Open Energy System Models

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Note: This article is based on the Wikipedia article on Open energy system models, which was written by Robbie Morrison and edited by a few other people (see article history in Wikipedia). You can find more information about all of the models mentioned here on the Wikipedia page. If you want to dive deeper into open energy system models, feel free to access the openmod initiative's Wiki pages as well.

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] 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] 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] 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] The paper makes a number of recommendations for projects wishing to transition to a more open approach.[6] The authors also conclude that, in terms of openness, energy research has lagged behind other fields, most notably physics, biotechnology, and medicine.[6]

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] Balmorel was also active at that time, having been made public in 2001.

Transparency, comprehensibility, and reproducibility

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][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]

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.

As of 2017, 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 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], the project had yet to publish any source code. The established OSeMOSYS project is developing a multi-sector energy model for Europe with Commission funding to support stakeholder outreach.[14] The flagship JRC-EU-TIMES model however remains closed source.[15]

The United States National Energy Modeling System 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.

Open electricity sector models
Project Host License Access Coding Documentation Scope/type
DIETER DIW Berlin MIT license download GAMS publication dispatch and investment
Dispa-SET EC Joint Research Centre EUPL 1.1 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 University of Kassel, Fraunhofer IWES 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
Access refers to the methods offered for accessing the codebase.

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 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.[16]

Open energy system models (bottom-up, with support for heat, gas, and such, as well as electricity)
Project Host License Access Coding Documentation Scope/type
Balmorel Denmark ISC registration GAMS manual energy markets
Calliope ETH Zurich Apache 2.0 download Python manual, website, list dispatch and investment
DESSTinEE Imperial College London CC-BY-SA 3.0 download Excel/VBA website simulation
Energy Transition Model Quintel Intelligence MIT GitHub Ruby website web-based
EnergyPATHWAYS Evolved Energy Research MIT GitHub Python website mostly simulation
ETEM ORDECSYS, Switzerland Eclipse 1.0 registration MathProg manual municipal
ficus Technical University of Munich GPLv3 GitHub Python manual local electricity and heat
oemof oemof community supported by Reiner Lemoine Institute, University of Flensburg, Fachhochschule Flensburg GPLv3 GitHub Python website framework - dispatch, investment, all sectors, LP/MILP
OSeMOSYS]] OSeMOSYS community Apache 2.0 GitHub GAMS, MathProg, Python website, forum planning at all scales
TEMOA North Carolina State University GPLv2+ GitHub Python website, forum system planning
GENeSYS-MOD Technical University of Berlin Apache 2.0 GitHub GAMS/Julia manual multi‑commodity optimization
Access refers to the methods offered for accessing the codebase.


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.
  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.
  7. ↑ Howells, Mark; Rogner, Holger; Strachan, Neil; Heaps, Charles; Huntington, Hillard; Kypreos, Socrates; Hughes, Alison; Silveira, Semida; DeCarolis, Joe; Bazilian, Morgan; Roehrl, Alexander "OSeMOSYS: the open source energy modeling system : an introduction to its ethos, structure and development". Energy Policy. 39: 5850–5870. doi:10.1016/j.enpol.2011.06.033. The name Morgan Bazillian has been corrected. ResearchGate version.
  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. ↑ DeCarolis, Joseph F; Hunter, Kevin; Sreepathi, Sarat "The case for repeatable analysis with energy economy optimization models". Energy Economics. 34: 1845–1853. doi:10.1016/j.eneco.2012.07.004. Retrieved 2016-07-08.
  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.
  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. ↑ Moura, Gustavo; Howells, Mark (August 2015). 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.. Sockholm, Sweden: Royal Institute of Technology (KTH). Available for download from ResearchGate.
  15. ↑ 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.
  16. ↑ Bussar, Christian; Moos, Melchior; Alvarez, Ricardo; Wolf, Philipp; Thien, Tjark; Chen, Hengsi; Cai, Zhuang; Leuthold, Matthias; Sauer, Dirk Uwe; Moser, Albert "Optimal allocation and capacity of energy storage systems in a future European power system with 100% renewable energy generation". Energy Procedia. 46: 40–47. doi:10.1016/j.egypro.2014.01.156. Retrieved 2016-07-07.
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