against a more complete set of weather conditions and to add backstop generation capacity so that the planning reserve margin is always met.  Finally, in a third phase, the costs are calculated by freezing the investment plan and operating the proposed power system over a full set of weather conditions.
 
−
 
−
A 2012 paper uses [[California]] from 2012 to 2027 as a [[case study]] for SWITCH.  The study finds that there is no ceiling on the amount of wind and solar power that could be used and that these resources could potentially reduce emissions by 90% or more (relative to 1990 levels) without reducing reliability or severely raising costs.  Furthermore, policies that encourage electricity customers to shift demand to times when renewable power is most abundant (for example, though the well-timed charging of [[electric vehicle]]s) could achieve radical emission reductions at moderate cost.<ref name="fripp-2012">
 
−
<!-- alternative URL: http://www.academia.edu/download/21117660/Switch_Calif_paper_and_supp_info.pdf -->
 
−
{{cite journal
 
−
| last = Fripp | first = Matthius
 
−
| date = 2012
 
−
| title = Switch: a planning tool for power systems with large shares of intermittent renewable energy
 
−
| journal = Environmental Science and Technology
 
−
| volume = 46
 
−
| number = 11
 
−
| pages = 6371–6378
 
−
| doi = 10.1021/es204645c
 
−
| issn = 0013-936X
 
−
| url = http://www2.hawaii.edu/~mfripp/papers/Fripp_2012_Switch_Calif_Renewables.pdf
 
−
| access-date = 2016-07-11
 
−
}}
 
−
</ref>
 
−
 
−
{{clear}}
 
−
 
−
=== URBS ===
 
−
 
−
{| class="infobox" style="width: 28em"
 
−
|-
 
−
! style="width: 35%" | Project
 
−
| style="width: 55%" | URBS
 
−
|-
 
−
! Host
 
−
| [[Technical University of Munich]]
 
−
|-
 
−
! Status
 
−
| active
 
−
|-
 
−
! Scope/type
 
−
| distributed energy systems
 
−
|-
 
−
! Code license
 
−
| [[GNU General Public License|GPLv3]]
 
−
|-
 
−
! Repository
 
−
| {{url|https://github.com/tum-ens/urbs}}
 
−
|}
 
−
 
−
URBS, [[Latin]] for city, is a [[linear programming]] model for exploring capacity expansion and unit commitment problems and is particularly suited to [[Distributed generation|distributed energy systems]] (DES).  It is being developed by the [https://www.ens.ei.tum.de/en/ Institute for Renewable and Sustainable Energy Systems], [[Technical University of Munich]], Germany.  The [[codebase]] is hosted on [[GitHub]].  URBS is written in [[Python (programming language)|Python]] and uses the [[Pyomo]] optimization packages.
 
−
 
−
URBS classes as an energy modeling framework and attempts to minimize the total discounted cost of the system.  A particular model selects from a set of technologies to meet a predetermined electricity demand.  It uses a time resolution of one hour and the spatial resolution is model-defined.  The decision variables are the capacities for the production, storage, and transport of electricity and the time scheduling for their operation.<ref name="huber-etal-2012">
 
−
{{cite book
 
−
| first1 = Matthias | last1 = Huber
 
−
| first2 = Johannes | last2 = Dorfner
 
−
| first3 = Thomas | last3 = Hamacher
 
−
| title = Electricity system optimization in the EUMENA region — Technical report
 
−
| date = 18 January 2012
 
−
| publisher = Institute for Energy Economy and Application Technology, Technical University of Munich
 
−
| location = Munich, Germany
 
−
| doi = 10.14459/2013md1171502
 
−
| url = https://mediatum.ub.tum.de/doc/1171502/1171502.pdf
 
−
| access-date = 2016-07-07
 
−
}}
 
−
</ref>{{rp|11–14}}
 
−
 
−
The software has been used to explore cost-optimal extensions to the European [[Electric power transmission|transmission grid]] using projected wind and solar capacities for 2020.  A 2012 study, using high spatial and technological resolutions, found [[variable renewable energy]] (VRE) additions cause lower revenues for conventional power plants and that grid extensions redistribute and alleviate this effect.<ref name="schaber-etal-2012">
 
−
{{cite journal
 
−
| first1 = Katrin | last1 = Schaber
 
−
| first2 = Florian | last2 = Steinke
 
−
| first3 = Thomas | last3 = Hamacher
 
−
| title = Transmission grid extensions for the integration of variable renewable energies in Europe: who benefits where?
 
−
| date = April 2012
 
−
| journal = Energy Policy
 
−
| volume = 43
 
−
| pages = 123–135
 
−
| doi = 10.1016/j.enpol.2011.12.040
 
−
}}
 
−
</ref>  The software has also been used to explore energy systems spanning Europe, the Middle East, and North Africa (EUMENA)<ref name="huber-etal-2012"/> and Indonesia, Malaysia, and Singapore.<ref name="stich-etal-2014">
 
−
{{cite conference
 
−
| first1 = Juergen | last1 = Stich
 
−
| first2 = Melanie | last2 = Mannhart
 
−
| first3 = Thomas | last3 = Zipperle
 
−
| first4 = Tobias | last4 = Massier
 
−
| first5 = Matthias | last5 = Huber
 
−
| first6 = Thomas | last6 = Hamacher
 
−
| title = Modelling a low-carbon power system for Indonesia, Malaysia and Singapore
 
−
| year = 2014
 
−
| conference = 33rd IEW International Energy Workshop, Peking, China
 
−
| url = https://mediatum.ub.tum.de/doc/1233948/1233948.pdf
 
−
| access-date = 2016-07-07
 
−
}}
 
−
</ref>
 
−
 
−
{{clear}}
 
−
 
−
== Open energy system models ==
 
−
 
−
Open energy system models capture some or all of the energy commodities found in an energy system.  All models include the electricity sector.  Some models add the heat sector, which can be important for countries with significant [[district heating]].  Other models add gas networks.  With the advent of [[Electric vehicle|emobility]], other models still include aspects of the transport sector.  Indeed, coupling these various sectors using [[power-to-X]] technologies is an emerging area of research.<ref name="bussar-etal-2014"/>
 
−
 
−
{| class="wikitable sortable"
 
−
|+ {{anchor|table-open-energy-system-models}} Open energy system models <span style="font-weight: normal">(bottom-up, with support for heat, gas, and such, as well as electricity)</span>
 
−
|-
 
−
! Project
 
−
! Host
 
−
! License
 
−
! Access
 
−
! Coding
 
−
! Documentation
 
−
! Scope/type
 
−
|-
 
−
| [[#Balmorel|Balmorel]]
 
−
| Denmark
 
−
| [[ISC license|ISC]]
 
−
| registration
 
−
| [[General Algebraic Modeling System|GAMS]]
 
−
| manual
 
−
| energy markets
 
−
|-
 
−
| [[#Calliope|Calliope]]
 
−
| [[ETH Zurich]]
 
−
| [[Apache License|Apache 2.0]]
 
−
| download
 
−
| [[Python (programming language)|Python]]
 
−
| manual, website, list
 
−
| dispatch and investment
 
−
|-
 
−
| [[#DESSTinEE|DESSTinEE]]
 
−
| [[Imperial College London]]
 
−
| [[Creative Commons license|{{nowrap|CC BY-SA 3.0}}]]
 
−
| download
 
−
| [[Microsoft Excel|Excel]]/[[Visual Basic for Applications|VBA]]
 
−
| website
 
−
| simulation
 
−
|-
 
−
| [[#Energy Transition Model|Energy Transition Model]]
 
−
| Quintel Intelligence
 
−
| [[MIT license|MIT]]
 
−
| [[GitHub]]
 
−
| [[Ruby (programming language)|Ruby]] (on [[Ruby on Rails|Rails]])
 
−
| website
 
−
| web-based
 
−
|-
 
−
| [[#EnergyPATHWAYS|EnergyPATHWAYS]]
 
−
| Evolved Energy Research
 
−
| [[MIT license|MIT]]
 
−
| [[GitHub]]
 
−
| [[Python (computer language)|Python]]
 
−
| website
 
−
| mostly simulation
 
−
|-
 
−
| [[#ETEM|ETEM]]
 
−
| ORDECSYS, Switzerland
 
−
| [[Eclipse Public License|Eclipse 1.0]]
 
−
| registration
 
−
| [[MathProg]]
 
−
| manual
 
−
| municipal
 
−
|-
 
−
| [[#ficus|ficus]]
 
−
| [[Technical University of Munich]]
 
−
| [[GNU General Public License|GPLv3]]
 
−
| [[GitHub]]
 
−
| [[Python (programming language)|Python]]
 
−
| manual
 
−
| local electricity and heat
 
−
|-
 
−
| [[#oemof|oemof]]
 
−
| oemof community supported by {{plainlist|
 
−
* Reiner Lemoine Institute
 
−
* [[University of Flensburg]]
 
−
* [[Fachhochschule Flensburg|Flensburg University of Applied Sciences]]
 
−
}}
 
−
| [[GNU General Public License|GPLv3]]
 
−
| [[GitHub]]
 
−
| [[Python (programming language)|Python]]
 
−
| website
 
−
| framework - dispatch, investment, all sectors, LP/MILP
 
−
|-
 
−
| [[#OSeMOSYS|OSeMOSYS]]
 
−
| OSeMOSYS community
 
−
| [[Apache License|Apache 2.0]]
 
−
| [[GitHub]]
 
−
| {{plainlist|
 
−
* [[General Algebraic Modeling System|GAMS]]
 
−
* [[MathProg]]
 
−
* [[Python (programming language)|Python]]
 
−
}}
 
−
| website, forum
 
−
| planning at all scales
 
−
|-
 
−
| [[#TEMOA|TEMOA]]
 
−
| [[North Carolina State University]]
 
−
| [[GNU General Public License|GPLv2+]]
 
−
| [[GitHub]]
 
−
| [[Python (programming language)|Python]]
 
−
| website, forum
 
−
| system planning
 
−
|- class="sortbottom"
 
−
| colspan="7" style="font-size: smaller" | {{plainlist|
 
−
* '''Access''' refers to the methods offered for accessing the codebase.
 
−
}}
 
−
|}
 
−
 
−
=== Balmorel ===
 
−
 
−
{| class="infobox" style="width: 28em"
 
−
|-
 
−
! style="width: 35%" | Project
 
−
| style="width: 55%" | Balmorel
 
−
|-
 
−
! Host
 
−
| stand-alone from Denmark
 
−
|-
 
−
! Status
 
−
| active
 
−
|-
 
−
! Scope/type
 
−
| energy markets
 
−
|-
 
−
! Code license
 
−
| [[ISC license|ISC]]
 
−
|-
 
−
! Website
 
−
| {{url|http://www.balmorel.com/}}
 
−
<!-- alternative URL: http://eabalmorel.dk/ -->
 
−
|}
 
−
 
−
Balmorel is a market-based energy system model from Denmark.  Development was originally financed by the Danish Energy Research Program in 2001.<ref name="wiese-2015"/>{{rp|23}}  The codebase was made public in March 2001.<ref>Personal email from Hans Ravn dated 11{{nbsp}}December 2016.  This makes Balmorel the first open energy modeling project to go public by quite a margin.</ref>  The Balmorel project maintains an extensive website, from where the [[codebase]] and [[Data (computing)|dataset]]s can be download as a [[Zip (file format)|zip file]].  Users are encouraged to register.  Documentation is available from the same site.<ref name="ravn-2001">
 
−
{{cite book
 
−
| first = Hans F | last = Ravn
 
−
| title = The Balmorel model: theoretical background
 
−
| date = March 2001
 
−
| publisher = Balmorel Project
 
−
| url = http://www.eabalmorel.dk/files/download/The%20Balmorel%20Mode%20Theoretical%20Background.pdf
 
−
| access-date = 2016-07-12
 
−
}}</ref><ref name="ravn-2012">
 
−
{{cite book
 
−
| first = Hans F | last = Ravn
 
−
| title = The Balmorel model structure — Version 3.02 (September 2011)
 
−
| date = 2 July 2012
 
−
| publisher = Balmorel Project
 
−
| url = http://www.eabalmorel.dk/files/download/TheBalmorelModelStructure-BMS302.pdf
 
−
| access-date = 2016-07-12
 
−
}}</ref><ref name="grohnheit-and-larsen-2001">
 
−
{{cite book
 
−
| first = Poul Erik | last1 = Grohnheit
 
−
| first2 = Helge V | last2 = Larsen
 
−
| title = Balmorel: data and calibration — Version 2.05
 
−
| date = March 2001
 
−
| publisher = Balmorel Project
 
−
| url = http://www.eabalmorel.dk/files/download/Balmorel%20Data%20and%20Calibration%20Version%202.05.pdf
 
−
| access-date = 2016-07-12
 
−
}}
 
−
</ref>  Balmorel is written in [[General Algebraic Modeling System|GAMS]].
 
−
 
−
The original aim of the Balmorel project was to construct a [[partial equilibrium]] model of the electricity and [[Cogeneration|CHP]] sectors in the [[Baltic Sea]] region, for the purposes of policy analysis.<ref name="ravn-etal-2001">
 
−
{{cite book
 
−
| first1 = Hans F | last1 = Ravn
 
−
| display-authors = etal
 
−
| title = Balmorel: a model for analyses of the electricity and CHP markets in the Baltic Sea region
 
−
| year = 2001
 
−
| publisher = Balmorel Project
 
−
| location = Denmark
 
−
| isbn = 87-986969-3-9
 
−
| url = http://www.eabalmorel.dk/files/download/Balmorel%20A%20Model%20for%20Analyses%20of%20the%20Electricity%20and%20CHP%20Markets%20in%20the%20Baltic%20Sea%20Region.pdf
 
−
| access-date = 2016-07-12
 
−
}}
 
−
</ref>  These ambitions and limitations have long since been superseded and Balmorel is no longer tied to its original geography and policy questions.<ref name="ravn-2012"/>  Balmorel classes as a dispatch and investment model and uses a time resolution of one hour.  It models electricity and heat supply and demand, and supports the intertemporal storage of both.  Balmorel is structured as a pure [[linear programming|linear program]] (no integer variables).
 
−
 
−
{{as of|2016}}, Balmorel has been the subject of some 22{{nbsp}}publications.  A 2008 study uses Balmorel to explore the Nordic energy system in 2050.  The focus is on renewable energy supply and the deployment of hydrogen as the main transport fuel.  Given certain assumptions about the future price of oil and carbon and the uptake of hydrogen, the model shows that it is economically optimal to cover, using renewable energy, more than 95% of the primary energy consumption for electricity and district heat and 65% of the transport.<ref name="karlsson-and-meibom-2008">
 
−
{{cite journal
 
−
| last1 = Karlsson | first1 = Kenneth Bernard
 
−
| last2 = Meibom | first2 = Peter
 
−
| title = Optimal investment paths for future renewable based energy systems: using the optimisation model Balmorel
 
−
| year = 2008
 
−
| journal = International Journal of Hydrogen Energy
 
−
| volume = 33
 
−
| number = 7
 
−
| pages = 1777–1787
 
−
| doi = 10.1016/j.ijhydene.2008.01.031
 
−
}}
 
−
</ref>  A 2010 study uses Balmorel to examine the integration of [[Plug-in hybrid|plug-in hybrid vehicle]]s (PHEV) into a system comprising one quarter wind power and three quarters thermal generation.  The study shows that PHEVs can reduce the {{CO2}} emissions from the power system if actively integrated, whereas a hands-off approach – letting people charge their cars at will – is likely to result in an increase in emissions.<ref name="goeransson-etal-2010">
 
−
{{cite journal
 
−
| first1 = Lisa | last1 = Göransson
 
−
| first2 = Sten | last2 = Karlsson
 
−
| first3 = Filip | last3 = Johnsson
 
−
| title = Integration of plug-in hybrid electric vehicles in a regional wind-thermal power system
 
−
| date = October 2010
 
−
| journal = Energy Policy
 
−
| volume = 38
 
−
| number = 10
 
−
| pages = 5482–5492
 
−
| doi = 10.1016/j.enpol.2010.04.001
 
−
}}
 
−
</ref>  A 2013 study uses Balmorel to examine cost-optimized wind power investments in the Nordic-Germany region.  The study investigates the best placement of wind farms, taking into account wind conditions, distance to load, and the generation and transmission infrastructure already in place.<ref name="goeransson-and-johnsson-2013">
 
−
{{cite journal
 
−
| last1 = Göransson | first1 = Lisa
 
−
| last2 = Johnsson | first2 = Filip
 
−
| date = May 2013
 
−
| title = Cost-optimized allocation of wind power investments: a Nordic-German perspective
 
−
| journal = Wind Energy
 
−
| volume = 16
 
−
| number = 4
 
−
| pages = 587–604
 
−
| doi = 10.1002/we.1517
 
−
}}
 
−
</ref>
 
−
 
−
{{clear}}
 
−
 
−
=== Calliope ===
 
−
 
−
{| class="infobox" style="width: 28em"
 
−
|-
 
−
! style="width: 35%" | Project
 
−
| style="width: 55%" | Calliope
 
−
|-
 
−
! Host
 
−
| [[ETH Zurich]]
 
−
|-
 
−
! Status
 
−
| active
 
−
|-
 
−
! Scope/type
 
−
| dispatch and investment
 
−
|-
 
−
! Code license
 
−
| [[Apache License|Apache 2.0]]
 
−
|-
 
−
! Website
 
−
| {{url|http://www.callio.pe}}
 
−
<!-- the .pe top-level domain is correct, it is actually for Peru -->
 
−
|-
 
−
! Repository
 
−
| {{url|https://github.com/calliope-project/calliope}}
 
−
|-
 
−
! Documentation
 
−
| {{url|http://docs.callio.pe/en/stable/}}
 
−
|}
 
−
 
−
Calliope is an energy system modeling framework, with a focus on flexibility, high spatial and temporal resolution, and the ability to execute different runs using the same base-case dataset.  The project is being developed at the [https://www.usys.ethz.ch/en/ Department of Environmental Systems Science], [[ETH Zurich]], [[Zürich]], Switzerland.  The project maintains a website, hosts the [[codebase]] at [[GitHub]], operates an [https://github.com/calliope-project/calliope/issues issues tracker], and runs two [[Electronic mailing list|email lists]].  Calliope is written in [[Python (programming language)|Python]] and uses the [[Pyomo]] library.  It can link to the open source [[GLPK]] solver and the commercial [[CPLEX]] and [[Gurobi]] solvers.  PDF documentation is available.<ref name="pfenninger-2016">
 
−
{{cite book
 
−
| first1 = Stefan | last1 = Pfenninger
 
−
| title = Calliope documentation — Release 0.3.7
 
−
| date = 10 March 2016
 
−
| url = https://media.readthedocs.org/pdf/calliope/stable/calliope.pdf
 
−
| access-date = 2016-07-11
 
−
}} The release version may be updated.
 
−
</ref>
 
−
 
−
A Calliope model consists of a collection of structured text files, in [[YAML]] and [[Comma-separated values|CSV]] formats, that define the technologies, locations, and resource potentials.  Calliope takes these files, constructs a pure [[Linear programming|linear optimization]] (no integer variables) problem, solves it, and reports the results in the form of [[pandas (software)|pandas]] [[data structures]] for analysis.  The framework contains five [[Abstract type|abstract]] base technologies – supply, demand, conversion, storage, transmission – from which new concrete technologies can be derived.  The design of Calliope enforces the clear separation of framework (code) and model (data).
 
−
 
−
A 2015 study uses Calliope to compare the future roles of [[nuclear power]] and [[Concentrated solar power|CSP]] in [[South Africa]].  It finds CSP could be competitive with nuclear by 2030 for baseload and more competitive when producing above baseload.  CSP also offers less investment risk, less environmental risk, and other co-benefits.<ref name="pfenninger-and-keirstead-2015-a">
 
−
{{cite journal
 
−
| first1 = Stefan | last1 = Pfenninger
 
−
| first2 = James | last2 = Keirstead
 
−
| title = Comparing concentrating solar and nuclear power as baseload providers using the example of South Africa
 
−
| year = 2015
 
−
| journal = Energy
 
−
| volume = 87
 
−
| pages = 303–314
 
−
| doi = 10.1016/j.energy.2015.04.077
 
−
}}
 
−
</ref>  A second 2015 study compares a large number of cost-optimal future power systems for [[Great Britain]].  Three generation technologies are tested: renewables, nuclear power, and fossil fuels with and without [[carbon capture and storage]] (CCS).  The scenarios are assessed on financial cost, emissions reductions, and energy security.  Up to 60% of [[Variable renewable energy|variable renewable]] capacity is possible with little increase in cost, while higher shares require large-scale [[Grid energy storage|storage]], imports, and/or [[Dispatchable generation|dispatchable]] renewables such as [[Tidal power|tidal range]].<ref name="pfenninger-and-keirstead-2015-b">
 
−
{{cite journal
 
−
| first1 = Stefan | last1 = Pfenninger
 
−
| first2 = James | last2 = Keirstead
 
−
| title = Renewables, nuclear, or fossil fuels? Scenarios for Great Britain's power system considering costs, emissions and energy security
 
−
| year = 2015
 
−
| journal = Applied Energy
 
−
| volume = 152
 
−
| pages = 83–93
 
−
| doi = 10.1016/j.apenergy.2015.04.102
 
−
| url = http://www.sciencedirect.com/science/article/pii/S0306261915005656/pdfft?md5=c2e6e2b14ecc752dd3cb455859a49c42&pid=1-s2.0-S0306261915005656-main.pdf
 
−
| access-date = 2016-07-07
 
−
}}
 
−
</ref>
 
−
 
−
{{clear}}
 
−
 
−
=== DESSTinEE ===
 
−
 
−
{| class="infobox" style="width: 28em"
 
−
|-
 
−
! style="width: 35%" | Project
 
−
| style="width: 55%" | DESSTinEE
 
−
|-
 
−
! Host
 
−
| [[Imperial College London]]
 
−
|-
 
−
! Status
 
−
| active
 
−
|-
 
−
! Scope/type
 
−
| simulation
 
−
|-
 
−
! Code license
 
−
| [[Creative Commons license|{{nowrap|CC BY-SA 3.0}}]]
 
−
|-
 
−
! Website
 
−
| {{url|https://sites.google.com/site/2050desstinee/}}
 
−
|}
 
−
 
−
DESSTinEE stands for Demand for Energy Services, Supply and Transmission in EuropE.  DESSTinEE is a model of the European energy system in 2050 with a focus on the electricity system.  DESSTinEE is being developed primarily at the [[Imperial College Business School]], [[Imperial College London]] (ICL), [[London]], United Kingdom.  The software can be downloaded from the project website.  DESSTinEE is written in [[Microsoft Excel|Excel]]/[[Visual Basic for Applications|VBA]] and comprises a set of standalone [[spreadsheet]]s.  A flier is available.<ref name="desstinee-2015">
 
−
{{cite book
 
−
| title = DESSTinEE: an energy transfer reference case
 
−
| year = 2015
 
−
| url = http://www.topandtail.org.uk/publications/outcomes/Planning_a_Transcontinental_Interconnected_System/An%20Energy%20Transfer%20Reference%20Case.pdf
 
−
| access-date = 2016-07-11
 
−
}}
 
−
</ref>
 
−
 
−
DESSTinEE is designed to investigate assumptions about the technical requirements for energy transport – particularly electricity – and the scale of the economic challenge to develop the necessary infrastructure.  Forty countries are considered in and around Europe and ten forms of primary and secondary energy are supported.  The model uses a predictive simulation technique, rather than solving for either [[Partial equilibrium|partial]] or [[Computable general equilibrium|general equilibrium]].  The model projects annual energy demands for each country to 2050, synthesizes hourly profiles for electricity demand in 2010 and 2050, and simulates the least-cost generation and transmission of electricity around the region.<ref name="openmod-wiki-desstinee">
 
−
{{cite web
 
−
| title = DESSTinEE
 
−
| publisher = Open Energy Modelling Initiative
 
−
| url = http://wiki.openmod-initiative.org/wiki/DESSTinEE
 
−
| access-date = 2016-12-03
 
−
}} [[File:CC-BY icon.svg|50px]] Material was copied from this source, which is available under a [https://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International {{nowrap|(CC BY 4.0)}} license].
 
−
</ref>
 
−
 
−
A 2016 study using DESSTinEE (and a second model eLOAD) examines the evolution of electricity load curves in Germany and Britain from the present until 2050.  In 2050, peak loads and ramp rates rise 20–60% and system utilization falls 15–20%, in part due to the substantial uptake of [[heat pump]]s and [[electric vehicle]]s.  These are significant changes.<ref name="bossmann-and-staffell-2016">
 
−
{{cite journal
 
−
| last1 = Boßmann | first1 = Tobias
 
−
| last2 = Staffell | first2 = Iain
 
−
| title = The shape of future electricity demand: exploring load curves in 2050s Germany and Britain
 
−
| year = 2016
 
−
| journal = Energy
 
−
| volume = 90
 
−
| number = 20
 
−
| pages = 1317–1333
 
−
| doi = 10.1016/j.energy.2015.06.082
 
−
}}
 
−
</ref>
 
−
 
−
{{clear}}
 
−
 
−
=== Energy Transition Model ===
 
−
 
−
{| class="infobox" style="width: 28em"
 
−
|-
 
−
! style="width: 35%" | Project
 
−
| style="width: 55%" | Energy Transition Model
 
−
|-
 
−
! Host
 
−
| [http://quintel.com/ Quintel Intelligence]
 
−
|-
 
−
! Status
 
−
| active
 
−
|-
 
−
! Scope/type
 
−
| web-based
 
−
|-
 
−
! Code license
 
−
| [[MIT license|MIT]]
 
−
|-
 
−
! Website
 
−
| {{url|https://energytransitionmodel.com}}
 
−
|-
 
−
! Interactive website
 
−
| {{url|https://pro.energytransitionmodel.com}}
 
−
|-
 
−
! Repository
 
−
| {{url|https://github.com/quintel/documentation}}
 
−
|}
 
−
 
−
The Energy Transition Model (ETM) is an interactive web-based model using a holistic description of a country's energy system.  It is being developed by Quintel Intelligence, [[Amsterdam]], the Netherlands.  The project maintains a project website, an interactive website, and a [[GitHub]] repository.  ETM is written in [[Ruby (programming language)|Ruby]] (on [[Ruby on Rails|Rails]]) and displays in a [[web browser]].  ETM consists of several software components as described in the documentation.
 
−
 
−
ETM is fully interactive.  After selecting a region (France, Germany, the Netherlands, Poland, Spain, United Kingdom, EU-27, or Brazil) and a year (2020, 2030, 2040, or 2050), the user can set 300 sliders (or enter numerical values) to explore the following:
 
−
 
−
* targets: set goals for the scenario and see if they can be achieved, targets comprise: {{CO2}} reductions, renewables shares, total cost, and caps on imports
 
−
* demands: expand or restrict energy demand in the future
 
−
* costs: project the future costs of energy carriers and energy technologies, these costs do not include taxes or subsidies
 
−
* supplies: select which technologies can be used to produce heat or electricity
 
−
 
−
ETM is based on an energy graph ([[Directed graph|digraph]]) where nodes ([[Glossary of graph theory#vertex|vertices]]) can convert from one type of energy to another, possibly with losses.  The connections ([[Glossary of graph theory#edge|directed edges]]) are the energy flows and are characterized by volume (in [[megajoule]]s) and carrier type (such as coal, electricity, usable-heat, and so forth).  Given a demand and other choices, ETM calculates the primary energy use, the total cost, and the resulting {{CO2}} emissions.  The model is demand driven, meaning that the digraph is traversed from ''useful demand'' (such as space heating, hot water usage, and car-kilometers) to ''primary demand'' (the extraction of gas, the import of coal, and so forth).
 
−
 
−
{{clear}}
 
−
 
−
=== EnergyPATHWAYS ===
 
−
 
−
{| class="infobox" style="width: 28em"
 
−
|-
 
−
! style="width: 35%" | Project
 
−
| style="width: 55%" | EnergyPATHWAYS
 
−
|-
 
−
! Host
 
−
| [http://www.evolved.energy/ Evolved Energy Research]
 
−
|-
 
−
! Status
 
−
| active
 
−
|-
 
−
! Scope/type
 
−
| mostly simulation
 
−
|-
 
−
! Code license
 
−
| [[MIT license|MIT]]
 
−
|-
 
−
! Repository
 
−
| {{url|https://github.com/energyPATHWAYS/energyPATHWAYS}}
 
−
|}
 
−
 
−
EnergyPATHWAYS is a bottom-up energy sector model used to explore the near-term implications of long-term deep decarbonization.  The lead developer is energy and climate protection consultancy, Evolved Energy Research, [[San Francisco]], USA.  The code is hosted on [[GitHub]].  EnergyPATHWAYS is written in [[Python (computer language)|Python]] and links to the open source [[COIN-OR#CBC|Cbc]] solver.  Alternatively, the [[GLPK]], [[CPLEX]], or [[Gurobi]] solvers can be employed.  EnergyPATHWAYS utilizes the [[PostgreSQL]] [[object-relational database management system]] (ORDBMS) to manage its [[Data (computing)|data]].
 
−
 
−
EnergyPATHWAYS is a comprehensive accounting framework used to construct economy-wide energy infrastructure scenarios.  While portions of the model do use [[linear programming]] techniques, for instance, for electricity dispatch, the EnergyPATHWAYS model is not fundamentally an optimization model and embeds few decision dynamics.  EnergyPATHWAYS offers detailed energy, cost, and emissions accounting for the energy flows from primary supply to final demand.  The energy system representation is flexible, allowing for differing levels of detail and the nesting of cities, states, and countries.  The model uses hourly least-cost electricity dispatch and supports [[power-to-gas]], short-duration energy storage, long-duration energy storage, and [[demand response]].  Scenarios typically run to 2050.
 
−
 
−
A predecessor of the EnergyPATHWAYS software, named simply PATHWAYS, has been used to construct policy models.  The California PATHWAYS model was used to inform Californian state climate targets for 2030.<ref name="williams-etal-2012">
 
−
{{cite journal
 
−
| last1 = Williams | first1 = James H
 
−
| last2 = DeBenedictis | first2 = Andrew
 
−
| last3 = Ghanadan | first3 = Rebecca
 
−
| last4 = Mahone | first4 = Amber
 
−
| last5 = Moore | first5 = Jack
 
−
| last6 = Morrow | first6 = William R
 
−
| last7 = Price | first7 = Snuller
 
−
| last8 = Torn | first8 = Margaret S
 
−
| title = The technology path to deep greenhouse gas emissions cuts by 2050: the pivotal role of electricity
 
−
| year = 2012
 
−
| journal = Science
 
−
| volume = 335
 
−
| number = 6064
 
−
| pages = 53–59
 
−
| doi = 10.1126/science.1208365
 
−
}} See also published [http://science.sciencemag.org/content/336/6079/296.2 correction].
 
−
</ref>  And the US PATHWAYS model contributed to the [[United Nations|UN]] [[Deep Decarbonization Pathways Project]] (DDPP) assessments for the United States.<ref name="us-ddpp">
 
−
{{cite web
 
−
| title = The US Deep Decarbonization Pathways Project (USDDPP)
 
−
| publisher = Deep Decarbonization Pathways Project (DDPP)
 
−
| location = New York, NY, USA
 
−
| url = http://usddpp.org
 
−
| access-date = 2016-12-06
 
−
}}
 
−
</ref>  {{as of|2016}}, the DDPP plans to employ EnergyPATHWAYS for future analysis.
 
−
 
−
{{clear}}
 
−
 
−
=== ETEM ===
 
−
 
−
{| class="infobox" style="width: 28em"
 
−
|-
 
−
! style="width: 35%" | Project
 
−
| style="width: 55%" | ETEM
 
−
|-
 
−
! Host
 
−
| [http://www.ordecsys.com/en/home ORDECSYS]
 
−
|-
 
−
! Status
 
−
| active
 
−
|-
 
−
! Scope/type
 
−
| municipal
 
−
|-
 
−
! Code license
 
−
| [[Eclipse Public License|Eclipse 1.0]]
 
−
|-
 
−
! Website
 
−
| {{plainlist|
 
−
* {{url|http://apps.ordecsys.com/etem}}
 
−
* {{url|http://www.energyplan.eu/othertools/local/etem/}}
 
−
}}
 
−
|}
 
−
 
−
ETEM stands for Energy Technology Environment Model.  The ETEM model offers a similar structure to [[#OSeMOSYS|OSeMOSYS]] but is aimed at urban planning.  The software is being developed by the ORDECSYS company, [[Chêne-Bougeries]], Switzerland, supported with European Union and national research grants.  The project has two websites.  The software can be downloaded from first of these websites (but {{as of|lc=yes|2016|07}}, this looks out of date).  A manual is available with the software.<ref name="drouet-and-thenie-2009">
 
−
{{cite book
 
−
| last1 = Drouet | first1 = Laurent
 
−
| last2 = Thénié | first2 = Julie
 
−
| title = ETEM: an energy–technology–environment model to assess urban sustainable development policies — Reference manual version 2.1
 
−
| year = 2009
 
−
| publisher = ORDECSYS (Operations Research Decisions and Systems)
 
−
| location = Chêne-Bougeries, Switzerland
 
−
}} This PDF is part of the software bundle.
 
−
</ref>  ETEM is written in [[MathProg]].{{efn|
 
−
Note that GMPL, referred to in the documentation, is an alternative name for [[MathProg]].
 
−
}}  Presentations describing ETEM are available.<ref name="drouet-and-zachary-2010">
 
−
{{cite book
 
−
| last1 = Drouet | first1 = Laurent
 
−
| last2 = Zachary | first2 = D
 
−
| title = Economic aspects of the ETEM model — Presentation
 
−
| date = 21 May 2010
 
−
| publisher = Resource Centre for Environmental Technologies, Public Research Centre Henri Tudor
 
−
| location = Esch-sur-Alzette, Luxembourg
 
−
| url = http://crteweb.tudor.lu/leaq/uploads/etem-economy.pdf
 
−
| access-date = 2016-07-12
 
−
}}</ref><ref name="ordecsys-2015">
 
−
{{cite book
 
−
| title = Spatial simulation and optimization with ETEM-SG: Energy–Technology–Environment-Model for smart cities — Presentation
 
−
| date = 2015
 
−
| publisher = ORDECSYS
 
−
| location = Chêne-Bougeries, Switzerland
 
−
| url = http://www.ordecsys.com/fr/system/files/u1/Ordecsys-ETEM-SG.pdf
 
−
| access-date = 2016-07-12
 
−
}}
 
−
</ref>
 
−
 
−
ETEM is a bottom-up model that identifies the optimal energy and technology options for a regional or city.  The model finds an energy policy with minimal cost, while investing in new equipment (new technologies), developing production capacity (installed technologies), and/or proposing the feasible import/export of primary energy.  ETEM typically casts forward 50{{nbsp}}years, in two or five year steps, with time slices of four seasons using typically individual days or finer.  The spatial resolution can be highly detailed.  Electricity and heat are both supported, as are [[district heating]] networks, household energy systems, and grid storage, including the use of [[plug-in hybrid|plug-in hybrid electric vehicles]] (PHEV).  ETEM-SG, a development, supports [[demand response]], an option which would be enabled by the development of [[smart grid]]s.
 
−
 
−
The ETEM model has been applied to Luxembourg, the Geneva and Basel-Bern-Zurich cantons in Switzerland, and the Grenoble metropolitan and Midi-Pyrénées region in France.  A 2005 study uses ETEM to study climate protection in the Swiss housing sector.  The ETEM model was coupled with the GEMINI-E3 world [[Computable general equilibrium|computable general equilibrium model]] (CGEM) to complete the analysis.<ref name="drouet-etal-2005">
 
−
{{cite book
 
−
| last1 = Drouet | first1 = Laurent
 
−
| last2 = Haurie | first2 = Alain
 
−
| last3 = Labriet | first3 = Maryse
 
−
| last4 = Thalmann | first4 = Philippe
 
−
| last5 = Vielle | first5 = Marc
 
−
| last6 = Viguier | first6 = Laurent
 
−
| title = A coupled bottom-up/top-down model for GHG abatement scenarios in the Swiss housing sector
 
−
| year = 2005
 
−
| doi = 10.1007/0-387-25352-1_2
 
−
| url = http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.111.8420&rep=rep1&type=pdf
 
−
| access-date = 2016-06-17
 
−
}}
 
−
</ref>  A 2012 study examines the design of [[smart grid]]s.  As distribution systems become more intelligent, so must the models needed to analysis them.  ETEM is used to assess the potential of smart grid technologies using a [[case study]], roughly calibrated on the [[Geneva]] canton, under three scenarios.  These scenarios apply different constraints on {{CO2}} emissions and electricity imports.  A stochastic approach is used to deal with the uncertainty in future electricity prices and the uptake of electric vehicles.<ref name="babonneau-2012">
 
−
{{cite journal
 
−
| first1 = Frédéric | last1 = Babonneau
 
−
| first2 = Alain | last2 = Haurie
 
−
| first3 = Guillaume Jean | last3 = Tarel
 
−
| first4 = Julien | last4 = Thénié
 
−
| title = Assessing the future of renewable and smart grid technologies in regional energy systems
 
−
| date = June 2012
 
−
| journal = Swiss Journal of Economics and Statistics (SJES)
 
−
| volume = 148
 
−
| number = 2
 
−
| pages = 229–273
 
−
| doi =
 
−
| url = http://www.sjes.ch/papers/2012-II-6.pdf
 
−
| access-date = 2016-07-12
 
−
}}
 
−
</ref>
 
−
 
−
{{clear}}
 
−
 
−
=== ficus ===
 
−
 
−
{| class="infobox" style="width: 28em"
 
−
|-
 
−
! style="width: 35%" | Project
 
−
| style="width: 55%" | ficus
 
−
|-
 
−
! Host
 
−
| [[Technical University of Munich]]
 
−
|-
 
−
! Status
 
−
| active
 
−
|-
 
−
! Scope/type
 
−
| local electricity and heat
 
−
|-
 
−
! Code license
 
−
| [[GNU General Public License|GPLv3]]
 
−
|-
 
−
! Repository
 
−
| {{url|https://github.com/yabata/ficus}}
 
−
|-
 
−
! Documentation
 
−
| {{url|https://ficus.readthedocs.io/en/latest/}}
 
−
|}
 
−
 
−
ficus is a [[Mixed integer program|mixed integer]] optimization model for local energy systems.  It is being developed at the [https://www.ewk.ei.tum.de/en/startseite/ Institute for Energy Economy and Application Technology], [[Technical University of Munich]], [[Munich]], Germany.  The project maintains a website.  The project is hosted on [[GitHub]].  ficus is written in [[Python (programming language)|Python]] and uses the [[Pyomo]] library.  The user can choose between the open source [[GLPK]] solver or the commercial [[CPLEX]] and [[Gurobi]] solvers.
 
−
 
−
Based on [[#URBS|URBS]], ficus was originally developed for optimizing the energy systems of factories and has now been extended to include local energy systems.  ficus supports multiple energy commodities – goods that can be imported or exported, generated, stored, or consumed – including electricity and heat.  It supports multiple-input and multiple-output energy conversion technologies with load-dependent efficiencies.  The objective of the model is to supply the given demand at minimal cost.  ficus uses exogenous cost time series for imported commodities as well as peak demand charges with a configurable timebase for each commodity in use.
 
−
 
−
{{clear}}
 
−
 
−
=== oemof ===
 
−
 
−
{| class="infobox" style="width: 28em"
 
−
|-
 
−
! style="width: 35%" | Project
 
−
| style="width: 55%" | oemof
 
−
|-
 
−
! Host
 
−
| oemof community supported by {{plainlist|
 
−
* [http://reiner-lemoine-institut.de/en/ Reiner Lemoine Institute]
 
−
* [[University of Flensburg]]
 
−
* [[Fachhochschule Flensburg|Flensburg University of Applied Sciences]]
 
−
}}
 
−
|-
 
−
! Status
 
−
| active
 
−
|-
 
−
! Scope/type
 
−
| electricity, heat, mobility, gas
 
−
|-
 
−
! Code license
 
−
| [[GNU General Public License|GPLv3]]
 
−
|-
 
−
! Website
 
−
| {{plainlist|
 
−
* {{url|https://oemof.org}}
 
−
<!-- * {{url|http://reiner-lemoine-institut.de/en/oemof/}} -->
 
−
}}
 
−
|-
 
−
! Repository
 
−
| {{url|https://github.com/oemof/}}
 
−
|-
 
−
! Documentation
 
−
| {{url|http://oemof.readthedocs.io}}
 
−
|}
 
−
 
−
oemof stands for Open Energy Modelling Framework.  The project is managed by the Reiner Lemoine Institute, [[Berlin]], Germany and the [http://www.znes-flensburg.de/ Center for Sustainable Energy Systems] (CSES or ZNES) at the [[University of Flensburg]] and the [[Fachhochschule Flensburg|Flensburg University of Applied Sciences]], both [[Flensburg]], Germany.  The project runs two websites and a [[GitHub]] repository.  oemof is written in [[Python (computer language)|Python]] and uses [[Pyomo]] and [[COIN-OR]] components for optimization.  Energy systems can be represented using spreadsheets ([[Comma-separated values|CSV]]) which should simplify data preparation.  {{nowrap|Version 0.1.0}} was released on 1{{nbsp}}December 2016.
 
−
 
−
oemof classes as an energy modeling framework.  It consists of a [[Linear programming|linear]] or [[Mixed integer program|mixed integer]] optimization problem formulation library (solph), an input data generation library (feedin-data), and other auxiliary libraries.  The solph library is used to represent multi-regional and multi-sectoral (electricity, heat, gas, mobility) systems and can optimize for different targets, such as financial cost or {{CO2}} emissions.  Furthermore, it is possible to switch between dispatch and investment modes.  In terms of scope, oemof can capture the European power system or alternatively it can describe a complex local power and heat sector scheme.
 
−
 
−
{{clear}}
 
−
 
−
=== OSeMOSYS ===
 
−
 
−
{| class="infobox" style="width: 28em"
 
−
|-
 
−
! style="width: 35%" | Project
 
−
| style="width: 55%" | OSeMOSYS
 
−
|-
 
−
! Host
 
−
| community project
 
−
|-
 
−
! Status
 
−
| active
 
−
|-
 
−
! Scope/type
 
−
| planning at all scales
 
−
|-
 
−
! Code license
 
−
| [[Apache License|Apache 2.0]]
 
−
|-
 
−
! Website
 
−
| {{url|http://www.osemosys.org}}
 
−
|-
 
−
! Repository
 
−
| {{url|https://github.com/KTH-dESA/OSeMOSYS}}
 
−
|}
 
−
 
−
OSeMOSYS stands for Open Source Energy Modelling System.  OSeMOSYS is intended for national and regional policy development and uses an intertemperal optimization framework.  The model posits a single socially motivated operator/investor with perfect foresight.  The OSeMOSYS project is a community endeavor, supported by the Energy Systems Analysis Group (dESA), [[Royal Institute of Technology|KTH Royal Institute of Technology]], [[Stockholm]], Sweden.  The project maintains a website providing background. The project also offers several active [[internet forum]]s on [[Reddit]].  OSeMOSYS was originally written in [[MathProg]], a high-level [[Mathematical optimization|mathematical programming]] language.  It was subsequently reimplemented in [[General Algebraic Modeling System|GAMS]] and [[Python (programming language)|Python]] and all three codebases are now maintained.  The project also provides a test model called UTOPIA.<ref name="lavigne-2017"/>  A manual is available.<ref name="moksnes-etal-2015">
 
−
{{cite book
 
−
| first1 = Nandi | last1 = Moksnes
 
−
| first2 = Manuel | last2 = Welsch
 
−
| first3 = Francesco | last3 = Gardumi
 
−
| first4 = Abhishek | last4 = Shivakumar
 
−
| first5 = Oliver | last5 = Broad
 
−
| first6 = Mark | last6 = Howells
 
−
| first7 = Constantinos | last7 = Taliotis
 
−
| first8 = Vignesh | last8 = Sridharan
 
−
| title = 2015 OSeMOSYS user manual — Working Paper Series DESA/15/11
 
−
| date = November 2015
 
−
| publisher = Division of Energy Systems Analysis, KTH Royal Institute of Technology
 
−
| location = Stockholm, Sweden
 
−
| url = http://www.osemosys.org/uploads/1/8/5/0/18504136/osemosys_manual_-_working_with_text_files_-_2015-11-05.pdf
 
−
| access-date = 2016-07-12
 
−
}} The version referred to in the manual is OSeMOSYS_2013_05_10.
 
−
</ref>
 
−
 
−
OSeMOSYS provides a framework for the analysis of energy systems over the medium (10–15 years) and long term (50–100 years).  OSeMOSYS uses pure [[Linear programming|linear optimization]], with the option of [[mixed integer programming]] for the treatment of, for instance, discrete power plant capacity expansions.  It covers most energy sectors, including heat, electricity, and transport.  OSeMOSYS is driven by exogenously defined [[Energy system#Energy-services|energy services]] demands.  These are then met through a set of technologies which draw on a set of resources, both characterized by their potentials and costs.  These resources are not limited to energy commodities and may include, for example, water and [[Land use, land-use change and forestry|land-use]].  This enables OSeMOSYS to be applied in domains other than energy, such as water systems.  Technical constraints, economic restrictions, and/or environmental targets may also be imposed to reflect policy considerations.  OSeMOSYS is available in extended and compact MathProg formulations, either of which should give identical results.  In its extended version, OSeMOSYS comprises a little more than 400 [[Source lines of code|lines of code]].
 
−
 
−
[[File:Osemosys energy model results for fictitious atlantis.png|thumb|left|Simplified results for a fictitious country called Atlantis used for training purposes]]
 
−
 
−
A key paper describing OSeMOSYS is available.<ref name="howells-etal-2011">
 
−
{{cite journal
 
−
| last1 = Howells | first1 = Mark
 
−
| last2 = Rogner | first2 = Holger
 
−
| last3 = Strachan | first3 = Neil
 
−
| last4 = Heaps | first4 = Charles
 
−
| last5 = Huntington | first5 = Hillard
 
−
| last6 = Kypreos | first6 = Socrates
 
−
| last7 = Hughes | first7 = Alison
 
−
| last8 = Silveira | first8 = Semida
 
−
| last9 = DeCarolis | first9 = Joe
 
−
| last10 = Bazilian | first10 = Morgan
 
−
| last11 = Roehrl | first11 = Alexander
 
−
| title = OSeMOSYS: the open source energy modeling system : an introduction to its ethos, structure and development
 
−
| year = 2011
 
−
| journal = Energy Policy
 
−
| volume = 39
 
−
| issue = 10
 
−
| pages = 5850–5870
 
−
| doi = 10.1016/j.enpol.2011.06.033
 
−
}} The name Morgan Bazillian has been corrected.  ResearchGate [https://www.researchgate.net/publication/229284137_OSeMOSYS_The_Open_Source_Energy_Modeling_System_An_introduction_to_its_ethos_structure_and_development version].
 
−
</ref>  A 2011 study uses OSeMOSYS to investigate the role of household investment decisions.<ref name="warren-2011">
 
−
{{cite conference
 
−
| last = Warren | first = Peter
 
−
| title = Incorporating behavioural complexity into the Open Source Energy Modelling System using intangible costs and benefits
 
−
| date = 23 September 2011
 
−
| conference = People and Buildings
 
−
| place = London, UK
 
−
| url = https://www.researchgate.net/publication/268352013_Incorporating_behavioural_complexity_into_the_Open_Source_Energy_Modelling_System_using_intangible_costs_and_benefits
 
−
| access-date = 2016-06-17
 
−
}}
 
−
</ref>  A 2012 study extends OSeMOSYS to capture the salient features of a [[smart grid]].  The paper explains how to model variability in generation, flexible demand, and [[Grid energy storage|grid storage]] and how these impact on the stability of the grid.<ref name="welsch-etal-2012">
 
−
{{cite journal
 
−
| first1 = Manuel | last1 = Welsch
 
−
| first2 = Mark | last2 = Howells
 
−
| first3 = Morgan | last3 = Bazilian
 
−
| first4 = Joseph F | last4 = DeCarolis
 
−
| first5 = Sebastian | last5 = Hermann
 
−
| first6 = Holger H | last6 = Rogner
 
−
| title = Modelling elements of smart grids: enhancing the OSeMOSYS (Open Source Energy Modelling System) code
 
−
| year = 2012
 
−
| journal = Energy
 
−
| volume = 46
 
−
| number = 1
 
−
| pages = 337–350
 
−
| doi = 10.1016/j.energy.2012.08.017
 
−
}}
 
−
</ref>  OSeMOSYS has been applied to village systems.  A 2015 paper compares the merits of stand-alone, mini-grid, and grid electrification for rural areas in [[East Timor|Timor-Leste]] under differing levels of access.<ref name="fuso-nerini-etal-2015">
 
−
{{cite journal
 
−
| last1 = Fuso Nerini | first1 = Francesco
 
−
| last2 = Dargaville | first2 = Roger
 
−
| last3 = Howells | first3 = Mark
 
−
| last4 = Bazilian | first4 = Morgan
 
−
| date = 1 January 2015
 
−
| title = Estimating the cost of energy access: the case of the village of Suro Craic in Timor Leste
 
−
| journal = Energy
 
−
| volume = 79
 
−
| pages = 385–397
 
−
| doi = 10.1016/j.energy.2014.11.025
 
−
| issn = 0360-5442
 
−
}}
 
−
</ref>  In a 2016 study, OSeMOSYS is modified to take into account realistic consumer behavior.<ref name="fragniere-etal-2016">
 
−
{{cite journal
 
−
| last1 =Fragnière | first1 = Emmanuel
 
−
| last2 = Kanala | first2 = Roman
 
−
| last3 = Moresino | first3 = Francesco
 
−
| last4 = Reveiu | first4 = Adriana
 
−
| last5 = Smeureanu | first5 = Ion
 
−
| title = Coupling techno-economic energy models with behavioral approaches
 
−
| year = 2016
 
−
| journal = Operational Research
 
−
| volume = <!-- not stated on journal website -->
 
−
| pages = 1–15
 
−
| doi = 10.1007/s12351-016-0246-9
 
−
}}
 
−
</ref>  Another 2016 study uses OSeMOSYS to build a local multi-regional energy system model of the [[Lombardy]] region in Italy.  One of the aims of the exercise was to encourage citizens to participate in the energy planning process.  Preliminary results indicate that this was successful and that open modeling is needed to properly include both the technological dynamics and the non-technological issues.<ref name="fattori-etal-2016">
 
−
{{cite journal
 
−
| first1 = Fabrizio | last1 = Fattori
 
−
| first2 = Davide | last2 = Albini
 
−
| first3 = Norma | last3 = Anglani
 
−
| title = Proposing an open-source model for unconventional participation to energy planning
 
−
| year = 2016
 
−
| journal = Energy Research and Social Science
 
−
| volume = 15
 
−
| pages = 12–33
 
−
| doi = 10.1016/j.erss.2016.02.005
 
−
}}
 
−
</ref>  A 2017 paper covering [[Alberta]], Canada factors in the risk of overrunning specified emissions targets because of technological uncertainty.  Among other results, the paper finds that solar and wind technologies are built out seven and five years earlier respectively when emissions risks are included.<ref name="niet-etal-2017">
 
−
{{cite journal
 
−
| last1 = Niet | first1 = T
 
−
| last2 = Lyseng | first2 = B
 
−
| last3 = English | first3 = J
 
−
| last4 = Keller | first4 = V
 
−
| last5 = Palmer-Wilson | first5 = K
 
−
| last6 = Moazzen | first6 = I
 
−
| last7 = Robertson | first7 = B
 
−
| last8 = Wild | first8 = P
 
−
| last9 = Rowe | first9 = A
 
−
| date = June 2017 <!-- the publisher notes: this issue is in progress but contains articles that are final and fully citable / so a forward date is okay -->
 
−
| title = Hedging the risk of increased emissions in long term energy planning
 
−
| journal = Energy Strategy Reviews
 
−
| volume = 16
 
−
| pages = 1–12
 
−
| doi = 10.1016/j.esr.2017.02.001
 
−
| issn = 2211-467X
 
−
}}
 
−
</ref>  Another 2017 paper analyses the electricity system in [[Cyprus]] and finds that, after European Union environmental regulations are applied post-2020, a switch from oil-fired to natural gas generation is indicated.<ref name="taliotis-etal-2017">
 
−
{{cite journal
 
−
| last1 = Taliotis | first1 = Constantinos
 
−
| last2 = Rogner | first2 = Holger
 
−
| last3 = Ressl | first3 = Stephan
 
−
| last4 = Howells | first4 = Mark
 
−
| last5 = Gardumi | first5 = Francesco
 
−
| title = Natural gas in Cyprus: the need for consolidated planning
 
−
| date = August 2017
 
−
| journal = Energy Policy
 
−
| volume = 107
 
−
| pages = 197–209
 
−
| doi = 10.1016/j.enpol.2017.04.047
 
−
| issn = 0301-4215
 
−
| url = http://www.sciencedirect.com/science/article/pii/S0301421517302720
 
−
| access-date = 2017-05-04
 
−
}}
 
−
</ref>
 
−
 
−
OSeMOSYS has been used to construct wide-area electricity models for [[Africa]], comprising 45{{nbsp}}countries<ref name="taliotis-etal-2016">
 
−
{{cite journal
 
−
| last1 = Taliotis | first1 = Constantinos
 
−
| last2 = Shivakumar | first2 = Abhishek
 
−
| last3 = Ramos | first3 = Eunice
 
−
| last4 = Howells | first4 = Mark
 
−
| last5 = Mentis | first5 = Dimitris
 
−
| last6 = Sridharan | first6 = Vignesh
 
−
| last7 = Broad | first7 = Oliver
 
−
| last8 = Mofor | first8 = Linus
 
−
| date = April 2016
 
−
| title = An indicative analysis of investment opportunities in the African electricity supply sector — Using TEMBA (The Electricity Model Base for Africa)
 
−
| journal = Energy for Sustainable Development
 
−
| volume = 31
 
−
| pages = 50–66
 
−
| doi = 10.1016/j.esd.2015.12.001
 
−
| issn = 0973-0826
 
−
}}
 
−
</ref><ref name="osemosys-website-temba">
 
−
{{cite web
 
−
| title = The Electricity Model Base for Africa (TEMBA)
 
−
| work = OSeMOSYS
 
−
| url = http://www.osemosys.org/temba.html
 
−
| access-date = 2017-01-13
 
−
}}
 
−
</ref> and [[South America]], comprising 13{{nbsp}}countries.<ref name="moura-and-howells-2015">
 
−
{{cite book
 
−
| first1 = Gustavo | last1 = Moura
 
−
| first2 = Mark | last2 = Howells
 
−
| title = SAMBA: the open source South American model base: a Brazilian perspective on long term power systems investment and integration — Working paper dESA /5/8/11
 
−
| date = August 2015
 
−
| publisher = Royal Institute of Technology (KTH)
 
−
| location = Sockholm, Sweden
 
−
| doi = 10.13140/RG.2.1.3038.7042
 
−
}} Available for download from [[ResearchGate]].
 
−
</ref><ref name="osemosys-website-samba">
 
−
{{cite web
 
−
| title = South American Model Base (SAMBA)
 
−
| work = OSeMOSYS
 
−
| url = http://www.osemosys.org/samba-south-american-model-base.html
 
−
| access-date = 2017-01-13
 
−
}}
 
−
</ref>  It has also been used to support United Nations' regional climate, land, energy, and water strategies (CLEWS)<ref name="global-clews-website">
 
−
{{cite web
 
−
| title = Global CLEWS (Climate, Land, Energy, and Water Strategies)
 
−
| publisher = Division for Sustainable Development, Department of Economic and Social Affairs (DESA), United Nations
 
−
| location = New York, USA
 
−
| url = https://unite.un.org/sites/unite.un.org/files/app-globalclews-v-1-0/landingpage.html
 
−
| access-date = 2017-01-13
 
−
}}
 
−
</ref> for the [[Sava]] river basin, central Europe,<ref name="de-strasser-etal-2016">
 
−
<!-- UN document reference: ECE/MP.WAT/NONE/3 -->
 
−
{{cite book
 
−
| first1 = Lucia | last1 = de Strasser
 
−
| first2 = Dimitris | last2 = Mentis
 
−
| first3 = Eunice | last3 = Ramos
 
−
| first4 = Vignesh | last4 = Sridharan
 
−
| first5 = Manuel | last5 = Welsch
 
−
| first6 = Mark | last6 = Howells
 
−
| first7 = Gia | last7 = Destouni
 
−
| first8 = Lea | last8 = Levi
 
−
| first9 = Stephen | last9 = Stec
 
−
| first10 = Ad de | last10 = Roo
 
−
| date = 2016
 
−
| title = Reconciling resource uses in transboundary basins: assessment of the water-food-energy-ecosystems nexus in the Sava River Basin
 
−
| publisher = United Nations Economic Commission for Europe (UNECE)
 
−
| location = Geneva, Switzerland
 
−
| url = https://www.unece.org/fileadmin/DAM/env/water/publications/GUIDELINES/2017/nexus_in_Sava_River_Basin/Nexus-SavaRiverBasin_ECE-MP.WAT-NONE-3_WEB_final_corrected_for_gDoc.pdf
 
−
| access-date = 2017-03-17
 
−
}}
 
−
</ref> the [[Syr Darya]] river basin, eastern Europe,<ref name="unece-2016">
 
−
{{cite book
 
−
| author = <!-- not specified -->
 
−
| title = Reconciling resource uses in transboundary basins: assessment of the water-food-energy-ecosystems nexus in the Syr Darya River basin
 
−
| date = 2016
 
−
| publisher = United Nations Economic Commission for Europe (UNECE)
 
−
| url = https://www.unece.org/fileadmin/DAM/env/water/publications/WAT_Nexus/ECE_MP.WAT_46_Chap.7_ENG_Syr-Daria-Web_TF.pdf
 
−
| access-date = 2017-01-13
 
−
}}
 
−
</ref>{{rp|29}} and Mauritius.<ref name="desa-clews-mauritius">
 
−
{{cite web
 
−
| title = Mauritius CLEWS (Climate, Land, Energy, and Water Strategies)
 
−
| publisher = Division for Sustainable Development, Department of Economic and Social Affairs (DESA), United Nations
 
−
| location = New York, USA
 
−
| url = http://un-desa-modelling.github.io/clews-mauritius-presentation/
 
−
| access-date = 2017-01-13
 
−
}}
 
−
</ref>  Models have previously been built for the [[Baltic States]], [[Bolivia]], [[Nicaragua]], and [[Sweden]].
 
−
 
−
An [[EU-28]] model covering western and central Europe is in planning.<ref name="howells-2017">
 
−
{{cite journal
 
−
| first1 = Mark | last1 = Howells
 
−
| date = November 2016
 
−
| title = OSeMOSYS: open source software for energy modelling
 
−
| journal = SETIS magazine
 
−
| number = 13
 
−
| pages = 37–38
 
−
| issn = 2467-382X
 
−
| url = http://publications.jrc.ec.europa.eu/repository/bitstream/JRC103767/2016_no.13_modelling%20magazine_web-version.pdf
 
−
| access-date = 2017-03-01
 
−
}}
 
−
</ref>  The model, funded as part of [[Horizon 2020]] and falling under work package WP7 of the REEEM project, will be used to help stakeholders engage with a range of sustainable energy futures for Europe.<ref name="reeem-work-packages">
 
−
{{cite web
 
−
| title = REEEM – Energy Systems Modelling Project
 
−
| website = Modelling the transformation of the European Energy System
 
−
| access-date = 2017-02-16
 
−
| url = http://www.reeem.org/work-packages/
 
−
}}
 
−
</ref>  The REEEM project runs from early-2016 till mid-2020.
 
−
 
−
In 2016, work started on a [[Web browser|browser]]-based interface to OSeMOSYS, known as the Model Management Infrastructure (MoManI).  Lead by the [[United Nations Department of Economic and Social Affairs|UN Department of Economic and Social Affairs]] (DESA), MoManI is being trialled in selected countries.  The interface can be used to construct models, visualize results, and develop better scenarios.  Atlantis is the name of a fictional country case-study for training purposes.<ref name="howells-etal-2016">
 
−
{{cite book
 
−
| first1 = Mark | last1 = Howells
 
−
| first2 = Abhishek | last2 = Shivakumar
 
−
| first3 = Martynas | last3 = Pelakaukas
 
−
| first4 = Yousef | last4 = Allmulla
 
−
| first5 = Andrii | last5 = Gritsevskyi
 
−
| date = 17 February 2016
 
−
| title = Model Management Interface (MoManI) for OSeMOSYS: supporting development investments and INDCs — Presentation
 
−
| publisher = KTH Royal Institute of Technology and UN Department of Economic and Social Affairs (DESA)
 
−
| location = Stockholm, Sweden and New York, USA
 
−
| url = https://github.com/UN-DESA-Modelling/Atlantis/raw/master/MoManI%20Training%20Overview.pdf
 
−
| access-date = 2017-01-17
 
−
}}
 
−
</ref><ref name="atlantis-website">
 
−
{{cite web
 
−
| title = Atlantis — Integrated Systems Analysis of Energy
 
−
| website = United Nations
 
−
| location = New York, USA
 
−
| url = https://unite.un.org/sites/unite.un.org/files/app-desa-atlantis/index.html
 
−
| access-date = 2017-01-16
 
−
}}
 
−
</ref><ref name="un-desa-github-atlantis">
 
−
{{cite web
 
−
| author = United Nations Department of Economic and Social Affairs (DESA)
 
−
| title = Atlantis
 
−
| website = GitHub
 
−
| url = https://github.com/UN-DESA-Modelling/Atlantis
 
−
| access-date = 2017-01-16
 
−
}}
 
−
</ref>
 
−
 
−
OSeMOSYS is used for university teaching.<ref name="lavigne-2016">
 
−
{{cite journal
 
−
| first = Denis | last = Lavigne
 
−
| title = Initiatives for teaching energy modelling to graduate students
 
−
| date = 2016
 
−
| journal = Universal Journal of Management
 
−
| volume = 4
 
−
| number = 8
 
−
| pages = 451–458
 
−
| doi = 10.13189/ujm.2016.040805
 
−
| url = http://www.hrpub.org/download/20160730/UJM5-12107423.pdf
 
−
| access-date = 2017-01-12
 
−
}}
 
−
</ref>  To that end, a 2017 paper describes the basic UTOPIA model, with an explanation on how to generate [[Pareto efficiency|Pareto frontiers]] for a given system.<ref name="lavigne-2017">
 
−
{{cite journal
 
−
| first = Denis | last = Lavigne
 
−
| title = OSeMOSYS energy modeling using an extended UTOPIA model
 
−
| date = 2017
 
−
| journal = Universal Journal of Educational Research
 
−
| volume = 5
 
−
| number = 1
 
−
| pages = 162–169
 
−
| doi = 10.13189/ujer.2017.050120
 
−
| url = http://www.hrpub.org/download/20161230/UJER20-19508357.pdf
 
−
| access-date = 2017-01-12
 
−
}}
 
−
</ref>{{rp|166–167}}
 
−
 
−
{{clear}}
 
−
 
−
=== TEMOA ===
 
−
 
−
{| class="infobox" style="width: 28em"
 
−
|-
 
−
! style="width: 35%" | Project
 
−
| style="width: 55%" | TEMOA
 
−
|-
 
−
! Host
 
−
| [[North Carolina State University]]
 
−
|-
 
−
! Status
 
−
| active
 
−
|-
 
−
! Scope/type
 
−
| system planning
 
−
|-
 
−
! Code license
 
−
| [[GNU General Public License|GPLv2+]]
 
−
|-
 
−
! Website
 
−
| {{url|http://temoaproject.org}}
 
−
|-
 
−
! Repository
 
−
| {{url|https://github.com/TemoaProject/temoa/}}
 
−
 
−
|}
 
−
 
−
TEMOA stands for Tools for Energy Model Optimization and Analysis.  The software is being developed by the Department of Civil, Construction, and Environmental Engineering, [[North Carolina State University]], [[Raleigh, North Carolina]], USA.  The project runs a website and a forum.  The [[source code]] is hosted on [[GitHub]].  The model is programmed in [[Pyomo]], an optimization components library written in [[Python (programming language)|Python]].  TEMOA can be used with any solver that [[Pyomo]] supports, including the open source [[GLPK]] solver.  TEMOA uses [[version control]] to publicly archive [[source code]] and [[Data (computing)|datasets]] and thereby enable third-parties to verify all published modeling work.<ref name="decarolis-etal-2012">
 
−
{{cite journal
 
−
| first1 = Joseph F | last1 = DeCarolis
 
−
| first2 = Kevin | last2 = Hunter
 
−
| first3 = Sarat | last3 = Sreepathi
 
−
| year = 2012
 
−
| title = The case for repeatable analysis with energy economy optimization models
 
−
| journal = Energy Economics
 
−
| volume = 34
 
−
| pages = 1845–1853
 
−
| doi = 10.1016/j.eneco.2012.07.004
 
−
| url = http://temoaproject.org/publications/DeCarolis_etal_2012.pdf
 
−
| access-date = 2016-07-08
 
−
}}
 
−
</ref>
 
−
 
−
TEMOA classes as a modeling framework and is used to conduct analysis using a bottom-up, technology rich energy system model.  The model objective is to minimize the system-wide cost of energy supply by deploying and utilizing energy technologies and commodities over time to meet a set of [[wiktionary:exogenous|exogenously]] specified end-use demands.<ref name="hunter-etal-2013">
 
−
{{cite journal
 
−
| last1 = Hunter | first1 = Kevin
 
−
| last2 = Sreepathi | first2 = Sarat
 
−
| last3 = DeCarolis | first3 = Joseph F
 
−
| title = Modeling for insight using Tools for Energy Model Optimization and Analysis (TEMOA)
 
−
| year = 2013
 
−
| journal = Energy Economics
 
−
| volume = 40
 
−
| pages = 339–349
 
−
| doi = 10.1016/j.eneco.2013.07.014
 
−
| url = http://www4.ncsu.edu/~jfdecaro/papers/Hunter_etal_2013.pdf
 
−
| access-date = 2016-07-08
 
−
}}
 
−
</ref>  TEMOA is "strongly influenced by the well-documented [[Energy modeling#MARKAL/TIMES|MARKAL/TIMES]] model generators".<ref name="decarolis-etal-2010">
 
−
{{cite book
 
−
| last1 = DeCarolis | first1 = Joseph
 
−
| last2 = Hunter | first2 = Kevin
 
−
| last3 = Sreepathi | first3 = Sarat
 
−
| title = The TEMOA project: Tools for Energy Model Optimization and Analysis
 
−
| year = 2010
 
−
| publisher = Department of Civil, Construction, and Environmental Engineering, North Carolina State University
 
−
| location = Raleigh, North Carolina, USA
 
−
| url = http://www.temoaproject.org/publications/DeCarolis_IEW2010_paper.pdf
 
−
| access-date = 2016-06-17
 
−
}}
 
−
</ref>{{rp|4}}
 
−
 
−
{{clear}}
 
−
 
−
== Project statistics ==
 
−
 
−
Statistics for the 25 open energy modeling projects listed are as follows:
 
−
 
−
{|
 
−
| style="vertical-align: top" |
 
−
 
−
  {| class="wikitable sortable" style="font-size: 100%"
 
−
  |+ Core programming language
 
−
  |-
 
−
  ! Paradigm
 
−
  ! Language
 
−
  ! Count
 
−
  |-
 
−
  | rowspan="1" | [[Imperative programming]]
 
−
  | [[R (programming language)|R]]
 
−
  | align="right" |  1
 
−
  |-
 
−
  | rowspan="4" | [[Object-oriented programming]]{{pad|1em}}
 
−
  | style="background-color: #EEEEB2" | [[C++]]
 
−
  | align="right" |  1
 
−
  |-
 
−
  | style="background-color: #EEEEB2" | [[Java (programming language)|Java]]
 
−
  | align="right" |  2
 
−
  |-
 
−
  | [[Python (programming language)|Python]]
 
−
  | align="right" | 13
 
−
  |-
 
−
  | [[Ruby (programming language)|Ruby]]
 
−
  | align="right" |  1
 
−
  |-
 
−
  | rowspan="2" | [[Mathematical optimization|Mathematical programming]]
 
−
  | style="background-color: #EED0B2" | [[General Algebraic Modeling System|GAMS]]
 
−
  | align="right" |  4
 
−
  |-
 
−
  | [[MathProg]]
 
−
  | align="right" |  2
 
−
  |-
 
−
  | [[Spreadsheet]]
 
−
  | style="background-color: #EED0B2" | [[Microsoft Excel|Excel]]/[[Visual Basic for Applications|VBA]]
 
−
  | align="right" |  1
 
−
  |- class="sortbottom"
 
−
  | colspan="3" style="font-size: smaller" | {{plainlist|
 
−
* {{background color|#EEEEB2|{{pad|2em}}}} indicates a compiled language.
 
−
* {{background color|#EED0B2|{{pad|2em}}}} indicates a commercial software license is required.
 
−
}}
 
−
  |}
 
−
 
−
| {{pad|5em}}    <!-- gutter between tables -->
 
−
| style="vertical-align: top" |
 
−
 
−
  {| class="wikitable sortable" style="font-size: 100%"
 
−
  |+ Primary origin
 
−
  |-
 
−
  ! Country                  !! Count
 
−
  |-
 
−
  | Australia                || style="text-align: right" |  2
 
−
  |-
 
−
  | Denmark                  || style="text-align: right" |  1
 
−
  |-
 
−
  | European Union            || style="text-align: right" |  1
 
−
  |-
 
−
  | Germany                  || style="text-align: right" | 11
 
−
  |-
 
−
  | Netherlands              || style="text-align: right" |  3
 
−
  |-
 
−
  | Sweden{{nnbsp}}{{efn|[[#OSeMOSYS|OSeMOSYS]] is deemed to reside in Sweden due to the influence of the [[Royal Institute of Technology|KTH Royal Institute of Technology]] on the project.}} || style="text-align: right" |  2
 
−
  |-
 
−
  | Switzerland              || style="text-align: right" |  2
 
−
  |-
 
−
  | United Kingdom{{pad|1em}} || style="text-align: right" |  1
 
−
  |-
 
−
  | United States            || style="text-align: right" |  2
 
−
  |}
 
−
 
−
| {{pad|5em}}    <!-- gutter between tables -->
 
−
| style="vertical-align: top" |
 
−
 
−
  {| class="wikitable sortable" style="font-size: 100%"
 
−
  |+ Project host
 
−
  |-
 
−
  ! Type                !! Count
 
−
  |-
 
−
  | Academic institution || style="text-align: right" | 16
 
−
  |-
 
−
  | Commercial entity    || style="text-align: right" |  5
 
−
  |-
 
−
  | Community-based      || style="text-align: right" |  1
 
−
  |-
 
−
  | Non-profit entity    || style="text-align: right" |  2
 
−
  |-
 
−
  | State-sponsored      || style="text-align: right" |  1
 
−
  |}
 
−
 
−
|}
 
−
 
−
The [[General Algebraic Modeling System|GAMS]] language requires a proprietary environment and its significant cost effectively limits participation to those who can access an institutional copy.<ref name="gams-2016">
 
−
{{cite book
 
−
| title = GAMS — Commercial Price List
 
−
| date = 15 March 2016
 
−
| url = http://www.gams.de/sales/commercialp.pdf
 
−
| access-date = 2016-07-11
 
−
}}
 
−
</ref>
 
−
 
−
== Programming components ==
 
−
 
−
=== Component models ===
 
−
 
−
A number of technical component models are now also open source.  While these component models do not constitute systems models aimed at public policy development (the focus of this page), they nonetheless warrant a mention.  Component models can be linked or otherwise adapted into these broader initiatives.
 
−
* Sandia photovoltaic array performance model<ref name="king-etal-2004">
 
−
{{cite book
 
−
| last1 = King | first1 = David L
 
−
| last2 = Boyson | first2 = William E
 
−
| last3 = Kratochvill | first3 = Jay A
 
−
| title = Photovoltaic array performance model — Sandia report SAND2004-3535
 
−
| year = 2004
 
−
| publisher = Sandia Corporation
 
−
| location = USA
 
−
| url = http://prod.sandia.gov/techlib/access-control.cgi/2004/043535.pdf
 
−
| access-date = 2016-06-17
 
−
}}
 
−
</ref>
 
−
 
−
A number of electricity auction models have been written in [[General Algebraic Modeling System|GAMS]], [[AMPL]], [[MathProg]], and other languages.{{efn|
 
−
[[MathProg]] is a subset of [[AMPL]].  It is sometimes possible to convert an AMPL model into MathProg without much effort.
 
−
}}  These include:
 
−
 
−
* the EPOC [[Electricity market#Bid-based, security-constrained, economic dispatch with nodal prices|nodal pricing]] model<ref name="guan-etal-2011">
 
−
{{cite book
 
−
| last1 = Guan | first1 = Ziming
 
−
| last2 = Philpott | first2 = Andy
 
−
| title = Modelling summary for the paper "Production inefficiency of electricity markets with hydro generation"
 
−
| year = 2011
 
−
| publisher = Electric Power Optimization Centre (EPOC), University of Auckland
 
−
| location = Auckland, New Zealand
 
−
| url = http://www.epoc.org.nz/presentations/Modellingsummary.pdf
 
−
| access-date = 2016-06-17
 
−
}}
 
−
</ref>
 
−
 
−
* [https://code.google.com/archive/p/vspd/ vSPD] [[Electricity market#Bid-based, security-constrained, economic dispatch with nodal prices|nodal pricing]] model<ref name="naidoo-2012">
 
−
{{cite book
 
−
| last = Naidoo | first = Ramu
 
−
| title = Vectorised schedule, pricing and dispatch (vSPD) v1.2: a guide to the Excel-based interface
 
−
| year = 2012
 
−
| publisher = Electricity Authority New Zealand
 
−
| location = Wellington, New Zealand
 
−
| url = http://code.google.com/p/vspd
 
−
| access-date = 2016-06-17
 
−
}}
 
−
</ref>
 
−
 
−
* Australian [[National Electricity Market]] examples using [[MathProg]] can be found at [[wikibooks:GLPK/Electricity markets]]
 
−
 
−
=== Open solvers ===
 
−
 
−
Many projects rely on a [[Linear programming|pure linear]] or [[Mixed integer program|mixed integer]] solver to perform classical optimization, constraint satisfaction, or some mix of the two.  While there are several open source solver projects, the most commonly deployed solver is [[GLPK]].  GLPK has been adopted by [[#Calliope|Calliope]], [[#ETEM|ETEM]], [[#ficus|ficus]], [[#OSeMOSYS|OSeMOSYS]], [[#SWITCH|SWITCH]], and [[#TEMOA|TEMOA]].  Another alternative is the Clp solver.<ref name="clp-homepage">
 
−
{{cite web
 
−
| title = Clp homepage
 
−
| url = https://www.coin-or.org/Clp/index.html
 
−
| access-date = 2017-04-23
 
−
}}
 
−
</ref><ref name="coin-or-linear-solver-website">
 
−
{{cite web
 
−
| title = COIN-OR linear programming solver
 
−
| url = https://projects.coin-or.org/Clp
 
−
| access-date = 2017-04-23
 
−
}}
 
−
</ref>  Proprietary solvers outperform open source solvers by a considerable margin (perhaps ten-fold), so choosing an open solver will limit performance in terms of both speed and memory consumption.<ref name="koch-etal-2011">
 
−
{{cite journal
 
−
| last1 = Koch | first1 = Thorsten
 
−
| last2 = Achterberg | first2 = Tobias
 
−
| last3 = Andersen | first3 = Erling
 
−
| last4 = Bastert | first4 = Oliver
 
−
| last5 = Berthold | first5 = Timo
 
−
| last6 = Bixby | first6 = Robert E
 
−
| last7 = Danna | first7 = Emilie
 
−
| last8 = Gamrath | first8 = Gerald
 
−
| last9 = Gleixner | first9 = Ambros M
 
−
| title = MIPLIB 2010: mixed integer programming library version 5
 
−
| year = 2011
 
−
| journal = Mathematical Programming Computation
 
−
| volume = 3
 
−
| issue = 2
 
−
| pages = 103–163
 
−
| doi = 10.1007/s12532-011-0025-9
 
−
| url = http://mpc.zib.de/index.php/MPC/article/viewFile/56/28
 
−
| access-date = 2016-06-17
 
−
}}
 
−
</ref>
 
−
 
−
== See also ==
 
−
 
−
'''General'''
 
−
 
−
* [[Building energy simulation]] – the modeling of energy flows in buildings
 
−
* [[Climate change mitigation scenarios]]
 
−
* [[Collaborative software development model]]
 
−
* [[Energy modeling]] – the process of building computer models of energy systems
 
−
* [[Energy system]] – the interpretation of the energy sector in system terms
 
−
* [[Open Energy Modelling Initiative]] – a European-based energy modeling community
 
−
* [[Open energy system databases]] – database projects which collect, clean, and republish energy-related datasets
 
−
* [[Unit commitment problem in electrical power production]]
 
−
 
−
'''Software'''
 
−
 
−
* [[List of optimization software#Free and open source software|List of free and open source optimization solvers]]
 
−
* [[COIN-OR#CBC|Cbc]] (COIN-OR Branch and Cut) – an open source optimization solver
 
−
* [[COIN-OR#CLP|Clp]] (COIN-OR LP) — an open source linear optimization solver
 
−
* [[Community Climate System Model]] – a mostly open source coupled global climate model
 
−
* [[Earth System Modeling Framework|ESMF]] (Earth System Modeling Framework) – open source software for building [[Climate model|climate]], [[numerical weather prediction]], and [[data assimilation]] applications
 
−
* [[GHGProof]] – an open source land-use model
 
−
* [[GLPK]] (GNU Linear Programming Kit) – an open source linear and mixed integer optimization solver
 
−
 
−
== Notes ==
 
−
 
−
{{notelist}}
 
−
 
−
== References ==
 
−
 
−
{{reflist|30em}}
 
−
 
−
== Further information ==
 
−
 
−
* [http://wiki.openmod-initiative.org/wiki/Open_Models Open energy models wiki] maintained by the [[Open Energy Modelling Initiative]]
 
  
 
== External links ==
 
== External links ==
Line 2,877: Line 380:
 
* [http://www.trnsys.com/ TRNSYS] – the transient system simulation tool
 
* [http://www.trnsys.com/ TRNSYS] – the transient system simulation tool
  
−
<!-- templates and categories -->
+
== References ==
−
{{Energy modeling}}
 
−
{{Computer modeling}}
 
−
{{FOSS}}
 
  
 
<references/>
 
<references/>

Revision as of 13:58, 7 August 2017

Note: This article is based on the Wikipedia article on Open energy system models that was written mostly by Robbie Morrison and edited by others (see Wikipedia history).

Open energy system models are energy system models that are open source. Similarly open energy system data employs open data methods to produce and distribute datasets primarily for use by open energy system models.

Energy system models are used to explore future energy systems and are often applied to questions involving energy and climate policy. The models themselves vary widely in terms of their type, design, programming, application, scope, level of detail, sophistication, and shortcomings.[1]Template:Rp The open energy modeling projects listed here fall exclusively within the bottom-up paradigm, in which a model is a relatively literal representation of the underlying system.[2]Template:Rp For many models, some form of mathematical optimization is used to inform the solution process.

Several drivers favor the development of open models and open data. There is an increasing interest in making public policy energy models more transparent to improve their acceptance by policymakers and the public.[3] There is also a desire to leverage the benefits that open data and open software development can bring, including reduced duplication of effort, better sharing of ideas and information, improved quality, and wider engagement and adoption.[4] Model development is therefore usually a team effort and constituted as either an academic project, a commercial venture, or a genuinely inclusive community initiative.

This article does not cover projects which simply make their source code or spreadsheets available for public download, but which omit a recognized free and open source software license. The absence of a license agreement creates a state of legal uncertainty whereby potential users cannot know which limitations the owner may want to enforce in the future.[5]Template:Rp The projects listed here are deemed suitable for inclusion through having pending or published academic literature or by being reported in secondary sources.

General considerations

Organization

An open energy system modeling project typically comprises a codebase, datasets, and software documentation and perhaps scientific publications.[4] The project repository may be hosted on an institutional server or on a public code-hosting site, such as GitHub. Some projects release only their codebase, while others ship some or all of their datasets as well. Projects may also offer email lists, chat rooms, and web forums to aid collaboration.

The majority of projects are based within university research groups, either singingly or as academic collaborations.

A 2017 paper lists the benefits of open data and models and discusses the reasons that many projects nonetheless remain closed.[6]Template:Rp The paper makes a number of recommendations for projects wishing to transition to a more open approach.[6]Template:Rp The authors also conclude that, in terms of openness, energy research has lagged behind other fields, most notably physics, biotechnology, and medicine.[6]Template:Rp

Growth

Open energy system modeling came of age in the 2010s. Just two projects were cited in a 2011 paper on the topic: OSeMOSYS and TEMOA.[7]Template:Rp Balmorel was also active at that time, having been made public in 2001.Template:Efn Template:As of, this article lists 25 such undertakings (with a further six waiting to be added).

Transparency, comprehensibility, and reproducibility

Template:See also

The use of open energy system models and open energy data represents one attempt to improve the transparency, comprehensibility, and reproducibility of energy system models, particularly those used to aid public policy development.[3]

A 2010 paper concerning energy efficiency modeling argues that "an open peer review process can greatly support model verification and validation, which are essential for model development".[8]Template:Rp[9] To further honor the process of peer review, researchers argue, in a 2012 paper, that it is essential to place both the source code and datasets under publicly accessible version control so that third-parties can run, verify, and scrutinize specific models.[10] A 2016 paper contends that model-based energy scenario studies, seeking to influence decision-makers in government and industry, must become more comprehensible and more transparent. To these ends, the paper provides a checklist of transparency criteria that should be completed by modelers. The authors however state that they "consider open source approaches to be an extreme case of transparency that does not automatically facilitate the comprehensibility of studies for policy advice."[11]Template:Rp

A one-page opinion piece from 2017 advances the case for using open energy data and modeling to build public trust in policy analysis. The article also argues that scientific journals have a responsibility to require that data and code be submitted alongside text for peer review.[12]

State projects

State-sponsored open source projects in any domain are a relatively new phenomena.

Template:As of, the European Commission now supports several open source energy system modeling projects to aid the transition to a low-carbon energy system for Europe. The Dispa-SET project (below) is modeling the European electricity system and hosts its codebase on GitHub. The MEDEAS project, which will design and implement a new open source energy-economy model for Europe, held its kick-off meeting in February 2016.[13]Template:Rp[14] Template:As of, the project had yet to publish any source code. The established OSeMOSYS project (below) is developing a multi-sector energy model for Europe with Commission funding to support stakeholder outreach.[15] The flagship Template:Nowrap model however remains closed source.[16]

The United States NEMS national model is available but nonetheless difficult to use. NEMS does not classify as an open source project in the accepted sense.[12]

Open electricity sector models

Open electricity sector models are confined to just the electricity sector. These models invariably have a temporal resolution of one hour or less. Some models concentrate on the engineering characteristics of the system, including a good representation of high-voltage transmission networks and AC power flow. Others models depict electricity spot markets and are known as dispatch models. While other models embed autonomous agents to capture, for instance, bidding decisions using techniques from bounded rationality. The ability to handle variable renewable energy, transmission systems, and grid storage are becoming important considerations.

Template:Anchor Open electricity sector models
Project Host License Access Coding Documentation Scope/type
DIETER DIW Berlin MIT download GAMS publication dispatch and investment
Dispa-SET EC Joint Research Centre [[European Union Public Licence|EUPLTemplate:Nbsp1.1]] GitHub GAMS, Python website European transmission and dispatch
EMLab-Generation Delft University of Technology Apache 2.0 GitHub Java manual, website agent-based
EMMA Neon Neue Energieökonomik CC BY-SA 3.0 download GAMS website electricity market
GENESYS RWTH Aachen University LGPLv2.1 on application C++ website European electricity system
NEMO University of New South Wales GPLv3 git repository Python website, list Australian NEM market
OnSSET KTH Royal Institute of Technology MIT GitHub Python website, GitHub cost-effective electrification
pandapower Template:Unbulleted list BSD-new GitHub Python website automated power system analysis
PowerMatcher Flexiblepower Alliance Network Apache 2.0 GitHub Java website smart grid
PyPSA Goethe University Frankfurt GPLv3 GitHub Python website electric power systems
renpass University of Flensburg GPLv3 by invitation R, MySQL manual renewables pathways
SciGRID University of Oldenburg Apache 2.0 git repository Python website, newsletter European transmission grid
SIREN Sustainable Energy Now AGPLv3 GitHub Python website renewable generation
SWITCH University of Hawai'i Apache 2.0 GitHub Python website optimal planning
URBS Technical University of Munich GPLv3 GitHub Python website distributed energy systems
Template:Plainlist


External links

References

  1. ↑ Pye, Steve; Bataille, Chris "Improving deep decarbonization modelling capacity for developed and developing country contexts". Climate Policy. 16 (S1): S27–S46. doi:10.1080/14693062.2016.1173004.
  2. ↑ Kolstad, Charles; Urama, Kevin; Broome, John; Bruvoll, Annegrete; Olvera, Micheline Cariño; Fullerton, Don; Gollier, Christian; Hanemann, William Michael; Hassan, Rashid; Jotzo, Frank; Khan, Mizan R; Meyer, Lukas; Mundaca, Luis Climate change 2014: mitigation of climate change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press. ISBN 978-1-107-65481-5. Retrieved 2016-05-09. chapter not supported.
  3. ↑ 3.0 3.1 (2016). Consulting with energy scenarios: requirements for scientific policy advice.. acatech — National Academy of Science and Engineering. ISBN 978-3-8047-3550-7. Retrieved 2016-12-19.
  4. ↑ 4.0 4.1 Bazilian, Morgan; Rice, Andrew; Rotich, Juliana; Howells, Mark; DeCarolis, Joseph; Macmillan, Stuart; Brooks, Cameron; Bauer, Florian; Liebreich, Michael "Open source software and crowdsourcing for energy analysis". Energy Policy. 49: 149–153. doi:10.1016/j.enpol.2012.06.032. Retrieved 2016-06-17.
  5. ↑ Morin, Andrew; Urban, Jennifer; Sliz, Piotr (26 July 2012). "A quick guide to software licensing for the scientist-programmer". PLOS Computational Biology. 8: e1002598. doi:10.1371/journal.pcbi.1002598. ISSN 1553-7358. Retrieved 2016-12-10. Template:Open access
  6. ↑ 6.0 6.1 6.2 Pfenninger, Stefan; DeCarolis, Joseph; Hirth, Lion; Quoilin, Sylvain; Staffell, Iain (February 2017). "The importance of open data and software: is energy research lagging behind?". Energy Policy. 101: 211–215. doi:10.1016/j.enpol.2016.11.046. ISSN 0301-4215. Retrieved 2017-02-03. Template:Open access
  7. ↑ Cite error: Invalid <ref> tag; no text was provided for refs named howells-etal-2011
  8. ↑ Mundaca, Luis; Neij, Lena; Worrell, Ernst; McNeil, Michael A (1 August 2010). Evaluating energy efficiency policies with energy-economy models — Report number LBNL-3862E.. Berkeley, CA, US: Ernest Orlando Lawrence Berkeley National Laboratory. Retrieved 2016-11-15.
  9. ↑ Mundaca, Luis; Neij, Lena; Worrell, Ernst; McNeil, Michael A (22 October 2010). "Evaluating energy efficiency policies with energy-economy models". Annual Review of Environment and Resources. 35: 305–344. doi:10.1146/annurev-environ-052810-164840. ISSN 1543-5938.
  10. ↑ Cite error: Invalid <ref> tag; no text was provided for refs named decarolis-etal-2012
  11. ↑ Cao, Karl-Kiên; Cebulla, Felix; Gómez Vilchez, Jonatan J; Mousavi, Babak; Prehofer, Sigrid (28 September 2016). "Raising awareness in model-based energy scenario studies — a transparency checklist". Energy, Sustainability and Society. 6: 28–47. doi:10.1186/s13705-016-0090-z. ISSN 2192-0567. Retrieved 2016-10-04. Template:Open access
  12. ↑ 12.0 12.1 (23 February 2017). "Energy scientists must show their workings". Nature News. 542: 393. doi:10.1038/542393a. Retrieved 2017-02-26.
  13. ↑ (November 2016). "SET-Plan update". SETIS magazine. (13): 5–7 ISSN 2467-382X. Retrieved 2017-03-01.
  14. ↑ Template:Cite web
  15. ↑ Cite error: Invalid <ref> tag; no text was provided for refs named howells-2017
  16. ↑ Simoes, Sofia; Nijs, Wouter; Ruiz, Pablo; Sgobbi, Alessandra; Radu, Daniela; Bolat, Pelin; Thiel, Christian; Peteves, Stathis (2013). The JRC-EU-TIMES model: assessing the long-term role of the SET Plan energy technologies — LD-NA-26292-EN-N.. Luxembourg: Publications Office of the European Union. ISBN 978-92-79-34506-7. Retrieved 2017-03-03. The DOI, ISBN, and ISSN refer to the online version.
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