View source for Earth:Open energy system models

{{Short description|Energy system models that are open source}}
{{for|sources of the open data required for open modeling|Open energy system databases}}

{{broader|energy modeling}}

'''Open energy-system models''' are energy-system [[Earth:Energy modeling|models]] that are open source.{{efn|
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.}} However, some of them may use third-party proprietary software as part of their workflows to input, process, or output data. Preferably, these models use [[Open data|open data]], which facilitates [[Open science|open science]].

Energy-system models are used to explore future energy systems and are often applied to questions involving [[Earth:Energy policy|energy]] and [[Earth:Politics of global warming|climate policy]]. The models themselves vary widely in terms of their type, design, [[Programming language|programming]], application, scope, level of detail, sophistication, and shortcomings. For many models, some form of [[Mathematical optimization|mathematical optimization]] is used to inform the solution process.

Energy regulators and system operators in Europe and North America began adopting open energy-system models for planning purposes in the early{{nbhyph}}2020s.<ref name="fairley-2023">
{{cite web
 | last1 = Fairley | first1 = Peter
 | title = EU: Climate-proof grids require more transparency > 'Black-box' US energy planning hinders renewable energy development
 | date = 27 June 2023
 | work = IEEE Spectrum
 | url = https://spectrum.ieee.org/power-grid-transparency-eu-us
 | location = New York, USA
 | issn = 0018-9235
 | access-date = 2023-06-27
}}  Web feature.
</ref> Open models and open data are increasingly being used by government agencies to guide the develop of net{{nbhyph}}zero public policy as well (with examples indicated throughout this article). Companies and engineering consultancies are likewise adopting open models for analysis (again see{{nbsp}}below).

== General considerations ==

{{see also|Earth:Energy modeling|Open-source software development}}

=== Organization ===

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.

Several drivers favor the development of open models and open data. There is an increasing interest in making [[Social:Public policy|public policy]] energy models more transparent to improve their acceptance by policymakers and the public.<ref name="acatech-etal-2016"><!-- alternative URL: http://www.akademienunion.de/fileadmin/redaktion/user_upload/Publikationen/Stellungnahmen/Stellungnahme_Energy_scenarios.pdf -->
{{cite book
 | editor1 = acatech
 | editor2 = Lepoldina
 | editor3 = Akademienunion
 | title = Consulting with energy scenarios: requirements for scientific policy advice
 | date = 2016
 | publisher = acatech — National Academy of Science and Engineering
 | place = Berlin, Germany
 | isbn = 978-3-8047-3550-7
 | url = http://www.acatech.de/fileadmin/user_upload/Baumstruktur_nach_Website/Acatech/root/de/Publikationen/Kooperationspublikationen/ESYS_Position_Paper_Energy_scenarios.pdf
 | access-date = 2016-12-19
 | archive-url = https://web.archive.org/web/20161221021207/http://www.acatech.de/fileadmin/user_upload/Baumstruktur_nach_Website/Acatech/root/de/Publikationen/Kooperationspublikationen/ESYS_Position_Paper_Energy_scenarios.pdf
 | archive-date = 21 December 2016
 | url-status = dead
 }}</ref> There is also a desire to leverage the benefits that open data and [[Software: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">
{{cite journal
 | last1 = Bazilian | first1 = Morgan
 | last2 = Rice | first2 = Andrew
 | last3 = Rotich | first3 = Juliana
 | last4 = Howells | first4 = Mark
 | last5 = DeCarolis | first5 = Joseph
 | last6 = Macmillan | first6 = Stuart
 | last7 = Brooks | first7 = Cameron
 | last8 = Bauer | first8 = Florian
 | last9 = Liebreich | first9 = Michael
 | title = Open source software and crowdsourcing for energy analysis
 | year = 2012
 | journal = Energy Policy
 | volume = 49
 | pages = 149–153
 | doi = 10.1016/j.enpol.2012.06.032
 | bibcode = 2012EnPol..49..149B
 | url = http://www.ourenergypolicy.org/wp-content/uploads/2012/07/BNEF_open_source_software_and_crowdsourcing_for_energy_analysis.pdf
 | access-date = 2016-06-17
}}
</ref> 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|source code]] or [[Engineering:Spreadsheet|spreadsheet]]s available for public download, but which omit a recognized [[Free software license|free and open-source software license]]. The absence of a [[Finance:License|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">
<!-- website URL: http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002598 -->
{{cite journal
 | last1 = Morin | first1 = Andrew
 | last2 = Urban | first2 = Jennifer
 | last3 = Sliz | first3 = Piotr
 | title = A quick guide to software licensing for the scientist-programmer
 | date = 26 July 2012
 | journal = PLOS Computational Biology
 | volume = 8
 | issue = 7
 | pages = e1002598
 | doi = 10.1371/journal.pcbi.1002598
 | doi-access=free
 | pmid = 22844236
 | pmc = 3406002
 | issn = 1553-7358
 | bibcode = 2012PLSCB...8E2598M
 }}
</ref>{{rp|1}} The projects listed here are deemed suitable for inclusion through having pending or published academic literature or by being reported in secondary sources.

A 2017 paper lists the benefits of open data and models and discusses the reasons that many projects nonetheless remain closed.<ref name="pfenninger-etal-2017">
{{cite journal
 | last1 = Pfenninger | first1 = Stefan
 | last2 = DeCarolis | first2 = Joseph
 | last3 = Hirth | first3 = Lion
 | last4 = Quoilin | first4 = Sylvain
 | last5 = Staffell | first5 = Iain
 | date = February 2017
 | title = The importance of open data and software: is energy research lagging behind?
 | journal = Energy Policy
 | volume = 101
 | pages = 211–215
 | doi = 10.1016/j.enpol.2016.11.046
 | issn = 0301-4215
 | doi-access = free
 | bibcode = 2017EnPol.101..211P
 | hdl = 10044/1/56796
 | hdl-access = free
 }}
</ref>{{rp|211–213}} The paper makes a number of recommendations for projects wishing to transition to a more open approach.<ref name="pfenninger-etal-2017"/>{{rp|214}} The authors also conclude that, in terms of openness, energy research has lagged behind other fields, most notably physics, biotechnology, and medicine.<ref name="pfenninger-etal-2017"/>{{rp|213–214}}

=== 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.<ref name="howells-etal-2011"/>{{rp|5861}} Balmorel was also active at that time, having been made public in 2001.{{efn|
NEMO was also under development in 2011 but it is unclear whether its codebase was public at that point.
}} {{as of|2022|07}}, 31{{nbsp}}such undertakings are listed here (with an approximately equal number waiting to be added).
Chang ''et{{nbsp}}al'' (2021) survey modeling trends and find the open to closed division about even after reviewing 54{{nbsp}}frameworks {{mdash}} although that interpretation is based on project count and not on uptake and use.<ref name="chang-etal-2021">
{{cite journal
 | last1 = Chang | first1 = Miguel
 | last2 = Thellufsen | first2 = Jakob Zink
 | last3 = Zakeri | first3 = Behnam
 | last4 = Pickering | first4 = Bryn
 | last5 = Pfenninger | first5 = Stefan
 | last6 = Lund | first6 = Henrik
 | last7 = Østergaard | first7 = Poul Alberg
 | title = Trends in tools and approaches for modelling the energy transition
 | date = 15 May 2021
 | journal = Applied Energy
 | volume = 290
 | pages = 116731
 | doi = 10.1016/j.apenergy.2021.116731
 | doi-access=free
 | bibcode = 2021ApEn..29016731C
 | s2cid = 233585332
 | issn = 0306-2619
| hdl = 20.500.11850/475214
 | hdl-access = free
 }} See figure{{nbsp}}4 in particular.
</ref> A{{nbsp}}2022 model comparison exercise in Germany reported eight from 40{{nbsp}}modeling projects (20%) were open source,<ref name="syranidou-etal-2022">
{{cite journal
 | last1 = Syranidou | first1 = Chloi
 | last2 = Koch | first2 = Matthias
 | last3 = Matthes | first3 = Björn
 | last4 = Winger | first4 = Christian
 | last5 = Linßen | first5 = Jochen
 | last6 = Rehtanz | first6 = Christian
 | last7 = Stolten | first7 = Detlef
 | title = Development of an open framework for a qualitative and quantitative comparison of power system and electricity grid models for Europe
 | date = 1 May 2022
 | journal = Renewable and Sustainable Energy Reviews
 | volume = 159
 | pages = 112055
 | doi = 10.1016/j.rser.2021.112055
 | s2cid = 246588297
 | issn = 1364-0321
 | url = https://www.sciencedirect.com/science/article/pii/S1364032121013174/pdfft
 | access-date = 2022-06-03
| doi-access = free
 | bibcode = 2022RSERv.15912055S
 }}  {{open access}}
</ref> these projects also had active communities behind them.<ref name="morrison-2022">
{{cite web
 | last1 = Morrison | first1 = Robbie
 | title = MODEX framework comparison study for Germany — Blog
 | date = 3 June 2022
 | work = Open Energy Modelling Initiative
 | url = https://forum.openmod.org/t/modex-framework-comparison-study-for-germany/3243/7
 | access-date = 2022-07-24
}}  {{open access}}
</ref>

=== 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.<ref name="acatech-etal-2016"/>

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".<ref name="mundaca-etal-2010a"><!--
document listing: http://www.osti.gov/scitech/biblio/1001644
document listing: https://eta.lbl.gov/publications/evaluating-energy-efficiency-policies
alternative download: http://www.osti.gov/scitech/servlets/purl/1001644
-->
{{cite journal|url=https://ies.lbl.gov/sites/all/files/lbnl-3862e_1.pdf|title=Evaluating energy efficiency policies with energy-economy models — Report number LBNL-3862E|journal=Annual Review of Environment and Resources|last1=Mundaca|first1=Luis|last2=Neij|first2=Lena|last3=Worrell|first3=Ernst|last4=McNeil|first4=Michael A|date=1 August 2010|publisher=Ernest Orlando Lawrence Berkeley National Laboratory|location=Berkeley, CA, US|doi=10.1146/annurev-environ-052810-164840|osti=1001644|access-date=2016-11-15|archive-date=21 December 2016|archive-url=https://web.archive.org/web/20161221114759/https://ies.lbl.gov/sites/all/files/lbnl-3862e_1.pdf|url-status=dead}}</ref>{{rp|17}}<ref name="mundaca-etal-2010b">
{{cite journal
 | last1 = Mundaca | first1 = Luis
 | last2 = Neij | first2 = Lena
 | last3 = Worrell | first3 = Ernst
 | last4 = McNeil | first4 = Michael A
 | title = Evaluating energy efficiency policies with energy-economy models
 | date = 22 October 2010
 | journal = Annual Review of Environment and Resources
 | volume = 35
 | issue = 1
 | pages = 305–344
 | doi = 10.1146/annurev-environ-052810-164840
 | issn = 1543-5938
| url = https://digital.library.unt.edu/ark:/67531/metadc837136/
 | doi-access = free
 }}
</ref> To further honor the process of [[Peer review|peer review]], researchers argue, in a 2012 paper, that it is essential to place both the [[Source code|source code]] and datasets under publicly accessible [[Version control|version control]] so that third-parties can run, verify, and scrutinize specific models.<ref name="decarolis-etal-2012"/> 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 [[Medicine:Checklist|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."<ref name="cao-etal-2016">
<!-- license: Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. -->
{{cite journal
 | last1 = Cao | first1 = Karl-Kiên
 | last2 = Cebulla | first2 = Felix
 | last3 = Gómez Vilchez | first3 = Jonatan J
 | last4 = Mousavi | first4 = Babak
 | last5 = Prehofer | first5 = Sigrid
 | s2cid = 52243291
 | date = 28 September 2016
 | title = Raising awareness in model-based energy scenario studies — a transparency checklist
 | journal = Energy, Sustainability and Society
 | volume = 6
 | issue = 1
 | pages = 28–47
 | doi = 10.1186/s13705-016-0090-z
 | issn = 2192-0567
 | doi-access = free
 | bibcode = 2016ESusS...6...28C
 }}
</ref>{{rp|4}}

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 [[Academic journal|scientific journals]] have a responsibility to require that data and code be submitted alongside text for [[Peer review|peer review]].<ref name="pfenninger-2017"><!-- alternative url: http://www.nature.com/news/energy-scientists-must-show-their-workings-1.21517 -->
{{cite journal|last=Pfenninger|first=Stefan|s2cid=4449502|date=23 February 2017|title=Energy scientists must show their workings|url=http://www.nature.com/polopoly_fs/1.21517!/menu/main/topColumns/topLeftColumn/pdf/542393a.pdf|journal=Nature News|volume=542|issue=7642|pages=393|bibcode=2017Natur.542..393P|doi=10.1038/542393a|pmid=28230147|access-date=2017-02-26}}
</ref> And an academic commentary from 2020 argues that distributed development would facilitate a more diverse contributor base and thus improve model quality{{nbsp}}{{mdash}} a process supported by online platforms and enabled by open data and code.<ref name="decarolis-etal-2020"/>

=== 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 (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.<ref name="set-plan-2016-one">
{{cite journal
 | author = <!-- staff writer, no by-line -->
 | date = November 2016
 | title = SET-Plan update
 | journal = SETIS Magazine
 | number = 13
 | pages = 5–7
 | 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>{{rp|6}}<ref name="medeas-website">
{{cite web
 | title = Medeas: modeling the renewable energy transition in Europe
 | work = Spanish National Research Council (CSIC)
 | location = Barcelona, Spain
 | url = http://www.medeas.eu
 | access-date = 2017-03-01
}}
</ref> {{as of|2017|02}}, 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.<ref name="howells-2016">
{{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 flagship {{nowrap|JRC-EU-TIMES}} model however remains closed source.<ref name="simoes-etal-2013">
{{cite book
 | first1 = Sofia | last1 = Simoes
 | first2 = Wouter | last2 = Nijs
 | first3 = Pablo | last3 = Ruiz
 | first4 = Alessandra | last4 = Sgobbi
 | first5 = Daniela | last5 = Radu
 | first6 = Pelin | last6 = Bolat
 | first7 = Christian | last7 = Thiel
 | first8 = Stathis | last8 = Peteves
 | date = 2013
 | title = The JRC-EU-TIMES model: assessing the long-term role of the SET Plan energy technologies — LD-NA-26292-EN-N
 | publisher = Publications Office of the European Union
 | location = Luxembourg
 | doi = 10.2790/97596
 | issn = 1831-9424
 | isbn = 978-92-79-34506-7
 | url = http://publications.jrc.ec.europa.eu/repository/bitstream/JRC85804/jrc_times_%20eu_overview_online.pdf
 | access-date = 2017-03-03
}}  The DOI, ISBN, and ISSN refer to the online version.
</ref>

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.<ref name="pfenninger-2017"/>

A 2021 research call from the European Union Horizon Europe scientific research funding program expressly sought energy system models that are open source.<ref name="european-commission-2021">
{{cite web
 | author = European Commission
 | title = Funding and tenders — Energy system modelling, optimisation and planning tools — TOPIC{{nbsp}}ID: HORIZON-CL5-2022-D3-01-13
 | date = 14 October 2021
 | work = European Commission
 | url = https://ec.europa.eu/info/funding-tenders/opportunities/portal/screen/opportunities/topic-details/horizon-cl5-2022-d3-01-13
 | access-date = 2021-11-10
}}  Deadline 26{{nbsp}}April 2022.
</ref>

=== Surveys ===

A survey completed in 2021 investigated the degree to which open energy-system modeling frameworks support flexibility options, broken down by supply, demand, [[Engineering:Energy storage|storage]], sector coupled, and network response. Of the frameworks surveyed, none supported all types, which suggests that the soft coupling of complementary frameworks could provide more holistic assessments of flexibility. Even so, most candidates opt for perfect foresight and do not natively admit probabilistic actions or explicit [[Decision theory|behavioral responses]].<ref name="heider-etal-2021">
{{cite journal |last1=Heider |first1=Anya |last2=Reibsch |first2=Ricardo |last3=Blechinger |first3=Philipp |last4=Linke |first4=Avia |last5=Hug |first5=Gabriela |date=1 November 2021 |title=Flexibility options and their representation in open energy modelling tools |journal=Energy Strategy Reviews |volume=38 |pages=100737 |doi=10.1016/j.esr.2021.100737 |issn=2211-467X |s2cid=244151317 |doi-access=free |bibcode=2021EneSR..3800737H |hdl-access=free |hdl=20.500.11850/516243}}
</ref>

== 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 [[Finance:Electric power transmission|high-voltage transmission networks]] and [[Physics:Power-flow study|AC power flow]]. Others models depict electricity [[Finance:Spot market|spot market]]s and are known as dispatch models. While other models embed [[Agent-based model|autonomous agents]] to capture, for instance, [[Finance:Auction|bidding decisions]] using techniques from [[Bounded rationality|bounded rationality]]. The ability to handle [[Engineering:Variable renewable energy|variable renewable energy]], transmission systems, and [[Engineering:Grid energy storage|grid storage]] are becoming important considerations.

{| class="wikitable sortable"
|+ {{anchor|table-open-electricity-sector-models}} Open electricity sector models
|-
! Project
! Host
! License
! Access
! Coding
! Documentation
! Scope/type
|-
| AMIRIS
| [[Organization:German Aerospace Center|German Aerospace Center]]
| [[Software:Apache License|Apache{{nbsp}}2.0]]
| [[Software:GitLab|GitLab]]
| [[Java (programming language)|Java]]
| wiki
| [[Agent-based model|agent{{nbhyph}}based]] electricity market modeling
|-
| Breakthrough Energy Model
| Breakthrough Energy Foundation
| MIT
| [[GitHub]]
| [[Python (programming language)|Python]], [[Julia (programming language)|Julia]]
| website, GitHub
| power sector modeling
|-
| DIETER
| [[Organization:German Institute for Economic Research|DIW Berlin]]
| MIT
| download
| [[Software:General Algebraic Modeling System|GAMS]]
| publication
| dispatch and investment
|-
| Dispa-SET
| EC Joint Research Centre
| [[Software:European Union Public Licence|EUPL{{nbsp}}1.1]]
| [[GitHub]]
| [[Software:General Algebraic Modeling System|GAMS]], [[Python (programming language)|Python]]
| website
| European transmission and dispatch
|-
| EMLab-Generation
| [[Organization:Delft University of Technology|Delft University of Technology]]
| [[Software:Apache License|Apache 2.0]]
| [[GitHub]]
| [[Java (programming language)|Java]]
| manual, website
| [[Agent-based model|agent-based]]
|-
| EMMA
| Neon Neue Energieökonomik
| [[Creative Commons license|CC BY-SA 3.0]]
| download
| [[Software:General Algebraic Modeling System|GAMS]]
| website
| electricity market
|-
| GENESYS
| RWTH Aachen University
| [[Software:GNU Lesser General Public License|LGPLv2.1]]
| on application
| [[C++]]
| website
| European electricity system
|-
| NEMO
| University of New South Wales
| [[Software:GNU General Public License|GPLv3]]
| git repository
| [[Python (programming language)|Python]]
| website, list
| Australian NEM market
|-
| OnSSET
| KTH Royal Institute of Technology
| [[Software:MIT License|MIT]]
| [[GitHub]]
| [[Python (programming language)|Python]]
| website, GitHub
| cost-effective electrification
|-
| pandapower
| {{unbulleted list|[[Organization:University of Kassel|University of Kassel]]|Fraunhofer Institute IEE}}
| BSD-new
| [[GitHub]]
| Python
| website
| automated power system analysis
|-
| PowerMatcher
| Flexiblepower Alliance Network
| [[Software:Apache License|Apache 2.0]]
| [[GitHub]]
| [[Java (programming language)|Java]]
| website
| smart grid
|-
| Power TAC
| {{unbulleted list|Erasmus Centre for Future Energy Business|Rotterdam School of Management|Erasmus University}}
| [[Software:Apache License|Apache 2.0]]
| [[GitHub]]
| [[Java (programming language)|Java]]
| website, forum
| automated retail electricity trading simulation
|-
| renpass
| [[Organization:University of Flensburg|University of Flensburg]]
| [[Software:GNU General Public License|GPLv3]]
| by invitation
| [[R (programming language)|R]], [[Software:MySQL|MySQL]]
| manual
| renewables pathways
|-
| SciGRID
| DLR Institute of Networked Energy Systems
| [[Software:Apache License|Apache 2.0]]
| git repository
| [[Python (programming language)|Python]]
| website, newsletter
| European transmission grid
|-
| SIREN
| Sustainable Energy Now
| [[Affero General Public License|AGPLv3]]
| [[GitHub]]
| [[Python (programming language)|Python]]
| website
| renewable generation
|-
| SWITCH
| [[Organization:University of Hawaiʻi|University of Hawaiʻi]]
| [[Software:Apache License|Apache 2.0]]
| [[GitHub]]
| [[Python (programming language)|Python]]
| website
| optimal planning
|-
| URBS
| Technical University of Munich
| [[Software:GNU General Public License|GPLv3]]
| [[GitHub]]
| [[Python (programming language)|Python]]
| website
| distributed energy systems
|- class="sortbottom"
| colspan="7" style="font-size: smaller" | {{plainlist|
* '''Access''' refers to the methods offered for accessing the codebase.
}}
|}

=== AMIRIS ===

{| class="infobox" style="width: 28em"
|-
! style="width: 35%" | Project
| style="width: 55%" | AMIRIS
|-
! Host
| [[Organization:German Aerospace Center|German Aerospace Center]]
|-
! Status
| active
|-
! Scope/type
| agent{{nbhyph}}based electricity markets
|-
! Code license
| Apache-2.0
|-
! Data license
| [[Creative Commons license|CC{{nbhyph}}BY{{nbhyph}}4.0]]
|-
! Language
| [[Java (programming language)|Java]]
|-
! Website
| {{URL|https://helmholtz.software/software/amiris}}
|-
! Repository
| {{URL|https://gitlab.com/dlr-ve/esy/amiris/amiris}}
|-
! Documentation
| {{URL|https://gitlab.com/dlr-ve/esy/amiris/amiris/-/wikis/home}}
|-
! Discussion
| {{URL|https://forum.openmod.org/tag/amiris}}
|-
! Datasets
| {{URL|https://gitlab.com/dlr-ve/esy/amiris/examples}}
|-
! Publications
| {{URL|https://zenodo.org/communities/amiris}}
|}

AMIRIS is the open [[Agent-based model|Agent-based]] Market model for the Investigation of Renewable and Integrated energy Systems. The AMIRIS simulation framework was first developed by the [[Organization:German Aerospace Center|German Aerospace Center]] (DLR) in 2008 and later released as an open source project in 2021.<ref name="klein-etal-2019">
{{cite journal
 | last1 = Klein | first1 = Martin
 | last2 = Frey | first2 = Ulrich J
 | last3 = Reeg | first3 = Matthias
 | title = Models within models: agent{{nbhyph}}based modelling and simulation in energy systems analysis
 | date = 2019
 | journal = Journal of Artificial Societies and Social Simulation
 | volume = 22
 | issue = 4
 | pages = 6
 | doi = 10.18564/jasss.4129
 | s2cid = 208170977
 | issn = 1460-7425
| doi-access = free
 }}  {{open access}}
</ref>

AMIRIS enables researchers to address questions regarding future energy markets, their market design, and energy-related policy instruments.<ref name="deissenroth-etal-2017">
{{cite journal
 | last1 = Deissenroth | first1 = Marc
 | last2 = Klein | first2 = Martin
 | last3 = Nienhaus | first3 = Kristina
 | last4 = Reeg | first4 = Matthias
 | title = Assessing the plurality of actors and policy interactions: agent{{nbhyph}}based modelling of renewable energy market integration
 | date = 10 December 2017
 | journal = Complexity
 | volume = 2017
 | pages = 1–24
 | doi = 10.1155/2017/7494313
 | issn = 1076-2787
 | url = https://downloads.hindawi.com/journals/complexity/2017/7494313.pdf
 | access-date = 2021-05-21
| doi-access = free
 }}
</ref>
In particular, AMIRIS is able to capture market effects that may arise from the integration of [[Engineering:Renewable energy|renewable energy]] sources and flexibility options by considering the strategies and behaviors of the various energy market actors present. For instance, those behaviors can be influenced by the prevailing political framework and by external uncertainties.<ref name="torralba-etal-2020">{{cite journal |last1=Torralba-Díaz |first1=Laura |last2=Schimeczek |first2=Christoph |last3=Reeg |first3=Matthias |last4=Savvidis |first4=Georgios |last5=Deissenroth-Uhrig |first5=Marc |last6=Guthoff |first6=Felix |last7=Fleischer |first7=Benjamin |last8=Hufendiek |first8=Kai |title=Identification of the Efficiency Gap by Coupling a Fundamental Electricity Market Model and an Agent-Based Simulation Model |journal=Energies |date=January 2020 |volume=13 |issue=15 |pages=3920 |doi=10.3390/en13153920|doi-access=free }}</ref> AMIRIS may also uncover [[Finance:Complexity economics|complex effects]] that may emerge from the inter{{nbhyph}}dependencies of the energy market participants.<ref name="frey-etal-2020">
{{cite journal
 | last1 = Frey | first1 = Ulrich
 | last2 = Klein | first2 = Martin
 | last3 = Nienhaus | first3 = Kristina
 | last4 = Schimeczek | first4 = Christoph
 | title = Self-Reinforcing Electricity Price Dynamics under the Variable Market Premium Scheme
 | date = 14 October 2020
 | journal = Energies
 | volume = 13
 | issue = 20
 | page = 5350
 | doi = 10.3390/en13205350
 | issn = 1996-1073
 | url = https://www.mdpi.com/1996-1073/13/20/5350/pdf
 | access-date = 2022-04-04
| doi-access = free
 }}
</ref>

[[File:AMIRIS Architecture.png|thumb|left|upright=1.5|alt=The figure provides an overview of the agents modeled in AMIRIS and illustrates the associated information, energy, and financial flows.|AMIRIS architecture]]

The embedded market clearing algorithm computes [[Finance:Electricity pricing|electricity prices]] based on the bids of prototyped market actors. These bids may not only reflect the [[Finance:Marginal cost|marginal cost]] of electricity production but also the limited information available to the actors and related uncertainties. But also the bidding can be strategic as an attempt to [[Game theory|game]] official support instruments or exploit [[Social:Market power|market power]] opportunities.

Actors in AMIRIS are represented as [[Agent-based model|agents]] that can be roughly divided into six classes: power plant operators, traders, market operators, policy providers, demand agents, and storage facility operators. In the model, power plant operators provide generation capacities to traders, but do not participate directly in markets. Instead, they supply traders who conduct the marketing and deploy bidding strategies on the operators behalf. Marketplaces serve as trading platforms and calculate market clearing. Policy providers define the regulatory framework which then may impact on the decisions of the other agents. Demand agents request energy directly at the [[Engineering:Electricity market|electricity market]]. Finally, flexibility providers, such as storage operators, use forecasts to determine bidding patterns to match their particular objectives, for instance, projected [[Finance:Profit maximization|profit maximization]].

AMIRIS is based on the open Framework for distributed Agent-based Modelling of Energy systems or FAME.<ref name="fame-project">
{{cite web
 | title = Framework for distributed Agent-based Modelling of Energy systems (FAME)
 | access-date = 2022-02-22
 | date = 2022-03-30
 | url = https://gitlab.com/fame-framework
}}  Source code repository.
</ref><ref name="fame-core">{{cite journal |last1=Schimeczek |first1=Christoph |last2=Deissenroth-Uhrig |first2=Marc |last3=Frey |first3=Ulrich |last4=Fuchs |first4=Benjamin |last5=Ghazi |first5=A. Achraf El |last6=Wetzel |first6=Manuel |last7=Nienhaus |first7=Kristina |title=FAME-Core: An open Framework for distributed Agent-based Modelling of Energy systems |journal=Journal of Open Source Software |date=17 April 2023 |volume=8 |issue=84 |pages=5087 |doi=10.21105/joss.05087|bibcode=2023JOSS....8.5087S |s2cid=258195264 |doi-access=free }}</ref>

AMIRIS can simulate large{{nbhyph}}scale agent systems in acceptable timeframes. For instance, the simulation of one year at hourly resolution may take as little as one minute on a contemporary desktop computer. The researchers at DLR also have access to high-performance computing facilities.

{{clear}}

=== Breakthrough Energy Model ===

{| class="infobox" style="width: 28em"
|-
! style="width: 35%" | Project
| style="width: 55%" | Breakthrough Energy Model
|-
! Host
| Breakthrough Energy Foundation
|-
! Status
| active
|-
! Scope/type
| power sector modeling
|-
! Code license
| MIT
|-
! Data license
| [[Creative Commons license|CC{{nbhyph}}BY{{nbhyph}}4.0]]
|-
! Language
| [[Python (programming language)|Python]], [[Julia (programming language)|Julia]]
|-
! Website
| {{URL|https://science.breakthroughenergy.org/}}
|-
! Repository
| {{URL|https://github.com/Breakthrough-Energy}}
|-
! Documentation
| {{URL|https://breakthrough-energy.github.io/docs/index.html}}
|}

The Breakthrough Energy Model is a production cost model with capacity expansion algorithms and heuristics, originally designed to explore the generation and transmission expansion needs to meet U.S. states' clean energy goals. The data management occurs within [[Python (programming language)|Python]] and the [[Physics:Power system simulation#Optimal power flow|DCOPF]] optimization problem is created via [[Julia (programming language)|Julia]]. The Breakthrough Energy Model is being developed by the Breakthrough Energy Sciences team.

The open data underlying the model builds upon the synthetic test cases developed by researchers at Texas A&M University.<ref name="xu-etal-2020">
{{cite conference
 | last1 = Xu | first1 = Yixing
 | last2 = Myhrvold | first2 = Nathan
 | last3 = Sivam | first3 = Dhileep
 | last4 = Mueller | first4 = Kaspar
 | last5 = Olsen | first5 = Daniel J.
 | last6 = Xia | first6 = Bainan
 | last7 = Livengood | first7 = Daniel
 | last8 = Hunt | first8 = Victoria
 | last9 = Rouillé d'Orfeuil | first9 = Benjamin
 | last10 = Muldrew | first10 = Daniel
 | last11 = Ondreicka | first11 = Merrielle
 | last12 = Bettilyon | first12 = Megan
 | title = 2020 IEEE Power & Energy Society General Meeting (PESGM)
 | chapter = U.S. Test System with High Spatial and Temporal Resolution for Renewable Integration Studies
 | date = August 2020
 | pages = 1–5
 | doi = 10.1109/PESGM41954.2020.9281850
 | isbn = 978-1-7281-5508-1
 | issn = 1944-9933
}}
</ref><ref name="bem-data">
{{cite journal
 | title = U.S. Test System with High Spatial and Temporal Resolution for Renewable Integration Studies
 | journal = Breakthrough Energy Model Dataset
 | year = 2020
 | doi = 10.5281/zenodo.3530899
 | url = https://zenodo.org/record/3530899
 | access-date = 2021-07-01
 | author1 = Yixing Xu
 | last2 = Myhrvold
 | first2 = Nathan
 | author3 = Dhileep Sivam
 | last4 = Mueller
 | first4 = Kaspar
 | last5 = Olsen
 | first5 = Daniel J.
 | author6 = Bainan Xia
 | last7 = Livengood
 | first7 = Daniel
 | last8 = Hunt
 | first8 = Victoria
 | last9 = D'Orfeuil
 | first9 = Ben Rouille
 | last10 = Muldrew
 | first10 = Daniel
 | author11 = Merrielle Ondreicka
 | last12 = Bettilyon
 | first12 = Megan
 | arxiv = 2002.06155
 }}
</ref><ref name="egtcr">
{{cite web
 | title = Electric Grid Test Case Repository
 | work = Texas A&M University Electric Grid Datasets
 | url = https://electricgrids.engr.tamu.edu/
 | access-date = 2021-07-01
}}
</ref>

The Breakthrough Energy Model initially explored the generation and transmission expansion necessary to meet clean energy goals in 2030 via the building of a Macro Grid.<ref name="macro-grid-xu-etal-2021">
{{cite book
 | last1 = Xu | first1 = Yixing
 | last2 = Olsen | first2 = Daniel
 | last3 = Xia | first3 = Bainan
 | last4 = Livengood | first4 = Dan
 | last5 = Hunt | first5 = Victoria
 | last6 = Li | first6 = Yifan
 | last7 = Smith | first7 = Lane
 | title = A 2030 United States Macro Grid: Unlocking Geographical Diversity to Accomplish Clean Energy Goals
 | date = January 2021
 | publisher = Breakthrough Energy Sciences
 | location = Seattle, Washington, USA
 | url = https://science.breakthroughenergy.org/publications/MacroGridReport.pdf
 | access-date = 2021-07-01
}}</ref> Ongoing work adds and expands modules to the model (e.g. electrification of buildings and transportation) to provide a framework for testing numerous scenario combinations. Development of and integration with other open-source data sets is in progress for modeling countries and regions beyond the United States.

The model was applied subsequently the 2021 Texas power crisis, in which winter power outages resulted in hundreds of deaths and billions of dollars in economic losses.<ref name="wu-etal-2022">
{{cite journal
 | last1 = Wu | first1 = Dongqi
 | last2 = Zheng | first2 = Xiangtian
 | last3 = Menati | first3 = Ali
 | last4 = Smith | first4 = Lane
 | last5 = Xia | first5 = Bainan
 | last6 = Xu | first6 = Yixing
 | last7 = Singh | first7 = Chanan
 | last8 = Xie | first8 = Le
 | title = How much demand flexibility could have spared Texas from the 2021 outage?
 | date = September 2022
 | journal = Advances in Applied Energy
 | volume = 7
 | pages = 100106
 | doi = 10.1016/j.adapen.2022.100106
 | arxiv = 2206.00184
 | bibcode = 2022AdAE....700106W
 | issn = 2666-7924
 | url = https://linkinghub.elsevier.com/retrieve/pii/S2666792422000245
 | access-date = 2022-11-08
}}  {{open access}}
</ref>{{rp|1}}

{{clear}}

=== DIETER ===

{| class="infobox" style="width: 28em"
|-
! style="width: 35%" | Project
| style="width: 55%" | DIETER
|-
! Host
| [[Organization:German Institute for Economic Research|DIW Berlin]]
|-
! Status
| active
|-
! Scope/type
| dispatch and investment
|-
! Code license
| MIT
|-
! Data license
| MIT
|-
! Language
| [[Software:General Algebraic Modeling System|GAMS]]
|-
! Website
| {{URL|http://www.diw.de/dieter}}
|}

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 [[Engineering:Grid energy storage|power storage]] and other flexibility options in a future greenfield setting with high shares of renewable generation. DIETER is being developed at the [[Organization:German Institute for Economic Research|German Institute for Economic Research]] (DIW), [[Place:Berlin|Berlin]], Germany. The [[Codebase|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">
{{cite book
 | last1 = Zerrahn | first1 = Alexander
 | last2 = Schill | first2 = Wolf-Peter
 | title = A greenfield model to evaluate long-run power storage requirements for high shares of renewables — DIW discussion paper 1457
 | year = 2015
 | publisher = German Institute for Economic Research (DIW)
 | location = Berlin, Germany
 | issn = 1619-4535
 | url = http://www.diw.de/documents/publikationen/73/diw_01.c.498475.de/dp1457.pdf
 | access-date = 2016-07-07
}}
</ref><ref>{{Cite journal|last1=Zerrahn|first1=Alexander|last2=Schill|first2=Wolf-Peter|title=Long-run power storage requirements for high shares of renewables: review and a new model|journal=Renewable and Sustainable Energy Reviews|doi=10.1016/j.rser.2016.11.098|volume=79|year=2017|pages=1518–1534|bibcode=2017RSERv..79.1518Z }}</ref> DIETER is written in [[Software:General Algebraic Modeling System|GAMS]] and was developed using the [[Software:CPLEX|CPLEX]] commercial solver.

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 [[Finance:Energy demand management|DSM]] capacities in the German wholesale and balancing electricity markets. Later model extensions include [[Engineering:Vehicle-to-grid|vehicle-to-grid]] interactions and prosumage of solar electricity.<ref>{{Cite journal|last1=Schill|first1=Wolf-Peter|last2=Niemeyer|first2=Moritz|last3=Zerrahn|first3=Alexander|last4=Diekmann|first4=Jochen|s2cid=163807710|date=2016-06-01|title=Bereitstellung von Regelleistung durch Elektrofahrzeuge: Modellrechnungen für Deutschland im Jahr 2035|journal=Zeitschrift für Energiewirtschaft|language=de|volume=40|issue=2|pages=73–87|doi=10.1007/s12398-016-0174-7|issn=0343-5377|hdl=10419/165995|hdl-access=free}}</ref><ref>{{Cite journal|last1=Schill|first1=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|journal=Economics of Energy & Environmental Policy|language=en-US|volume=6|issue=1|doi=10.5547/2160-5890.6.1.wsch|issn=2160-5882|url=http://www.diw.de/documents/publikationen/73/diw_01.c.552031.de/dp1637.pdf}}</ref>

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), [[Engineering: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|last1=Schill|first1=Wolf-Peter|last2=Zerrahn|first2=Alexander|title=Long-run power storage requirements for high shares of renewables: Results and sensitivities|journal=Renewable and Sustainable Energy Reviews|volume=83|pages=156–171|doi=10.1016/j.rser.2017.05.205|year=2018|bibcode=2018RSERv..83..156S }}</ref>

{{clear}}

=== Dispa-SET ===

{| class="infobox" style="width: 28em"
|-
! style="width: 35%" | Project
| style="width: 55%" | Dispa-SET
|-
! Host
| EC Joint Research Centre
|-
! Status
| active
|-
! Scope/type
| European transmission and dispatch
|-
! Code license
| [[Software:European Union Public Licence|EUPL{{nbsp}}1.2]]
|-
! Data license
| [[Creative Commons license|CC{{nbhyph}}BY{{nbhyph}}4.0]]
|-
! Website
| {{URL|http://www.dispaset.eu}}
|-
! Repository
| {{URL|https://github.com/energy-modelling-toolkit/Dispa-SET}}
|-
! Documentation
| {{URL|http://www.dispaset.eu}}
|}

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 [[Software: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 [[Software:CPLEX|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
 | last3 = Zucker | first3 = Andreas
 | date = 2014
 | title = Dispa-SET 2.0: unit commitment and power dispatch model: description, formulation, and implementation — EUR 27015 EN
 | publisher = Publications Office of the European Union
 | location = Luxembourg
 | doi = 10.2790/399921
 | isbn = 978-92-79-44690-0
 | 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">
{{cite book
 | last1 = Quoilin | first1 = Sylvain
 | last2 = Hidalgo González | first2 = Ignacio
 | last3 = Zucker | first3 = Andreas
 | date = 2017
 | title = Modelling future EU power systems under high shares of renewables: the Dispa-SET 2.1 open-source model — EUR 28427 EN
 | publisher = Publications Office of the European Union
 | location = Luxembourg
 | doi = 10.2760/25400
 | isbn = 978-92-79-65265-3
 | url = http://publications.jrc.ec.europa.eu/repository/bitstream/JRC105452/dispaset2.1_technical_report.pdf
 | access-date = 2017-03-01
}}
</ref> {{nowrap|Dispa-SET}} is hosted on [[GitHub]], together with a trial dataset, and third-party contributions are encouraged. The [[Codebase|codebase]] has been tested on Windows, macOS, and Linux. Online documentation is available.<ref name="dispa-set-documentation">
{{cite web
 | title = Dispa-SET documentation
 | url = https://dispa-set.readthedocs.io/en/latest/
 | access-date = 2017-03-02
}}  Automatically the latest version.
</ref>

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">
{{cite journal
 | author = <!-- staff writer, no by-line -->
 | date = November 2016
 | title = SET-Plan Update
 | journal = SETIS Magazine
 | number = 13
 | pages = 5–7
 | 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>

[[File:Rolling horizon.png|thumb|left|48{{nbsp}}hour rolling horizon optimization for any given 24{{nbsp}}hour day]]

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 [[Finance:Elasticity (economics)|inelastic]]. The system is subject to intra-period and inter-period [[Finance:Unit commitment problem in electrical power production|unit commitment]] constraints (the latter covering nuclear and thermal generation for the most part) and operated under [[Physics:Economic dispatch|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}}

Two related publications describe the role and representation of flexibility measures within power systems facing ever greater shares of [[Engineering:Variable renewable energy|variable renewable energy]] (VRE).<ref name="hidalgo-gonzalez-etal-2015">
{{cite report
 | first1 = Ignacio | last1 = Hidalgo González
 | first2 = Pablo | last2 = Ruiz Castello
 | first3 = Alessandra | last3 = Sgobbi
 | first4 = Wouter | last4 = Nijs
 | first5 = Sylvain | last5 = Quoilin
 | first6 = Andreas | last6 = Zucker
 | first7 = Christian | last7 = Thiel
 | 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
 | access-date = 2017-03-02
}}  The DOI and ISBN refer to the online version.
</ref><ref name="quoilin-etal-2015">
{{cite conference
 | first1 = Sylvain | last1 = Quoilin
 | first2 = Wouter | last2 = Nijs
 | first3 = Ignacio | last3 = Hidalgo González
 | first4 = Andreas | last4 = Zucker
 | first5 = Christian | last5 = Thiel
 | date = 19 May 2015
 | title = Evaluation of simplified flexibility evaluation tools using a unit commitment model
 | journal = Energy Market, Eem, International Conference on the European
 | conference = 2015 12th International Conference on the European Energy Market (EEM)
 | pages = 1–5
 | doi = 10.1109/EEM.2015.7216757
 | isbn = 978-1-4673-6692-2
 | issn = 2165-4077
}}
</ref> These flexibility measures comprise: dispatchable generation (with constraints on efficiency, ramp rate, part load, and up and down times), conventional storage (predominantly [[Engineering:Pumped-storage hydroelectricity|pumped-storage hydro]]), cross-border interconnectors, [[Finance:Energy demand management|demand side management]], renewables curtailment, last resort load shedding, and nascent [[Engineering:Power-to-X|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 [[Physics:Ohm's law|Ohm's law]] applied to a resistor carrying DC current.<ref name="andersson-2008">{{cite book
 | last1 = Andersson
 | first1 = Göran
 | date = 2008
 | title = Modelling and analysis of electric power systems: power flow analysis fault analysis power systems dynamics and stability
 | publisher = ETH Zurich
 | location = Zürich, Switzerland
 | url = http://www.eeh.ee.ethz.ch/uploads/tx_ethstudies/modelling_hs08_script_02.pdf
 | access-date = 2017-02-02
 | archive-url = https://web.archive.org/web/20160303233609/http://www.eeh.ee.ethz.ch/uploads/tx_ethstudies/modelling_hs08_script_02.pdf
 | archive-date = 3 March 2016
 | url-status = dead
 }}</ref>{{rp|59}}  For the purposes of optimization, the quadratic loss function is also piecewise linearized.
}} (transmission is currently treated as a transportation problem), new constraints (like [[Physics:Water cooling|cooling water]] supply), [[Stochastic|stochastic]] scenarios, and the inclusion of markets for [[Engineering:Ancillary services (electric power)|ancillary{{nbsp}}services]].<ref name="dispa-set-documentation"/>

{{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">
<!-- alternative url: http://orbi.ulg.be/handle/2268/172402 -->
{{cite conference
 | 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
}}
</ref>

A 2020 study investigated the collective impact of future climatic conditions on 34 European power systems, including potential variations in solar, wind, and hydro‑power output and electricity demand under various projected meteorological scenarios for the European continent.<ref name="de-felice-etal-2020">
{{cite book
 | last1 = De Felice | first1 = Matteo
 | last2 = Busch | first2 = Sebastian
 | last3 = Kanellopoulos | first3 = Konstantinos
 | last4 = Kavvadias | first4 = Konstantinos
 | last5 = Hidalgo Gonzalez | first5 = Ignacio
 | title = Power system flexibility in a variable climate — EUR 30184 EN, JRC120338
 | date = April 2020
 | publisher = Publications Office of the European Union
 | location = Luxembourg
 | doi = 10.2760/75312
 | doi-access=free
 | isbn = 978-92-76-18183-5
}}
</ref>

{{nowrap|Dispa-SET}} has been applied in Africa with soft linking to the LISFLOOD model to examine water{{nbhyph}}energy nexus problems in the context of a changing climate.<ref name="pavicevic-etal-2021">
{{cite journal
 | last1 = Pavičević | first1 = Matija
 | last2 = De Felice | first2 = Matteo
 | last3 = Busch | first3 = Sebastian
 | last4 = Hidalgo González | first4 = Ignacio
 | last5 = Quoilin | first5 = Sylvain
 | title = Water-energy nexus in African power pools – the Dispa-SET Africa model
 | date = 1 August 2021
 | journal = Energy
 | volume = 228
 | pages = 120623
 | doi = 10.1016/j.energy.2021.120623
 | bibcode = 2021Ene...22820623P
 | hdl = 2268/288350
 | s2cid = 234814778
 | issn = 0360-5442
 | url = https://www.sciencedirect.com/science/article/pii/S0360544221008720
 | access-date = 2021-04-30
| hdl-access = free
 }}
</ref>

{{clear}}

=== EMLab-Generation ===

{| class="infobox" style="width: 28em"
|-
! style="width: 35%" | Project
| style="width: 55%" | EMLab-Generation
|-
! Host
| [[Organization:Delft University of Technology|Delft University of Technology]]
|-
! Status
| active
|-
! Scope/type
| agent-based
|-
! Code license
| [[Software:Apache License|Apache 2.0]]
|-
! Website
| {{URL|http://emlab.tudelft.nl/generation.html}}
|-
! Repository
| {{URL|https://github.com/EMLab/emlab-generation}}
|}

EMLab-Generation is an [[Agent-based model|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], [[Organization:Delft University of Technology|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
 | 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
 | first3 = Jörn C | last3 = Richstein
 | title = EMLab-Generation: an experimentation environment for electricity policy analysis — Project report — Version 1.2
 | date = August 2015
 | publisher = Energy Modelling Lab, Delft University of Technology
 | location = Delft, The Netherlands
 | url = http://emlab.tudelft.nl/generation/emlab-generation-report-1.2.pdf
 | access-date = 2016-07-09
}}
</ref> EMLab-Generation is written in [[Java (programming language)|Java]].

EMLab-Generation simulates the actions of [[Physics:Electric power industry|power companies]] investing in generation capacity and uses this to explore the long-term effects of various [[Earth:Energy policy|energy]] and [[Earth: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 [[Finance:Net present value|net present value]] (NPV) of prospective power plant projects. They can adopt a variety of technologies, using scenarios from the 2011 [[Organization: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.

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 [[Earth: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
 | doi-access = free
 | bibcode = 2014EnPol..71..139R
 }}
</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, [[Place:Berlin|Berlin]], Germany. The [[Source code|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 [[Software:General Algebraic Modeling System|GAMS]] and uses the [[Software:CPLEX|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 [[Finance:Partial equilibrium|partial equilibrium]] model of the wholesale [[Engineering:Electricity market|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 [[Physics:Cogeneration|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 = Technische Universität 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
| year = 2015
 }}
</ref>

EMMA has been used to study the economic effects of the increasing penetration of [[Engineering:Variable renewable energy|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
 | bibcode = 2013EneEc..38..218H
 | url = http://www.neon-energie.de/Hirth-2013-Market-Value-Renewables-Solar-Wind-Power-Variability-Price.pdf
 | access-date = 2016-07-09
| hdl = 1814/27135
 }}
</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
 | bibcode = 2015EnerJ..36..149H
 | 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
| [[Software:GNU Lesser General Public License|LGPLv2.1]]
|-
! Data license
| [[Software:GNU Lesser General Public License|LGPLv2.1]]
|-
! Language
| [[C++]]
|-
! 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|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 [[Software:Boost (C++ libraries)|Boost]] libraries, the [[Software:MySQL|MySQL]] relational database, the [[Software:Qt|Qt{{nbsp}}4]] application framework, and optionally the [[Software:CPLEX|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|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
 | doi-access = free
 | bibcode = 2014EnPro..46...40B
 }}
</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
 | bibcode = 2016JEnSt...6....1B
 | url = https://zenodo.org/record/902120
 }}
</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
| [[Software:GNU General Public License|GPLv3]]
|-
! Language
| [[Python (programming language)|Python]]
|-
! Website
| {{URL|https://nemo.ozlabs.org}}
|-
! Repository
| {{URL|https://github.com/bje-/NEMO}} <!-- was {{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 Centre for Energy and Environmental Markets (CEEM), University of New South Wales (UNSW), [[Place:Sydney|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 [[Engineering:Concentrated solar power|concentrated solar power]] (CSP) with thermal storage, windfarms, [[Chemistry:Photovoltaics|photovoltaics]], existing hydroelectricity, and [[Earth:Biofuel|biofuel]]led [[Engineering:Gas turbine|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
 | bibcode = 2012EnPol..45..606E
 | issn = 0301-4215
 | url = https://www.researchgate.net/publication/241756578
 | 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 [[Physics:Carbon capture and storage|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
 | bibcode = 2014REne...66..196E
 | 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">
{{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
 | bibcode = 2016REne...95..127E
 | 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
| KTH Royal Institute of Technology
|-
! Status
| active
|-
! Scope/type
| cost-effective electrification
|-
! Code license
| [[Software:MIT License|MIT]]
|-
! Website
| {{URL|http://www.onsset.org}}
|-
! Mailing list
| {{URL|https://groups.google.com/g/onsset}}
|-
! Repository
| {{URL|https://github.com/OnSSET/onsset}}
|-
! Documentation
| {{URL|https://onsset-manual.readthedocs.io}}
|-
! Datasets
| {{URL|https://energydata.info}}
|}

OnSSET is the OpeN Source Spatial Electrification Toolkit. OnSSET is being developed by the division of Energy Systems, KTH Royal Institute of Technology, [[Place:Stockholm|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' SDG{{nnbsp}}7: the provision of affordable, reliable, sustainable, and modern energy for all. The toolkit is known as OnSSET and was released on 26{{nbsp}}November 2016. OnSSET 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 runs a [https://groups.google.com/g/onsset mailing list].<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
 | archive-date = 13 March 2017
 | archive-url = https://web.archive.org/web/20170313214145/https://www.kth.se/en/itm/inst/energiteknik/forskning/desa/projects/sustainable-energy-f/open-source-spatial-electrification-toolkit-onsset-1.663655
 | url-status = dead
 }}</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
 | archive-date = 14 March 2017
 | archive-url = https://web.archive.org/web/20170314062641/http://presentations.copernicus.org/EGU2016-14161_presentation.pptx
 | url-status = dead
 }}</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 [[Engineering: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
 | bibcode = 2016Ene....95..255N
 | issn = 0360-5442
}}
</ref> The toolkit supports a range of conventional and renewable energy technologies, including photovoltaics, wind turbines, and [[Engineering:Small hydro|small hydro]] generation. {{as of|2017}}, [[Biology:Bioenergy|bioenergy]] and hybrid technologies, such as [[Chemistry: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 [[Software:ArcGIS|ArcGIS]] package or an open source equivalent such as [[Software:GRASS GIS|GRASS]] or [[Software:QGIS|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 applied to microgrids using a three‑tier analysis starting with settlement archetypes.<ref name="pena-balderrama-etal-2020">
{{cite journal
 | last1 = Peña Balderrama | first1 = JG
 | last2 = Balderrama Subieta | first2 = S
 | last3 = Lombardi | first3 = Francesco
 | last4 = Stevanato | first4 = N
 | last5 = Sahlberg | first5 = A
 | last6 = Howells | first6 = Mark
 | last7 = Colombo | first7 = E
 | last8 = Quoilin | first8 = Sylvain
 | title = Incorporating high-resolution demand and techno-economic optimization to evaluate micro-grids into the Open Source Spatial Electrification Tool (OnSSET)
 | date = 1 June 2020
 | journal = Energy for Sustainable Development
 | volume = 56
 | pages = 98–118
 | doi = 10.1016/j.esd.2020.02.009
 | issn = 0973-0826
 | doi-access = free
 | bibcode = 2020ESusD..56...98P
 | hdl = 10044/1/86932
 | hdl-access = free
 }}
</ref>

OnSSET has been used for case studies in Afghanistan,<ref name="korkovelos-etal-2017">
{{cite book
 | last1 = Korkovelos | first1 = Alexandros
 | last2 = Bazilian | first2 = Morgan
 | last3 = Mentis | first3 = Dimitrios
 | last4 = Howells | first4 = Mark
 | title = A GIS approach to planning electrification in Afghanistan
 | date = 2017
 | publisher = The World Bank
 | location = Washington DC, USA
}}
</ref> Bolivia,<ref name="pena-balderrama-etal-2020"/><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> Cameroon,<ref name="ouedraogo-2023">
{{cite journal
 | last1 = Ouedraogo | first1 = Nadia S
 | title = A GIS approach to electrification planning in Cameroon
 | date = 1 January 2023
 | journal = Energy Strategy Reviews
 | volume = 45
 | issue =
 | pages = 101020
 | doi = 10.1016/j.esr.2022.101020
 | s2cid = 254705677
 | issn = 2211-467X
 | url = https://www.sciencedirect.com/science/article/pii/S2211467X22002140/pdfft
 | access-date = 2022-12-24
| doi-access = free
 | bibcode = 2023EneSR..4501020O
 }}  {{open access}}
</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
 | bibcode = 2016AppGe..72....1M
 | issn = 0143-6228
| url = http://discovery.ucl.ac.uk/10049297/1/Broad%20180523_Mentis%20et%20al%202016_The-benefits-of-geospatial-planning-in-energy-access-Clear-NEW.pdf
 }}
</ref> Malawi,<ref name="korkovelos-etal-2019">
{{cite journal
 | last1 = Korkovelos | first1 = Alexandros
 | last2 = Khavari | first2 = Babak
 | last3 = Sahlberg | first3 = Andreas
 | last4 = Howells | first4 = Mark
 | last5 = Arderne | first5 = Christopher
 | title = The role of open access data in geospatial electrification planning and the achievement of SDG7: an OnSSET-based case study for Malawi
 | date = January 2019
 | journal = Energies
 | volume = 12
 | issue = 7
 | pages = 1395
 | doi = 10.3390/en12071395
 | issn = 1996-1073
 | doi-access = free
 | bibcode = 2019Energ..12.1395K
 }}  Dimitrios Mentis added as sixth author following original publication.
</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
 | bibcode = 2015ESusD..29..142M
 | issn = 0973-0826
}}
</ref><ref name="isihak-2023">
{{cite journal
 | last1 = Isihak | first1 = Salisu R
 | title = Achieving universal electricity access in line with SDG7 using GIS-based model: an application of OnSSET for rural electrification planning in Nigeria
 | date = 1 January 2023
 | journal = Energy Strategy Reviews
 | volume = 45
 | issue =
 | pages = 101021
 | doi = 10.1016/j.esr.2022.101021
 | s2cid = 254619906
 | issn = 2211-467X
 | url = https://www.sciencedirect.com/science/article/pii/S2211467X22002152/pdfft
 | access-date = 2022-12-24
| doi-access = free
 | bibcode = 2023EneSR..4501021I
 }}  {{open access}}
</ref> and Tanzania.<ref name="berndtsson-2016-msc"/> OnSSET has also been applied in [[Place:India|India]], Kenya, and Zimbabwe. In addition, continental studies have been carried out for [[Place:Sub-Saharan Africa|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 = https://un-desa-modelling.github.io/electrification-paths-presentation/
 | access-date = 2017-03-09
 | archive-date = 9 February 2018
 | archive-url = https://web.archive.org/web/20180209110641/http://un-desa-modelling.github.io/electrification-paths-presentation/
 | url-status = dead
 }}</ref> A{{nbsp}}4{{nbhyph}}way [[Geographic information system|GIS]]{{nbhyph}}based study set in Nigeria reported that OnSSET offered the best set of capabilities.<ref name="isihak-etal-2022">
{{cite journal
 | last1 = Isihak | first1 = Salisu
 | last2 = Akpan | first2 = Uduak
 | last3 = Bhattacharyya | first3 = Subhes
 | title = Evolution of GIS-based rural electrification planning models and an application of OnSSET in Nigeria — Official pre‑proof
 | date = 19 February 2022
 | journal = Renewable and Sustainable Energy Transition
 | volume =
 | issue =
 | pages = 100019
 | doi = 10.1016/j.rset.2022.100019
 | s2cid = 247004954
 | issn = 2667-095X
 | url = https://www.sciencedirect.com/science/article/pii/S2667095X22000034/pdfft
 | access-date = 2022-02-24
| doi-access = free
 }}  {{open access}}
</ref>

OnSSET results have contributed to the [[Organization: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,<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> 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
 | hdl = 11343/119617
 | 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> and the IEA ''Africa Energy Outlook'' report in 2019.<ref name="iea-2019">
{{cite book
 | author = International Energy Agency (IEA)
 | title = Africa energy outlook
 | date = 8 November 2019
 | publisher = IEA Publications
 | location = Paris, France
}}  No cost but registration required.
</ref> OnSSET also forms part of the nascent GEP platform.<ref name="gep-ongoing">
{{cite web
 | author = GEP
 | title = Global Electrification Platform Explorer
 | url = https://electrifynow.energydata.info
 | access-date = 2020-11-19
 }}
</ref>

{{clear}}

=== pandapower ===

{| class="infobox" style="width: 28em"
|-
! style="width: 35%" | Project
| style="width: 55%" | pandapower
|-
! Host
| {{plainlist|
* [[Organization:University of Kassel|University of Kassel]]
* Fraunhofer Institute IEE
}}
|-
! Status
| active
|-
! Scope/type
| automated power system analysis
|-
! Code license
| BSD-new
|-
! Website
| {{URL|http://www.pandapower.org}}
|-
! Repository
| {{URL|https://github.com/e2nIEE/pandapower}}
|-
! Python package
| {{URL|https://pypi.org/project/pandapower/}}
|-
! Documentation
| {{URL|https://pandapower.readthedocs.io}}
|-
! Discussion
| {{URL|https://forum.openmod.org/tag/pandapower}}
|}

pandapower is a power system analysis and optimization program being jointly developed by the Energy Management and Power System Operation research group, [[Organization:University of Kassel|University of Kassel]] and the Department for Distribution System Operation, Fraunhofer Institute for Energy Economics and Energy System Technology (IEE), both of Kassel, Germany. The codebase is hosted on [[GitHub]] and is also available as a [[Python Package Index|package]]. The project maintains a website, an [[Electronic mailing list|emailing list]], and online documentation. pandapower is written in [[Python (programming language)|Python]]. It uses the [[Software:Pandas|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 [[Physics:Power-flow study|power flow]]. Unlike some open source power system tools, pandapower does not depend on proprietary platforms like [[Software:MATLAB|MATLAB]].

pandapower supports the automated analysis and optimization of [[Engineering:Electric power distribution|distribution]] and 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 itself can be plotted, with or without geographical information, using the [[Software:Matplotlib|matplotlib]] and [[Company:Plotly|plotly]] libraries.

A 2016 publication 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 [[Chemistry:Photovoltaics|photovoltaics]] into existing distribution grids. The study concludes 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>

A 2018 paper describes the package and its design and provides an example case study. The article explains how users work with an element-based model (EBM) which is converted internally to a bus-branch model (BBM) for computation. The package supports power system simulation, optimal power flow calculations (cost information is required), state estimation (should the system characterization lacks fidelity), and [[Graph theory|graph]]-based network analysis. The case study shows how a few tens of lines of scripting can interface with pandapower to advance the design of a system subject to diverse operating requirements. The associated code is hosted on GitHub as [[Organization:Project Jupyter|jupyter notebooks]].<ref name="thurner-etal-2018">
{{cite journal
 | last1 = Thurner | first1 = Leon
 | last2 = Scheidler | first2 = Alexander
 | last3 = Schäfer | first3 = Florian
 | last4 = Menke | first4 = Jan-Hendrik
 | last5 = Dollichon | first5 = Julian
 | last6 = Meier | first6 = Friederike
 | last7 = Meinecke | first7 = Steffen
 | last8 = Braun | first8 = Martin
 | s2cid = 4917834
 | title = Pandapower: an open source python tool for convenient modeling, analysis and optimization of electric power systems
 | date = 2018
 | journal = IEEE Transactions on Power Systems
 | volume =33
 | issue =6
 | pages =6510–6521
 | doi = 10.1109/TPWRS.2018.2829021
 | issn = 0885-8950
 | arxiv = 1709.06743
 | bibcode = 2018ITPSy..33.6510T
 }}  The arXiv link given is for version{{nbsp}}3.
</ref>

{{as of|2018}}, BNetzA, the German network regulator, is using pandapower for automated grid analysis.<ref name="thurner-2018">
{{cite mailing list
 | last1 = Thurner | first1 = Leon
 | title = pandapower news: reference paper published / unbalanced calculations / BNetzA adopts pandapower
 | date = 4 May 2018
 | mailing-list = openmod-initiative
 | url = https://groups.google.com/forum/#!topic/openmod-initiative/FYDJXndCZ98
 | access-date = 2018-05-04
 | quote = We are especially proud to say that the German Federal Network Agency (Bundesnetzagentur) is also adopting pandapower for automated grid analysis.
}}
</ref> Energy research institutes in Germany are also following the development of pandapower.<ref name="degner-etal-2017">
{{cite book
 | last1 = Degner | first1 = Thomas
 | last2 = Rohrig | first2 = Kurt
 | last3 = Strauß | first3 = Philipp
 | last4 = Braun | first4 = Martin
 | last5 = Wurdinger | first5 = Kerstin
 | last6 = Korte | first6 = Klaas
 | title = Forschung für die Energiewende – Die Gestaltung des Energiesystems Beiträge zur FVEE-Jahrestagung 2016
 | trans-title = Research for the energiewende — the design of the energy system contributions to the FVEE Annual Conference 2016
 | language = German
 | date = 22 March 2017
 | chapter = Anforderungen an ein zukunftsfähiges Stromnetz
 | trans-chapter = Requirements for a sustainable power grid
 | pages = 88–95
 | publisher = Forschungsverbund Erneuerbare Energien (FVEE)
 | location = Berlin, Germany
 | url = http://www.fvee.de/fileadmin/publikationen/Themenhefte/th2016/th2016.pdf
 | access-date = 2018-05-04
}}
</ref>{{rp|90}}

{{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
| [[Software:Apache License|Apache 2.0]]
|-
! Website
| {{URL|https://flexiblepower.github.io}}
|-
! Repository
| {{URL|https://github.com/flexiblepower/powermatcher}}
|}

The PowerMatcher software implements a [[Engineering:Smart grid|smart grid]] coordination mechanism which balances [[Engineering:Distributed generation|distributed energy resources]] (DER) and flexible loads through autonomous [[Finance:Bidding|bidding]]. The project is managed by the Flexiblepower Alliance Network (FAN) in [[Place:Amsterdam|Amsterdam]], the Netherlands. The project maintains a website and the [[Source code|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 = [[Organization:Vrije Universiteit Amsterdam|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}}

=== Power TAC ===

{| class="infobox" style="width: 28em"
|-
! style="width: 35%" | Project
| style="width: 55%" | Power TAC
|-
! Host
| Erasmus Centre for Future Energy Business at the Rotterdam School of Management, [[Organization:Erasmus University Rotterdam|Erasmus University]]
|-
! Status
| active
|-
! Scope/type
| automated retail electricity trading simulation
|-
! Code license
| [[Software:Apache License|Apache 2.0]]
|-
! Website
| {{URL|https://powertac.org}}
|}

Power TAC stands for Power Trading Agent Competition. Power TAC is an [[Agent-based model|agent-based model]] simulating the performance of retail markets in an increasingly [[Finance:Prosumer|prosumer]]- and renewable-energy-influenced electricity landscape. The first version of the Power TAC project started in 2009, when the open source platform was released as an open-source multi-agent competitive gaming platform to simulate electricity retail market performance in smart grid scenarios. The inaugural annual tournament was held in Valencia, Spain in 2012.

Autonomous machine-learning trading agents, or 'brokers', compete directly with each other as profit-maximizing aggregators between wholesale markets and retail customers. Customer models represent households, small and large businesses, multi-residential buildings, wind parks, solar panel owners, electric vehicle owners, cold-storage warehouses, etc. Brokers aim at making profit through offering electricity tariffs to customers and trading electricity in the wholesale market, while carefully balancing supply and demand.

The competition is founded and orchestrated by Professors [[Biography:Wolfgang Ketter|Wolfgang Ketter]] and John Collins and the platform software is developed collaboratively by researchers at the Rotterdam School of Management, Erasmus University Centre for Future Energy Business, the Institute for Energy Economics at the University of Cologne, and the Computer Science department at the University of Minnesota. The platform uses a variety of real-world data about weather, market prices and aggregate demand, and customer behavior. Broker agents are developed by research teams around the world and entered in annual tournaments. Data from those tournaments are publicly available and can be used to assess agent performance and interactions. The platform exploits competitive benchmarking to facilitate research into, among other topics, tariff design in retail electricity markets, bidding strategies in wholesale electricity markets, performance of markets as penetration of sustainable energy resources or electric vehicles is ramped up or down, effectiveness of machine learning approaches, and alternative policy approaches to market regulation. The software has contributed to research topics ranging from the use of electric vehicle fleets as virtual power plants to how an electricity customer decision support system (DSS) can be used to design effective demand response programs using methods such as dynamic pricing.

{{clear}}

=== renpass ===

{| class="infobox" style="width: 28em"
|-
! style="width: 35%" | Project
| style="width: 55%" | renpass
|-
! Host
| [[Organization:University of Flensburg|University of Flensburg]]
|-
! Status
| inactive
|-
! Scope/type
| renewables pathways
|-
! Code license
| [[Software:GNU General Public License|GPLv3]]
|-
! Website
| {{URL|https://github.com/fraukewiese/renpass}}
<!-- alternative: [https://www.uni-flensburg.de/index.php?id=18227] -->
|-
! Repository
| {{URL|https://github.com/znes/renpass}}
|}

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 Centre for Sustainable Energy Systems (CSES or ZNES), [[Organization:University of Flensburg|University of Flensburg]], Germany. The project runs a website, from where the [[Codebase|codebase]] can be download. renpass is written in [[R (programming language)|R]] and links to a [[Software:MySQL|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.

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 [[Place:Baltic Sea|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 [[Engineering: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 ''[[Earth:Energiewende|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
 | journal = Wiley Interdisciplinary Reviews: Energy and Environment
 | volume = 3
 | number = 5
 | pages = 490–504
 | issn = 2041-840X
 | doi = 10.1002/wene.109
| bibcode = 2014WIREE...3..490W
 | s2cid = 108676376
 }}
</ref>

{{clear}}

=== SciGRID ===

{| class="infobox" style="width: 28em"
|-
! style="width: 35%" | Project
| style="width: 55%" | SciGRID
|-
! Host
| Deutsches Zentrum für Luft- und Raumfahrt
|-
! Status
| active
|-
! Scope/type
| European transmission grid
|-
! Code license
| [[Software: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 [[Finance:Electric power transmission|electricity transmission networks]]. The research project is managed by [[Organization:German Aerospace Center|DLR]] Institute of Networked Energy Systems located in Oldenburg, Germany. The project maintains a website and an email newsletter. SciGRID is written in [[Python (programming language)|Python]] and uses a [[Software:PostgreSQL|PostgreSQL]] database. The first release (v0.1) was made on 15{{nbsp}}June 2015.

SciGRID aims to rectify the lack of [[Open research|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 [[Software:Open Database License|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
 | archive-date = 8 August 2016
 | archive-url = https://web.archive.org/web/20160808070740/http://www.scigrid.de/publications/15_dresden_poster.pdf
 | url-status = dead
 }}</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, [[Organization:University of Groningen|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
 | archive-date = 27 February 2017
 | archive-url = https://web.archive.org/web/20170227013508/http://scigrid.de/publications/16_1_BWiegmans_Master_Thesis_2015.pdf
 | url-status = dead
 }}</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 [[Social:Non-governmental organization|NGO]] based in Perth, Australia. The project maintains a website. SIREN runs on Windows and the [[Source code|source code]] is hosted on [[Software:SourceForge|SourceForge]]. The software is written in Python and uses the SAM model (System Advisor Model) from the US [[Organization:National Renewable Energy Laboratory|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 [[Organization:NASA|NASA]] MERRA-2 satellite data.{{efn|name=merra-2|
MERRA-2 stands for Modern-Era Retrospective analysis for Research and Applications, Version 2.  The [[Earth:Remote sensing|remote-sensed]] data is provided unencumbered by the [[Organization:NASA|NASA]] [[Organization:Goddard Space Flight Center|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 [[Finance: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
 | access-date = 2017-12-05
}}
</ref>

{{clear}}

===SWITCH===
{| class="infobox" style="width: 28em"
|-
! style="width: 35%" | Project
| style="width: 55%" | SWITCH
|-
! Host
| [[Organization:University of Hawaiʻi|University of Hawaiʻi]]
|-
! Status
| active
|-
! Scope/type
| optimal planning
|-
! Code license
| [[Software: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, [[Organization:University of Hawaiʻi at Mānoa|University of Hawaiʻi at Mānoa]], Hawaii, USA. The project runs a small website and hosts its [[Codebase|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 [[Software:CPLEX|CPLEX]] solver.

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 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 [[Engineering: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|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 [[Engineering:Electric vehicle|electric vehicle]]s) could achieve radical emission reductions at moderate cost.<ref name="fripp-2012">
<!-- alternative URL: https://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
 | pmid = 22506835
 | issn = 0013-936X
 | url = http://www2.hawaii.edu/~mfripp/papers/Fripp_2012_Switch_Calif_Renewables.pdf
 | access-date = 2016-07-11
| bibcode = 2012EnST...46.6371F
 | citeseerx = 10.1.1.469.9527
 }}
</ref>

SWITCH was used more recently to underpin consensus-based power system planning in Hawaii.<ref name="fripp-2016">
{{cite book
 | last1 = Fripp | first1 = Matthias
 | title = Consensus-based power system planning using open assumptions and models — Presentation
 | date = 29 June 2016
 | publisher = University of Hawaiʻi
 | location = Manoa, Hawaii, USA
 | url = http://ee.hawaii.edu/~mfripp/talks/Fripp_2016-06-29_HECO_Consensus.pdf
 | access-date = 2019-01-31
}}
</ref> The model is also being applied in Chile, Mexico, and elsewhere.<ref name="johnston-etal-2018"/>

Major version{{nbsp}}2.0 was released in late{{nbhyph}}2018.<ref name="johnston-etal-2018">
{{cite journal
 | last1 = Johnston | first1 = Josiah
 | last2 = Henríquez | first2 = Rodrigo
 | last3 = Maluenda | first3 = Benjamín
 | last4 = Fripp | first4 = Matthias
 | s2cid = 51783016
 | title = Switch 2.0: a modern platform for planning high-renewable power systems
 | journal =SoftwareX
 | volume =10
 | issue =
 | pages =100251
 | arxiv = 1804.05481
 | doi =10.1016/j.softx.2019.100251
 | issn =
 | year = 2019
 | bibcode = 2019SoftX..1000251J
 }}  arXiv preprint v3.  The release date for 2.0.0 was 1{{nbsp}}August 2018 under GitHub commit fc19cfe.
</ref> An investigation that year favorably compared SWITCH with the proprietary [[Company:General Electric|General Electric]] MAPS model using Hawaii as a case study.<ref name="fripp-2018">
{{cite journal
 | last1 = Fripp | first1 = Matthias
 | s2cid = 53070135
 | title = Intercomparison between Switch 2.0 and GE MAPS models for simulation of high-renewable power systems in Hawaii
 | date = 27 December 2018
 | journal = Energy, Sustainability and Society
 | volume = 8
 | issue = 1
 | pages = 41
 | doi = 10.1186/s13705-018-0184-x
 | issn = 2192-0567
 | doi-access = free
 | bibcode = 2018ESusS...8...41F
 }}
</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
| [[Software:GNU General Public License|GPLv3]]
|-
! Repository
| {{URL|https://github.com/tum-ens/urbs}}
|}

URBS, [[Social:Latin|Latin]] for city, is a [[Linear programming|linear programming]] model for exploring capacity expansion and unit commitment problems and is particularly suited to [[Engineering: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|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 [[Finance:Electric power transmission|transmission grid]] using projected wind and solar capacities for 2020. A 2012 study, using high spatial and technological resolutions, found [[Engineering:Variable renewable energy|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
| bibcode = 2012EnPol..43..123S
 | hdl = 11858/00-001M-0000-0026-E54A-9
 | url = http://mediatum.ub.tum.de/node?id=1107775
 | hdl-access = free
 }}
</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}}

==Energy system models==
Open energy-system models capture some or all of the energy commodities found in an energy system. Typically models of the electricity sector are always included. Some models add the heat sector, which can be important for countries with significant [[Physics:District heating|district heating]]. Other models add gas networks. With the advent of [[Engineering:Electric vehicle|emobility]], other models still include aspects of the transport sector. Indeed, coupling these various sectors using [[Engineering:Power-to-X|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, in addition to electricity)</span>
|-
! Project
! Host
! License
! Access
! Coding
! Documentation
! Scope/type
|-
| AnyMOD.jl
| Technische Universität Berlin
| MIT
| [[GitHub]]
| [[Julia (programming language)|Julia]]
| website
| system planning framework
|-
| Backbone
| VTT
| [[Software:GNU Lesser General Public License|LGPLv3]]
| [[Software:GitLab|GitLab]]
| [[Software:General Algebraic Modeling System|GAMS]]
| website
| dispatch, investment, all sectors, LP/MILP
|-
| Balmorel
| Denmark
| [[ISC license|ISC]]
| registration
| [[Software:General Algebraic Modeling System|GAMS]]
| manual
| energy markets
|-
| Calliope
| [[Organization:ETH Zurich|ETH Zurich]]
| [[Software:Apache License|Apache 2.0]]
| download
| [[Python (programming language)|Python]]
| manual, website, list
| dispatch and investment
|-
| DESSTinEE
| [[Organization:Imperial College London|Imperial College London]]
| [[Creative Commons license|{{nowrap|CC BY-SA 3.0}}]]
| download
| [[Software:Microsoft Excel|Excel]]/[[Visual Basic for Applications|VBA]]
| website
| simulation
|-
| Energy Transition Model
| Quintel Intelligence
| MIT
| [[GitHub]]
| [[Ruby (programming language)|Ruby]] (on [[Software:Ruby on Rails|Rails]])
| website
| web-based
|-
| EnergyPATHWAYS
| Evolved Energy Research
| MIT
| [[GitHub]]
| Python
| website
| mostly simulation
|-
| ETEM
| ORDECSYS, Switzerland
| [[Software:Eclipse Public License|Eclipse 1.0]]
| registration
| MathProg
| manual
| municipal
|-
| ficus
| Technical University of Munich
| [[Software:GNU General Public License|GPLv3]]
| [[GitHub]]
| [[Python (programming language)|Python]]
| manual
| local electricity and heat
|-
| GENeSYS-MOD
| Technische Universität Berlin
| Apache-2.0
| [[GitHub]]
| {{plainlist|
* [[Software:General Algebraic Modeling System|GAMS]]
* [[Julia (programming language)|Julia]]
}}
| GitHub
| multi{{nbhyph}}commodity optimization
|-
| GenX
| [[Organization:Massachusetts Institute of Technology|MIT]] and [[Organization:Princeton University|Princeton University]]
| [[Software:GNU General Public License|GPLv2]]
| [[GitHub]]
| [[Julia (programming language)|Julia]]
| website
| multi{{nbhyph}}commodity sector investment planning
|-
| oemof
| oemof community supported by {{plainlist|
* Reiner Lemoine Institute
* [[Organization:University of Flensburg|University of Flensburg]]
* Flensburg University of Applied Sciences
}}
| MIT
| [[GitHub]]
| [[Python (programming language)|Python]]
| website
| dispatch, investment, all sectors, LP/MILP
|-
| OSeMOSYS
| OSeMOSYS community
| [[Software:Apache License|Apache 2.0]]
| [[GitHub]]
| {{plainlist|
* [[Software:General Algebraic Modeling System|GAMS]]
* MathProg
* [[Python (programming language)|Python]]
}}
| website, forum
| planning at all scales
|-
| PyPSA
| [[Organization:Goethe University Frankfurt|Goethe University Frankfurt]]
| MIT
| [[GitHub]]
| [[Python (programming language)|Python]]
| website
| electric power systems with sector coupling
|-
| REMix
| Deutsches Zentrum für Luft- und Raumfahrt e.V.
| BSD 3-Clause
| [[Software:GitLab|GitLab]]
| [[Python (programming language)|Python]]/[[Software:General Algebraic Modeling System|GAMS]]
| website
| dispatch, investment, all sectors, LP/MILP
|-
| TEMOA
| [[Organization:North Carolina State University|North Carolina State University]]
| [[Software: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.
}}
|}

=== AnyMOD.jl ===

{| class="infobox" style="width: 28em"
|-
! style="width: 35%" | Project
| style="width: 55%" | AnyMOD.jl
|-
! Host
| Technische Universität Berlin
|-
! Status
| active
|-
! Scope/type
| energy system planning
|-
! Code license
| MIT
|-
! Language
| [[Julia (programming language)|Julia]]
|-
! Website
| {{URL| https://github.com/leonardgoeke/AnyMOD.jl}}
|-
! Documentation
| {{URL|https://leonardgoeke.github.io/AnyMOD.jl/stable/}}
|-
! Publications
| {{URL|https://www.researchgate.net/project/AnyMODjl-Methods-and-applications}}
|}

[[File:AnyMOD sankey diagram.svg|thumb|left|Exemplary [[Sankey diagram]] visualizing energy flows for France in 2040 computed by AnyMOD.jl as part of a study on the European Green Deal{{nnbsp}}<ref name="hainsch-etal-2020"/>{{rp}}<ref name="goeke-2020b"/>{{rp}}]]

AnyMOD.jl is a framework for planning macro{{nbhyph}}energy systems at a high level of spatio-temporal detail. The framework covers the expansion and operation of short-term and seasonal storage, fossil and renewable generation, transmission infrastructure, and [[Engineering:Power-to-X|sector coupling]] technologies. It can be used to plan long{{nbhyph}}term pathways under perfect foresight.

AnyMOD.jl is implemented in [[Julia (programming language)|Julia]] and relies on the [[JuMP]] library for optimization and DataFrames.jl for data management. Models are formulated as linear optimization problems and can be solved with open-source libraries like [[Organization:HiGHS optimization solver|HiGHS]] or commercial solvers like [[Software:CPLEX|CPLEX]]. To increase accessibility and enable [[Version control|version-controlled]] development, specific models are fully defined using [[Comma-separated values|CSV]] files.

Compared to similar tools, AnyMOD.jl puts an emphasis on innovative methods to achieve high detail and capture [[Engineering:Variable renewable energy|intermittent renewables]], while maintaining a comprehensive scope in terms of regions and sectors. These methods include varying the spatio-temporal resolution by energy carrier within the same model and a scaling algorithm to improve the properties of the underlying optimization problem.<ref name="goeke-2020a">
{{cite journal
 | last1 = Göke | first1 = Leonard
 | title = A graph-based formulation for modeling macro-energy systems
 | date = 1 November 2021
 | journal = Applied Energy
 | volume = 301
 | issue =
 | pages = 117377
 | doi = 10.1016/j.apenergy.2021.117377
 | arxiv = 2004.10184
 | bibcode = 2021ApEn..30117377G
 | s2cid = 236976140
 | issn = 0306-2619
 | url = https://www.sciencedirect.com/science/article/abs/pii/S0306261921007807
 | access-date = 2022-08-16}}
</ref><ref name="goeke-2020b">
{{cite journal
 | last1 = Göke | first1 = Leonard
 | title = AnyMOD.jl: A Julia package for creating energy system models
 | date = December 2021
 | journal = SoftwareX
 | volume = 16
 | issue =
 | pages = 100871
 | doi = 10.1016/j.softx.2021.100871
 | arxiv = 2011.00895
 | bibcode = 2021SoftX..1600871G
 | s2cid = 226283383
 | issn = 2352-7110
 | url = https://www.sciencedirect.com/science/article/pii/S2352711021001382
 | access-date = 2022-08-16}} {{open access}}
</ref> Methods from [[Stochastic programming|stochastic programming]] are now being implemented to better address the uncertainties associated with renewable generation.<ref name="goeke-etal-2022a">
{{cite journal
 | last1 = Göke | first1 = Leonard
 | last2 = Kendziorski | first2 = Mario
 | last3 = Schmidt | first3 = Felix
 | title = Stabilized Benders decomposition for energy planning under climate uncertainty
 | journal = European Journal of Operational Research
 | date = 2024
 | volume = 316
 | pages = 183–199
 | doi = 10.1016/j.ejor.2024.01.016
 | arxiv = 2208.07078}}</ref>

{{as of|2022|post=,}} most studies deploying the tool have focused on the German energy system in a European context, for instance investigating the trade{{nbhyph}}offs between centralized and decentralized designs, the role of grid planning, and the potential of sufficiency measures.<ref name="kendziorski-etal-2022">
{{cite journal
 | last1 = Kendziorski | first1 = Mario
 | last2 = Göke | first2 = Leonard
 | last3 = von Hirschhausen | first3 = Christian
 | last4 = Kemfert | first4 = Claudia
 | last5 = Zozmann | first5 = Elmar
 | title = Centralized and decentral approaches to succeed the 100% energiewende in Germany in the European context {{mdash}} A model-based analysis of generation, network, and storage investments
 | date = August 2022
 | journal = Energy Policy
 | volume = 167
 | issue =
 | pages = 113039
 | doi = 10.1016/j.enpol.2022.113039
 | arxiv = 2205.09066
 | bibcode = 2022EnPol.16713039K
 | s2cid = 248863407
 | issn = 0301-4215
 | url = https://www.sciencedirect.com/science/article/abs/pii/S0301421522002646
 | access-date = 2022-08-16}}
</ref><ref name="goeke-etal-2022b">
{{cite journal
 | last1 = Göke | first1 = Leonard
 | last2 = Kendziorski | first2 = Mario
 | last3 = Kemfert | first3 = Claudia
 | last4 = von Hirschhausen | first4 = Christian
 | title = Accounting for spatiality of renewables and storage in transmission planning
 | date = September 2022
 | journal = Energy Economics
 | volume = 113
 | issue =
 | pages = 106190
 | doi = 10.1016/j.eneco.2022.106190
 | arxiv = 2108.04863
 | bibcode = 2022EneEc.11306190G
 | s2cid = 236976304
 | issn = 0140-9883
 | url = https://www.sciencedirect.com/science/article/pii/S0140988322003401
 | access-date = 2022-08-16}}
</ref><ref name="eerma-etal-2022">
{{cite journal
 | last1 = Eerma | first1 = MH
 | last2 = Manning | first2 = D
 | last3 = Økland | first3 = GL
 | last4 = Rodriguez del Angel | first4 = C
 | last5 = Seifert | first5 = PE
 | last6 = Winkler | first6 = J
 | last7 = Zamora Blaumann | first7 = A
 | last8 = Zozmann | first8 = E
 | last9 = Hosseinioun | first9 = SS
 | last10 = Göke | first10 = L
 | last11 = Kendziorski | first11 = M
 | last12 = von Hirschhausen | first12 = C
 | title = The potential of behavioral changes to achieve a fully renewable energy system {{mdash}} A case study for Germany
 | date = August 2022
 | journal = Renewable and Sustainable Energy Transition
 | volume = 2
 | issue =
 | pages = 100028
 | doi = 10.1016/j.rset.2022.100028
 | s2cid = 250637854
 | issn = 2667-095X
 | doi-access = free
 | hdl = 11250/3053144
 | hdl-access = free
 }} {{open access}}
</ref> In addition, AnyMOD.jl has been used to support policy reports from the [[Organization:German Institute for Economic Research|German Institute for Economic Research]] (DIW) on the [[Earth:European Green Deal|European Green Deal]] and the coordination of the German [[Earth:Energiewende|Energiewende]].<ref name="hainsch-etal-2020">
{{cite book
 | last1 = Hainsch | first1 = Karlo
 | last2 = Göke | first2 = Leonard
 | last3 = Kemfert | first3 = Claudia
 | last4 = Oei | first4 = Pao-Yu
 | last5 = von Hirschhausen | first5 = Christian
 | title = European Green Deal: Using ambitious climate targets and renewable energy to climb out of the economic crisis {{mdash}} DIW Weekly Report{{nbsp}}9
 | year = 2020
 | publisher = German Institute for Economic Research (DIW)
 | location = Berlin, Germany
 | issn = 2667-095X
 | url = https://www.diw.de/documents/publikationen/73/diw_01.c.793352.de/dwr-20-28-1.pdf
 | access-date = 2022-08-16}}
</ref><ref name="goeke-etal-2021">
{{cite book
 | last1 = Göke | first1 = Leonard
 | last2 = Kemfert | first2 = Claudia
 | last3 = Kendziorski | first3 = Mario
 | last4 = von Hirschhausen | first4 = Christian
 | title = 100% renewable energy for Germany: Coordinated expansion planning needed {{mdash}} DIW Weekly Report{{nbsp}}11
 | year = 2021
 | publisher = German Institute for Economic Research (DIW)
 | location = Berlin, Germany
 | issn = 2568-7697
 | url = https://www.diw.de/documents/publikationen/73/diw_01.c.822478.de/dwr-21-29-1.pdf
 | access-date = 2022-08-16}}
</ref>

{{clear}}

===Backbone===
{| class="infobox" style="width: 28em"
|-
! style="width: 35%" | Project
| style="width: 55%" | Backbone
|-
! Host
| VTT
|-
! Status
| active
|-
! Scope/type
| framework - dispatch, investment, all sectors, LP/MILP
|-
! Code license
| [[Software:GNU Lesser General Public License|LGPLv3]]
|-
! Language
| [[Software:General Algebraic Modeling System|GAMS]]
|-
! Website
| {{URL|https://gitlab.vtt.fi/backbone/backbone/-/wikis/home}}
|-
! Repository
| {{URL|https://gitlab.vtt.fi/backbone/backbone}}
|-
! Documentation
| {{URL|https://gitlab.vtt.fi/backbone/backbone/-/wikis/home}}
|}

Backbone is an energy system modeling framework that allows for a high level of detail and adaptability. It has been used to study city-level energy systems as well as multi-country energy systems. It was originally developed during 2015{{endash}}2018 in an Academy of Finland{{nbhyph}}funded project 'VaGe' by the Design and Operation of Energy Systems team at VTT. It has been further developed in a collaboration which includes VTT, UCD, and [[Organization:Ruhr University Bochum|RUB]].

The framework is agnostic about what is modeled, but still has capabilities to represent a large range of energy system characteristics {{emdash}} such as generation and transfer, reserves, unit commitment, heat diffusion in buildings, storages, multiple emissions and P2X, etc. It offers linear and mixed integer constraints for capturing things like unit start-ups and investment decisions. It allows the modeler to change the temporal resolution of the model between time steps. {{emdash}} and this enables, for example, to use a coarser time resolution further ahead in the time horizon of the model. The model can be solved as an investment model (single or multi-period, myopic, or full foresight) or as a rolling production cost unit commitment model to simulate operations.<ref name="helisto-etal-2019">
{{cite journal
 | first1 = Niina | last1 = Helistö
 | first2 = Juha | last2 = Kiviluoma
 | first3 = Jussi | last3 = Ikäheimo
 | first4 = Topi | last4 = Rasku
 | first5 = Erkka | last5 = Rinne
 | first6 = Ciara | last6 = O'Dwyer
 | first7 = Ran | last7 = Li
 | first8 = Damian | last8 = Flynn
 | title = Backbone—An Adaptable Energy Systems Modelling Framework
 | date = 2019
 | journal = Energies
 | volume = 12
 | number = 17
 | pages = 3388
 | doi = 10.3390/en12173388
 | doi-access = free}}</ref>
{{clear}}

Backbone's own wiki page has a tutorial for new users, example models, and user created mods. Open datasets include Northern European model for electricity, heat, and hydrogen{{nnbsp}}<ref name="NE-Backbone">
{{cite book
 | first1 = Jussi | last1 = Ikäheimo
 | first2 = Anu | last2 = Purhonen
 | first3 = Tomi J. | last3 = Lindroos
 | first4 = Eric | last4 = Harrison
 | title = North European Backbone model
 | url = https://github.com/vttresearch/north_european_model
 | access-date = 2023-11-14}}
</ref> and district heating and cooling model for the Finnish capital region.<ref name="Helsinki-DHC-model">
{{cite book
 | first1 = Tomi J. | last1 = Lindroos
 | first2 = Esa | last2 = Pursiheimo
 | title = Helsinki region DHC model
 | url = https://gitlab.vtt.fi/backbone/models/helsinki-dhc-model
 | access-date = 2023-11-14}}
</ref>

===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|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
 | first1 = 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 [[Software:General Algebraic Modeling System|GAMS]].

The original aim of the Balmorel project was to construct a [[Finance:Partial equilibrium|partial equilibrium]] model of the electricity and [[Physics:Cogeneration|CHP]] sectors in the [[Place:Baltic Sea|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
| bibcode = 2008IJHE...33.1777K
 | s2cid = 93243823}}
</ref> A 2010 study uses Balmorel to examine the integration of [[Engineering: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| bibcode = 2010EnPol..38.5482G
 }}
</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
| bibcode = 2013WiEn...16..587G| doi-access = free
 }}</ref>

{{clear}}

===Calliope===
{| class="infobox" style="width: 28em"
|-
! style="width: 35%" | Project
| style="width: 55%" | Calliope
|-
! Host
| [[Organization:ETH Zurich|ETH Zurich]], TU Delft
|-
! Status
| active
|-
! Scope/type
| dispatch and investment
|-
! Code license
| [[Software:Apache License|Apache 2.0]]
|-
! Language
| [[Python (programming language)|Python]]
|-
! 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|https://calliope.readthedocs.io}}
|}

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], [[Organization:ETH Zurich|ETH Zurich]], [[Place:Zürich|Zürich]], Switzerland. The project maintains a website, hosts the [[Codebase|codebase]] at [[GitHub]], operates an 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 [[Software:CPLEX|CPLEX]] solver. 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>
And a two{{nbhyph}}page software review is available.<ref name="pfenninger-and-pickering-2018">
{{cite journal
 | last1 = Pfenninger | first1 = Stefan
 | last2 = Pickering | first2 = Bryn
 | title = Calliope: a multi-scale energy systems modelling framework
 | date = 12 September 2018
 | journal = Journal of Open Source Software
 | volume = 3
 | issue = 29
 | pages = 825
 | doi = 10.21105/joss.00825
 | bibcode = 2018JOSS....3..825P
 | issn = 2475-9066
 | doi-access = free
 }}
</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 [[Software:Pandas|pandas]] [[Data structures|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 [[Physics:Nuclear power|nuclear power]] and [[Engineering: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| bibcode = 2015Ene....87..303P
 }}
</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 [[Physics:Carbon capture and storage|carbon capture and storage]] (CCS). The scenarios are assessed on financial cost, emissions reductions, and energy security. Up to 60% of [[Engineering:Variable renewable energy|variable renewable]] capacity is possible with little increase in cost, while higher shares require large-scale [[Engineering:Grid energy storage|storage]], imports, and/or [[Physics:Dispatchable generation|dispatchable]] renewables such as [[Engineering: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
 | doi-access = free| bibcode = 2015ApEn..152...83P
 | hdl = 20.500.11850/105689
 | hdl-access = free
 }}</ref>

Calliope co{{nbhyph}}developer Stefan Pfenninger discusses the role that energy system models can play in supporting real{{nbhyph}}world decisions at a seminar held in mid{{nbhyph}}2021.<ref name="pfenninger-2021">
{{cite AV media
 | last1 = Pfenninger | first1 = Stefan
 | title = Model-based decision-support for the energy transition with Calliope
 | date = 22 April 2021
 | publisher = TU Delft
 | location = Delft, The Netherlands
 | url = https://www.youtube.com/watch?v=vTXtcNN5NGk
 | access-date = 2022-04-15}} YouTube. 00:29:23. Summer 2021 Webinar Series of Newcastle University Whole Energy Systems Interest Group (NUWIG).
</ref> One study cited investigates the consequences of pursuing energy self{{nbhyph}}sufficiency by duly adding increasingly restrictive internal constraints.<ref name="troendle-etal-2020">
{{cite journal
 | last1 = Tröndle | first1 = Tim
 | last2 = Lilliestam | first2 = Johan
 | last3 = Marelli | first3 = Stefano
 | last4 = Pfenninger | first4 = Stefan
 | title = Trade-offs between geographic scale, cost, and infrastructure requirements for fully renewable electricity in Europe
 | date = 16 September 2020
 | journal = Joule
 | volume = 4
 | issue = 9
 | pages = 1929–1948
 | doi = 10.1016/j.joule.2020.07.018
 | pmid = 32999994
 | pmc = 7498190
 | bibcode = 2020Joule...4.1929T
 | issn = 2542-4785
 | url = https://www.sciencedirect.com/science/article/pii/S2542435120303366/pdfft
 | access-date = 2021-04-25}} {{open access}}
</ref> Another at near optimal solutions for Italy.<ref name="lombardi-etal-2020">
{{cite journal
 | last1 = Lombardi | first1 = Francesco
 | last2 = Pickering | first2 = Bryn
 | last3 = Colombo | first3 = Emanuela
 | last4 = Pfenninger | first4 = Stefan
 | title = Policy decision support for renewables deployment through spatially explicit practically optimal alternatives
 | date = 14 October 2020
 | journal = Joule
 | volume = 4
 | issue = 10
 | pages = 2185–2207
 | doi = 10.1016/j.joule.2020.08.002
 | s2cid = 221739436
 | issn = 2542-4785| doi-access = free
 | bibcode = 2020Joule...4.2185L
 }} {{closed access}}
</ref> A{{nbsp}}2023 video describes recent developments, many of which are designed to benefit users.<ref name="sanvito-2023">
{{cite AV media
 | last1 = Sanvito | first1 = Francesco
 | title = Calliope{{nbsp}}v0.7.0
 | date = 24 March 2023
 | publisher = Open Energy Modelling Initiative
 | url = https://www.youtube.com/watch?v=9dYoo1AzjLM
 | access-date = 2023-08-03
}}  YouTube.  00:04:51 duration.  {{open access}}
</ref>

{{clear}}

===DESSTinEE===
{| class="infobox" style="width: 28em"
|-
! style="width: 35%" | Project
| style="width: 55%" | DESSTinEE
|-
! Host
| [[Organization:Imperial College London|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, [[Organization:Imperial College London|Imperial College London]] (ICL), [[Place:London|London]], United Kingdom. The software can be downloaded from the project website. DESSTinEE is written in [[Software:Microsoft Excel|Excel]]/[[Visual Basic for Applications|VBA]] and comprises a set of standalone [[Engineering:Spreadsheet|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 [[Finance: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}} 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 [[Physics:Heat pump|heat pump]]s and [[Engineering:Electric vehicle|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
| hdl = 10044/1/25173
 | s2cid = 153704373
 | hdl-access = free}}</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
|-
! 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, [[Place:Amsterdam|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 [[Software:Ruby on Rails|Rails]]) and displays in a [[Software:Web browser|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 megajoules) 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
|-
! 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 and links to the open source [[Organization:COIN-OR#CBC|Cbc]] solver. Alternatively, the GLPK, [[Software:CPLEX|or CPLEX]] solvers can be employed. EnergyPATHWAYS utilizes the [[Software:PostgreSQL|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|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 [[Physics:Power-to-gas|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
 | s2cid = 2999525
 | 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
 | pmid = 22116030
 | bibcode = 2012Sci...335...53W| osti = 1209365
 | url = http://www.escholarship.org/uc/item/2mz2472z}} See also published [https://www.science.org/doi/10.1126/science.1215539 correction].
</ref> And the US PATHWAYS model contributed to the United Nations [[Organization:Deep Decarbonization Pathways Project|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] {{Webarchive|url=https://web.archive.org/web/20161109225110/http://ordecsys.com/en/home |date=9 November 2016 }}
|-
! Status
| active
|-
! Scope/type
| municipal
|-
! Code license
| [[Software: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 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
 | archive-date = 4 August 2016
 | archive-url = https://web.archive.org/web/20160804091725/http://ordecsys.com/fr/system/files/u1/Ordecsys-ETEM-SG.pdf
 | url-status = dead
 }}</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 [[Physics:District heating|district heating]] networks, household energy systems, and grid storage, including the use of [[Engineering: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 [[Engineering:Smart grid|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
 | title = Energy and Environment
 | pages = 27–61
 | last2 = Haurie | first2 = Alain
 | last3 = Labriet | first3 = Maryse
 | last4 = Thalmann | first4 = Philippe
 | last5 = Vielle | first5 = Marc
 | last6 = Viguier | first6 = Laurent
 | chapter = 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
 | citeseerx = 10.1.1.111.8420
 | isbn = 0-387-25351-3}}</ref> A 2012 study examines the design of [[Engineering:Smart grid|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|case study]], roughly calibrated on the [[Place:Geneva|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
 | s2cid = 166497864
 | 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 =10.1007/bf03399367
 | 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
| [[Software: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 optimization model for local energy systems. It is being developed at the Institute for Energy Economy and Application Technology, Technical University of Munich, [[Place: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 [[Software:CPLEX|CPLEX]] solver.

Based on 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}}

=== GENeSYS-MOD ===

{| class="infobox" style="width: 28em"
|-
! style="width: 35%" | Project
| style="width: 55%" | GENeSYS{{nbhyph}}MOD
|-
! Host
| Technische Universität Berlin
|-
! Status
| active
|-
! Scope/type
| multi{{nbhyph}}commodity optimization
|-
! Code license
| Apache-2.0
|-
! Data license
| [[Creative Commons license|CC{{nbhyph}}BY{{nbhyph}}4.0]]
|-
! Language
|[[Software:General Algebraic Modeling System|GAMS]], [[Julia (programming language)|Julia]]
|-
! Website
|
|-
! Repository
| {{URL|https://github.com/GENeSYS-MOD}}
|-
! Documentation
| {{URL|https://github.com/GENeSYS-MOD}}
|-
! Datasets
| {{URL|https://github.com/GENeSYS-MOD}}
|-
! Publications
| {{URL|https://zenodo.org/communities/genesys-mod}}
|}

The Global Energy System Model (GENeSYS{{nbhyph}}MOD) is a linear cost-minimizing optimization model being developed at Technische Universität Berlin, Germany.{{efn|This project should not to be confused with the similarly named GENESYS project.}}  The project was originally based on the OSeMOSYS framework and the first version was released in 2017 using [[Software:General Algebraic Modeling System|GAMS]].<ref name="loeffler-etal-2017a">
{{cite journal
 | last1 = Löffler | first1 = Konstantin
 | last2 = Hainsch | first2 = Karlo
 | last3 = Burandt | first3 = Thorsten
 | last4 = Kemfert | first4 = Claudia
 | last5 = von Hirschhausen | first5 = Christian
 | title = Designing a model for the global energy system — GENeSYS-MOD: an application of the Open-Source Energy Modeling System (OSeMOSYS)
 | date = 2017
 | journal = Energies
 | volume = 10
 | issue = 1468
 | page = 1468
 | doi = 10.3390/en10101468
 | doi-access = free
 | s2cid = 6944356
 | issn = 1996-1073
| hdl = 10419/200750
 | hdl-access = free
 }}</ref>  The codebase was later translated into [[Julia (programming language)|Julia]].  Both versions and a representative dataset are available on GitHub.

[[File:GENeSYS-MOD energy framework schematic 02.svg|thumb|left|GENeSYS-MOD schematic showing overall structure]]

GENeSYS{{nbhyph}}MOD couples the demand sectors covering electricity, buildings, industry, and transport and finds the cost-optimal investment into conventional and renewable energy generation, storage, and infrastructure.  The research focus is on long-term system development and pathway analysis.<ref name="burandt-etal-2018">
{{cite report
 | last1 = Burandt | first1 = Thorsten
 | last2 = Löffler | first2 = Konstantin
 | last3 = Hainsch | first3 = Karlo
 | title = GENeSYS-MOD v2.0 — Enhancing the Global Energy System Model: model improvements, framework changes, and European data set
 | date = 2018
 | series = DIW Berlin Data Documentation
 | number = 94
 | publisher = DIW Berlin (Deutsches Institut für Wirtschaftsforschung)
 | location = Berlin, Germany
 | issn = 1861-1532
 | url = https://www.econstor.eu/handle/10419/180395
 | access-date = 2024-10-24
}} {{URL|1=https://www.econstor.eu/bitstream/10419/180395/1/1026898889.pdf|2=PDF file.}}
</ref>

The model was first used to analyze decarbonization scenarios at the global level, broken down into ten regions.  However, the framework is highly flexible, allowing for calculations at various levels of detail, from individual households to global aggregations, depending on the desired research question and availability of input data.

A{{nbsp}}2019 study examined the low{{nbhyph}}carbon transition of the European energy system and specifically the problem of [[Stranded asset|stranded asset]]s under a{{nbsp}}range of scenarios.  It found that up to {{euro|200{{nbsp}}billion}} in fossil-fueled capacities could be stranded by 2035 unless stronger policy signals are able to address short{{nbhyph}}term planning biases.<ref name="loeffler-etal-2019">
{{cite journal
 | last1 = Löffler | first1 = Konstantin
 | last2 = Burandt | first2 = Thorsten
 | last3 = Hainsch | first3 = Karlo
 | last4 = Oei | first4 = Pao-Yu
 | title = Modeling the low-carbon transition of the European energy system — a quantitative assessment of the stranded assets problem
 | date = 16 November 2019
 | journal = Energy Strategy Reviews
 | volume = 26
 | pages = 100422
 | doi = 10.1016/j.esr.2019.100422
 | bibcode = 2019EneSR..2600422L
 | issn = 2211-467X
 | url = https://www.sciencedirect.com/science/article/pii/S2211467X19301142/pdfft
 | access-date = 2024-10-25
| hdl = 10419/214633
 | hdl-access = free
 }} {{open access}}
</ref>  Another 2019 study evaluates China's energy system transformation, highlighting the need to reduce coal consumption by 60% by 2050 to meet global climate targets.  Renewable energies, and in particular [[Chemistry:Photovoltaics|photovoltaics]] and onshore wind, emerge as cost-effective solutions, but overcoming local resistance and increasing [[Social:Stakeholder engagement|stakeholder engagement]] remain crucial for success.<ref name="burandt-etal-2019">
{{cite journal
 | last1 = Burandt | first1 = Thorsten
 | last2 = Xiong | first2 = Bobby
 | last3 = Löffler | first3 = Konstantin
 | last4 = Oei | first4 = Pao-Yu
 | title = Decarbonizing China's energy system — Modeling the transformation of the electricity, transportation, heat, and industrial sectors
 | date = 1 December 2019
 | journal = Applied Energy
 | volume = 255
 | doi = 10.1016/j.apenergy.2019.113820
 | bibcode = 2019ApEn..25513820B
 | url = https://www.sciencedirect.com/science/article/pii/S0306261919315077/pdfft
 | access-date = 2024-10-22
| hdl = 10419/214646
 | hdl-access = free
 }}  {{open access}}
</ref>  A{{nbsp}}2021 study investigates the [[Earth:European Green Deal|European Green Deal]] goal of achieving 100% [[Chemistry:Greenhouse gas|greenhouse gas]] reductions by 2050, examining the interplay of technological developments, policy imperatives, and societal attitudes.  The study presents four future storylines that highlight the critical contribution of high rates of [[Physics:Electrification|electrification]] combined with near{{nbhyph}}term technology deployment to achieve the necessarily rapid decarbonization.<ref name="hainsch-etal-2022">
{{cite journal
 | last1 = Hainsch | first1 = Karlo
 | last2 = Löffler | first2 = Konstantin
 | last3 = Burandt | first3 = Thorsten
 | last4 = Auer | first4 = Hans
 | last5 = Crespo del Granado | first5 = Pedro
 | last6 = Pisciella | first6 = Paolo
 | last7 = Zwickl-Bernhard | first7 = Sebastian
 | title = Energy transition scenarios: what policies, societal attitudes, and technology developments will realize the EU Green Deal?
 | date = 15 January 2022
 | journal = Energy
 | volume = 239
 | doi = 10.1016/j.energy.2021.122067
 | bibcode = 2022Ene...23922067H
 | url = https://www.sciencedirect.com/science/article/pii/S036054422102315X/pdfft
 | access-date = 2024-10-22
| doi-access = free
 }} {{open access}}
</ref>

{{clear}}

===GenX===
{| class="infobox" style="width: 28em"
|-
! style="width: 35%" | Project
| style="width: 55%" | GenX
|-
! Host
| [[Organization:Massachusetts Institute of Technology|MIT]] and [[Organization:Princeton University|Princeton University]]
|-
! Status
| active
|-
! Scope/type
| multi{{nbhyph}}commodity sector investment planning
|-
! Code license
| [[Software:GNU General Public License|GPLv2]]
|-
! Website
| {{URL|http://genx.mit.edu}}
|-
! Repository
| {{URL|https://github.com/GenXProject/GenX}}
|-
! Documentation
| {{URL|https://genxproject.github.io/GenX/dev/}}
|}

[[File:Carbon impacts of core scenarios from 2022 GenX numerical study examining rapid displacement of natural gas from Russia.png|thumb|left|Carbon impacts for three core scenarios from the 2022 numerical study that examined the rapid elimination of Russian natural gas from Europe. All core scenarios resulted in reduced emissions.<ref name="lau-etal-2022"/>{{rp|7}}]]

GenX is multi{{nbhyph}}commodity sector capacity expansion model originally developed by researchers in the United States.<ref name="jenkins-and-sepulveda-2017">
{{cite book
 | last1 = Jenkins | first1 = Jesse D
 | last2 = Sepulveda | first2 = Nestor A
 | title = Enhanced decision support for a changing electricity landscape: the GenX configurable electricity resource capacity expansion model — An MIT Energy Initiative Working Paper — Revision{{nbsp}}1.0
 | date = 27 November 2017
 | publisher = Massachusetts Institute of Technology
 | location = Cambridge, Massachusetts, USA
 | url = https://energy.mit.edu/wp-content/uploads/2017/10/Enhanced-Decision-Support-for-a-Changing-Electricity-Landscape.pdf
 | access-date = 2021-04-06}} MITEI-WP-2017-10.
</ref><ref name="genx-documentation">
{{cite web
 | title = GenX documentation
 | work = GenX project
 | url = https://genxproject.github.io/GenX/dev/
 | access-date = 2021-06-09}}</ref> The framework is written in [[Julia (programming language)|Julia]] and deploys the [[JuMP]] library for building the underlying optimization problem.<ref name="dunning-etal-2017">
{{cite journal
 | last1 = Dunning | first1 = Iain
 | last2 = Huchette | first2 = Joey
 | last3 = Lubin | first3 = Miles
 | title = JuMP: a modeling language for mathematical optimization
 | date = 2017
 | journal = SIAM Review
 | volume = 59
 | issue = 2
 | pages = 295–320
 | doi = 10.1137/15M1020575
 | arxiv = 1508.01982
 | s2cid = 1407251
 | issn = 0036-1445
 | url = https://mlubin.github.io/pdf/jump-sirev.pdf
 | access-date = 2018-05-21}}</ref><ref name="jump-ongoing">
{{cite web
 | title = JuMP
 | url = https://jump.dev/JuMP.jl/stable/
 | access-date = 2021-06-09}}</ref> GenX through [[JuMP]] can utilize various open source (including [[Organization:COIN-OR#CBC|CBC]]/[[Organization:COIN-OR#CLP|CLP]]) and commercial optimization solvers (including [[Software:CPLEX|CPLEX]]). In June{{nbsp}}2021, the project launched as an active open source project and test suites are available to assist onboarding.<ref name="jenkins-2021">
{{cite web
 | last1 = Jenkins | first1 = Jesse
 | title = GenX open source release
 | date = 9 June 2021
 | work = Open Energy Modelling Initiative
 | url = https://groups.google.com/g/openmod-initiative/c/idddpATurxU
 | access-date = 2021-06-09}} Public mailing list posting.</ref>

In parallel, the [[Open energy system databases#PowerGenome|PowerGenome]] project is designed to provide GenX with a comprehensive current state dataset of the United States electricity system.<ref name="schivley-2020">
{{cite AV media
 | last1 = Schivley | first1 = Greg
 | title = Create capacity expansion model inputs with PowerGenome
 | date = 26 March 2020
 | publisher = Open Energy Modelling Initiative (openmod)
 | url = https://www.youtube.com/watch?v=GFr9olzie1o
 | access-date = 2020-09-16
 | type = webcast
 | format = MP4}} MP4 webcast 00:10:55.</ref> That dataset can then be used as a springboard to develop future scenarios.

GenX has been used to explore long-term storage options in systems with high renewables shares,<ref name="sepulveda-etal-2021">
{{cite journal
 | last1 = Sepulveda | first1 = Nestor A
 | last2 = Jenkins | first2 = Jesse D
 | last3 = Edington | first3 = Aurora
 | last4 = Mallapragada | first4 = Dharik S
 | last5 = Lester | first5 = Richard K
 | title = The design space for long-duration energy storage in decarbonized power systems
 | date = May 2021
 | journal = Nature Energy
 | volume = 6
 | issue = 5
 | pages = 506–516
 | doi = 10.1038/s41560-021-00796-8
 | bibcode = 2021NatEn...6..506S
 | hdl = 1721.1/138145.2
 | s2cid = 233647828
 | issn = 2058-7546| hdl-access = free
 }} {{closed access}}</ref><ref name="mallapragada-etal-2020">
{{cite journal
 | last1 = Mallapragada | first1 = Dharik S
 | last2 = Sepulveda | first2 = Nestor A
 | last3 = Jenkins | first3 = Jesse D
 | title = Long-run system value of battery energy storage in future grids with increasing wind and solar generation
 | date = 1 October 2020
 | journal = Applied Energy
 | volume = 275
 | pages = 115390
 | doi = 10.1016/j.apenergy.2020.115390
 | s2cid = 224962497
 | issn = 0306-2619| doi-access = free
 | bibcode = 2020ApEn..27515390M
 }} {{closed access}}</ref> to explore the value of '[[Physics:Dispatchable generation|firm]]' low-carbon power generation options,<ref name="sepulveda-etal-2018">
{{cite journal
 | last1 = Sepulveda | first1 = Nestor A
 | last2 = Jenkins | first2 = Jesse D
 | last3 = de Sisternes | first3 = Fernando J
 | last4 = Lester | first4 = Richard K
 | title = The role of firm low-carbon resources in deep decarbonization of power generation
 | date = 21 November 2018
 | journal = Joule
 | volume = 2
 | issue = 11
 | pages = 2403–2420
 | doi = 10.1016/j.joule.2018.08.006
 | doi-access=free
 | s2cid = 169890352}}</ref> and a variety of other applications. While North America remains a key focus, the software has been applied to problems in India,<ref name="rudnick-garcia-2019">
{{cite book
 | last1 = Rudnick García | first1 = Iván
 | title = Decarbonizing the Indian power sector by 2037: evaluating different pathways that meet long-term emissions targets
 | date = September 2019
 | publisher = Massachusetts institute Of Technology (MIT)
 | location = Cambridge, Massachusetts, USA
 | url = https://dspace.mit.edu/bitstream/handle/1721.1/127737/1196239288-MIT.pdf
 | access-date = 2021-06-09}}
</ref> Italy,<ref name="bompard-etal-2020">
{{cite journal
 | last1 = Bompard | first1 = E
 | last2 = Botterud | first2 = A
 | last3 = Corgnati | first3 = S
 | last4 = Huang | first4 = T
 | last5 = Jafari | first5 = M
 | last6 = Leone | first6 = P
 | last7 = Mauro | first7 = S
 | last8 = Montesano | first8 = G
 | last9 = Papa | first9 = C
 | last10 = Profumo | first10 = F
 | title = An electricity triangle for energy transition: application to Italy
 | date = 1 November 2020
 | journal = Applied Energy
 | volume = 277
 | pages = 115525
 | doi = 10.1016/j.apenergy.2020.115525
 | bibcode = 2020ApEn..27715525B
 | s2cid = 225025268
 | issn = 0306-2619}} {{closed access}}</ref> and Spain.<ref name="oyler-and-parsons-2020">{{cite book
 | last1 = Oyler
 | first1 = Anthony Fratto
 | last2 = Parsons
 | first2 = John E
 | title = The value of pumped hydro storage for deep decarbonization of the Spanish grid — Working paper CEEPR{{nbsp}}WP{{nbsp}}2020-007
 | date = May 2020
 | publisher = MIT Center for Energy and Environmental Policy Research (CEEPR)
 | location = Cambridge, Massachusetts, USA
 | url = http://ceepr.mit.edu/files/papers/2020-007.pdf
 | access-date = 2021-06-09
 | archive-date = 9 June 2021
 | archive-url = https://web.archive.org/web/20210609090202/http://ceepr.mit.edu/files/papers/2020-007.pdf
 | url-status = dead
 }}</ref>

GenX was deployed in a 2021 case study with Louisville Gas and Electric and Kentucky Utilities that showed that stakeholder-driven modeling utilizing open{{nbhyph}}source tools and public data can contribute productively to utility{{nbhyph}}led analysis and planning.<ref name="schwartz-etal-2022">
{{cite book
 | last1 = Schwartz | first1 = Aaron
 | last2 = Dyson | first2 = Mark
 | last3 = Shwisberg | first3 = Lauren
 | title = Power planning to the people: how stakeholder-driven modeling can help build a better grid
 | date = December 2022
 | publisher = RMI
 | location = Basalt, Colorado, United States
 | url = https://rmi.org/insight/power-planning-to-the-people/
 | access-date = 2023-06-30
}}  Registration required.  {{open access}}
</ref><ref name="schwartz-2022">
{{cite web
 | last1 = Schwartz | first1 = Aaron
 | title = Free software, more voices, better plans
 | date = 12 December 2022
 | work = RMI
 | location = Basalt, Colorado, USA
 | url = https://rmi.org/free-software-more-voices-better-plans/
 | access-date = 2023-06-29
}}
</ref>

A mid{{nbhyph}}2022 study examined the natural gas crisis facing Europe, and particularly Germany, and concluded that there are several feasible paths (labeled "cases") to eliminate all imports of Russian natural gas by October{{nbsp}}2022.<ref name="lau-etal-2022">
{{cite book
 | last1 = Lau | first1 = Michael
 | last2 = Ricks | first2 = Wilson
 | last3 = Patankar | first3 = Neha
 | last4 = Jenkins | first4 = Jesse
 | title = Pathways to European independence from Russian natural gas
 | date = 8 July 2022
 | publisher = Zero Lab, Princeton University
 | location = Princeton, New Jersey, USA
 | doi = 10.5281/zenodo.6811675
 | url = https://zenodo.org/record/6812163
 | access-date = 2022-07-09}} DOI resolves to latest version.  License information from Zenodo landing page.  {{open access}}
</ref><ref name="anon-2022">
{{cite book
 | author = Anon
 | title = FAQ: European independence from Russian natural gas
 | date = 8 July 2022
 | publisher = Zero Lab, Princeton University
 | location = Princeton, New Jersey, USA
 | doi = 10.5281/zenodo.6811675
 | url = https://zenodo.org/record/6812163
 | access-date = 2022-07-09}} DOI resolves to latest version. License information from Zenodo landing page.  {{open access}}
</ref> Ongoing work seeks to examine the effect of extending the operating lives of Germany's three remaining nuclear reactors past 2022 and the effect of strong [[Earth:Drought|drought]] conditions on hydro{{nbsp}}generation and the system more generally. 
{{clear}}

===oemof===
{| class="infobox" style="width: 28em"
|-
! style="width: 35%" | Project
| style="width: 55%" | oemof
|-
! Host
| oemof{{nbsp}}e.V. nonprofit with community support from {{plainlist|
* [http://reiner-lemoine-institut.de/en/ Reiner Lemoine Institute]
* [[Organization:University of Flensburg|University of Flensburg]]
* Flensburg University of Applied Sciences
}}
|-
! Status
| active
|-
! Scope/type
| electricity, heat, mobility, gas
|-
! Code license
| MIT
|-
! Language
| [[Python (programming language)|Python]]
|-
! Website
| {{plainlist|
* {{URL|https://oemof.org}}
<!-- *{{url|http://reiner-lemoine-institut.de/en/oemof/}} -->
}}
|-
! Repository
| {{URL|https://github.com/oemof/}}
|-
! Documentation
| {{URL|https://oemof.readthedocs.io}}
|-
! Discussion
| {{URL|https://forum.openmod.org/tag/oemof}}
|}

oemof stands for Open Energy Modelling Framework. The project is managed by the Reiner Lemoine Institute, [[Place:Berlin|Berlin]], Germany and the Center for Sustainable Energy Systems (CSES or ZNES) at the [[Organization:University of Flensburg|University of Flensburg]] and the Flensburg University of Applied Sciences, both Flensburg, Germany. The project runs two websites and a [[GitHub]] repository. oemof is written in Python and uses [[Pyomo]] and [[Organization:COIN-OR|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 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.

oemof has been applied in sub{{nbhyph}}Saharan Africa.<ref name="juanpera-etal-2020">
{{cite journal
 | last1 = Juanpera | first1 = M
 | last2 = Blechinger | first2 = P
 | last3 = Ferrer-Martí | first3 = L
 | last4 = Hoffmann | first4 = MM
 | last5 = Pastor | first5 = R
 | title = Multicriteria-based methodology for the design of rural electrification systems: a case study in Nigeria
 | date = 1 November 2020
 | journal = Renewable and Sustainable Energy Reviews
 | volume = 133
 | pages = 110243
 | doi = 10.1016/j.rser.2020.110243
 | bibcode = 2020RSERv.13310243J
 | hdl = 2117/328448
 | s2cid = 221817141
 | issn = 1364-0321
 | url = https://upcommons.upc.edu/bitstream/handle/2117/328448/RSER%2BJuanpera%2Bet%2Bal.%2BManuscript_RV-clean.pdf
 | access-date = 2022-05-24}} Linked URL is a preprint.
</ref> A masters project in 2020 compared oemof and OSeMOSYS.<ref name="muschner-2020">
{{cite thesis
 | last1 = Muschner | first1 = Christoph
 | title = An Open Source Energy Modelling Framework Comparison of OSeMOSYS and oemof
 | type = MSc
 | date = 2020
 | publisher = KTH
 | location = Stockholm
 | url = https://www.diva-portal.org/smash/get/diva2:1444892/FULLTEXT01.pdf
 | access-date = 2020-11-03}}</ref>

{{clear}}

===OSeMOSYS===
{| class="infobox" style="width: 28em"
|-
! style="width: 35%" | Project
| style="width: 55%" | OSeMOSYS
|-
! Host
| community project
|-
! Status
| active
|-c
! Scope/type
| planning at all scales
|-
! Code license
| [[Software:Apache License|Apache 2.0]]
|-
! Language
| various
|-
! Website
| {{URL|http://www.osemosys.org}}
|-
! Forum
| {{URL|https://groups.google.com/g/osemosys}}
|-
! Repository
| {{URL|https://github.com/OSeMOSYS/OSeMOSYS}}
|-
! Discussion
| {{URL|https://forum.openmod.org/tag/osemosys}}
|}

OSeMOSYS stands for Open Source Energy Modelling System. OSeMOSYS is intended for national and regional policy development and uses an intertemporal optimization framework. The model posits a single socially motivated operator/investor with perfect foresight. The OSeMOSYS project is a community endeavor, supported by the division of Energy Systems, KTH Royal Institute of Technology, [[Place:Stockholm|Stockholm]], Sweden. The project maintains a website providing background. The project also offers several active [[Internet forum|internet forum]]s on [[Software:Google Groups|Google Groups]]. OSeMOSYS was originally written in MathProg, a high-level [[Mathematical optimization|mathematical programming]] language. It was subsequently reimplemented in [[Software: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. 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>

[[File:Osemosys energy model results for fictitious atlantis.png|thumb|left|Simplified results for a fictitious country called Atlantis used for training purposes]]

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. 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]]. OSeMOSYS has been used as a base for constructing reduced models of energy systems.<ref name="volodina-etal-2022">
{{cite arXiv
 | last1 = Volodina | first1 = Victoria
 | last2 = Sonenberg | first2 = Nikki
 | last3 = Smith | first3 = Jim Q
 | last4 = Challenor | first4 = Peter G
 | last5 = Dent | first5 = Chris J
 | last6 = Wynn | first6 = Henry P
 | title = Propagating uncertainty in a network of energy models
 | date = 2022
 | class = stat.AP
 | eprint = 2201.09624
 | quote =[[Gaussian process emulator]]s can be used to approximate the behaviour of complex, computationally intensive models and used to generate predictions together with a measure of uncertainty about the predicted model output.}}</ref>

[[File:Cumulative electricity trade 2015–2065 among African countries, reference scenario (Pappis et al 2022).png|thumb|right|Cumulative electricity trade (2015–2065) among African countries for the reference scenario (TWh){{nnbsp}}<ref name="pappis-etal-2022"/>{{rp|8}}]]

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
 | url = https://www.researchgate.net/publication/229284137
 | 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.</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
 | access-date = 2016-06-17}}</ref> A 2012 study extends OSeMOSYS to capture the salient features of a [[Engineering:Smart grid|smart grid]]. The paper explains how to model variability in generation, flexible demand, and [[Engineering: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| bibcode = 2012Ene....46..337W
 }}</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 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
 | bibcode = 2015Ene....79..385F
 | 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
 | s2cid = 44593439
 | title = Coupling techno-economic energy models with behavioral approaches
 | year = 2016
 | journal = Operational Research
 | volume = <!-- not stated on journal website -->
 | issue = 2
 | pages = 1–15
 | doi = 10.1007/s12351-016-0246-9
| url = http://opus.bath.ac.uk/50662/1/article_or_15_02.pdf}}</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| bibcode = 2016ERSS...15...12F
 }}</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
 | bibcode = 2017EneSR..16....1N
 | issn = 2211-467X
 | url = https://doaj.org/article/446233bf7de84624b69b16daf57e4171
 }} </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
 | bibcode = 2017EnPol.107..197T
 | issn = 0301-4215}}</ref>

OSeMOSYS has been used to construct wide-area electricity models for [[Earth:Africa|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
 | bibcode = 2016ESusD..31...50T
 | 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 [[Company:ResearchGate|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 = https://un-desa-modelling.github.io/clews-mauritius-presentation/
 | access-date = 2017-01-13
 | archive-date = 15 December 2017
 | archive-url = https://web.archive.org/web/20171215200408/http://un-desa-modelling.github.io/clews-mauritius-presentation/
 | url-status = dead
 }}</ref> Models have previously been built for the Baltic States, Bolivia, Nicaragua, Sweden, and Tanzania.<ref name="rocco-etal-2021">
{{cite journal
 | last1 = Rocco | first1 = Matteo V
 | last2 = Fumagalli | first2 = Elena
 | last3 = Vigone | first3 = Chiara
 | last4 = Miserocchi | first4 = Ambrogio
 | last5 = Colombo | first5 = Emanuela
 | title = Enhancing energy models with geo-spatial data for the analysis of future electrification pathways: the case of Tanzania
 | date = 1 March 2021
 | journal = Energy Strategy Reviews
 | volume = 34
 | pages = 100614
 | doi = 10.1016/j.esr.2020.100614
 | doi-access=free
 | bibcode = 2021EneSR..3400614R
 | s2cid = 233779581
 | issn = 2211-467X| hdl = 11311/1167171
 | hdl-access = free
 }}</ref> A{{nbsp}}2021 paper summarizes recent applications and also details various versions, forks, and local enhancements related to the OSeMOSYS codebase.<ref name ="niet-etal-2021"/> An{{nbsp}}electricity sector analysis for Bangladesh completed in{{nbsp}}2021 concluded that [[Engineering:Solar power|solar power]] is economically competitive under every investigated scenario.<ref name="olsson-and-gardumi-2021">
{{cite journal
 | last1 = Olsson | first1 = John Mogren
 | last2 = Gardumi | first2 = Francesco
 | title = Modelling least cost electricity system scenarios for Bangladesh using OSeMOSYS
 | date = 1 November 2021
 | journal = Energy Strategy Reviews
 | volume = 38
 | pages = 100705
 | doi = 10.1016/j.esr.2021.100705
 | doi-access=free
 | bibcode = 2021EneSR..3800705O
 | s2cid = 239658794
 | issn = 2211-467X}}</ref> A{{nbsp}}2022 study looked at the effects of a changing climate on the Ethiopian power system.<ref name="mekonnen-etal-2022">
{{cite journal
 | last1 = Mekonnen | first1 = Tewodros Walle
 | last2 = Teferi | first2 = Solomon Tesfamariam
 | last3 = Kebede | first3 = Fitsum Salehu
 | last4 = Anandarajah | first4 = Gabrial
 | title = Assessment of impacts of climate change on hydropower-dominated power system — the case of Ethiopia
 | date = January 2022
 | journal = Applied Sciences
 | volume = 12
 | issue = 4
 | pages = 1954
 | doi = 10.3390/app12041954
 | issn = 2076-3417
 | url = https://www.mdpi.com/2076-3417/12/4/1954/pdf
 | access-date = 2022-02-13
| doi-access = free}} {{open access}}</ref> OSeMOSYS has also been applied variously in Zimbabwe{{nnbsp}}<ref name="howells-etal-2021">
{{cite journal
 | last1 = Howells | first1 = Mark
 | last2 = Boehlert | first2 = Brent
 | last3 = Benitez | first3 = Pablo César
 | title = Potential climate change risks to meeting Zimbabwe's NDC goals and how to become resilient
 | date = January 2021
 | journal = Energies
 | volume = 14
 | issue = 18
 | pages = 5827
 | doi = 10.3390/en14185827
 | issn = 1996-1073
 | doi-access = free| hdl = 1721.1/146571
 | hdl-access = free
 }} {{open access}}
</ref> and Ecuador.<ref name="fonseca-and-gardumi-2022">
{{cite journal
 | last1 = Fonseca | first1 = Roberto Heredia
 | last2 = Gardumi | first2 = Francesco
 | title = Assessing the impact of applying individual discount rates in power system expansion of Ecuador using OSeMOSYS
 | date = 14 February 2022
 | journal = International Journal of Sustainable Energy Planning and Management
 | volume = 33
 | pages = 35–52
 | doi = 10.5278/ijsepm.6820
 | issn = 2246-2929
 | url = https://journals.aau.dk/index.php/sepm/article/view/6820/6046
 | access-date = 2022-02-15}} {{open access}}</ref> Another 2022 study examined water usage, split by withdraws and consumption, for several low carbon energy strategies for Africa.<ref name="pappis-etal-2022">
{{cite journal
 | last1 = Pappis | first1 = Ioannis
 | last2 = Sridharan | first2 = Vignesh
 | last3 = Howells | first3 = Mark
 | last4 = Medarac | first4 = Hrvoje
 | last5 = Kougias | first5 = Ioannis
 | last6 = Sánchez | first6 = Rocío González
 | last7 = Shivakumar | first7 = Abhishek
 | last8 = Usher | first8 = Will
 | title = The effects of climate change mitigation strategies on the energy system of Africa and its associated water footprint
 | date = March 2022
 | journal = Environmental Research Letters
 | volume = 17
 | issue = 4
 | pages = 044048
 | doi = 10.1088/1748-9326/ac5ede
 | bibcode = 2022ERL....17d4048P
 | s2cid = 247542994
 | issn = 1748-9326
 | doi-access = free
 }} {{open access}}
</ref> Another study that year examined renewable energy in [[Place:Egypt|Egypt]].<ref name="dallmann-etal-2022">
{{cite journal
 | last1 = Dallmann | first1 = Christoph
 | last2 = Schmidt | first2 = Matthew
 | last3 = Möst | first3 = Dominik
 | title = Between path dependencies and renewable energy potentials: a case study of the Egyptian power system
 | date = 1 May 2022
 | journal = Energy Strategy Reviews
 | volume = 41
 | pages = 100848
 | doi = 10.1016/j.esr.2022.100848
 | s2cid = 248757578
 | issn = 2211-467X
 | url = https://www.sciencedirect.com/science/article/pii/S2211467X22000451/pdfft
 | access-date = 2022-05-24| doi-access = free
 | bibcode = 2022EneSR..4100848D
 }}</ref> And another the Dominican Republic.<ref name="quevedo-and-moya-2022">
{{cite journal
 | last1 = Quevedo | first1 = Jarrizon
 | last2 = Moya | first2 = Idalberto Herrera
 | title = Modeling of the Dominican Republic energy systems with OSeMOSYS to assess alternative scenarios for the expansion of renewable energy sources
 | date = 16 June 2022
 | journal = Energy Nexus
 | volume = 6
 | pages = 100075
 | doi = 10.1016/j.nexus.2022.100075
 | s2cid = 248483022
 | issn = 2772-4271| doi-access = free
 | bibcode = 2022EnNex...6j0075Q
 }} {{open access}}</ref> The Italian island of [[Earth:Pantelleria|Pantelleria]] was used as a case study to compare battery and hydrogen storage and found that a hybrid system was least cost.<ref name="marocco-etal-2023">
{{cite journal
 | last1 = Marocco | first1 = Paolo
 | last2 = Novo | first2 = Riccardo
 | last3 = Lanzini | first3 = Andrea
 | last4 = Mattiazzo | first4 = Giuliana
 | last5 = Santarelli | first5 = Massimo
 | title = Towards 100% renewable energy systems: the role of hydrogen and batteries
 | date = 1 January 2023
 | journal = Journal of Energy Storage
 | volume = 57
 | pages = 106306
 | doi = 10.1016/j.est.2022.106306
 | bibcode = 2023JEnSt..5706306M
 | s2cid = 254480905
 | issn = 2352-152X
 | url = https://www.sciencedirect.com/science/article/pii/S2352152X22022952
 | access-date = 2023-01-02}} {{closed access}}</ref>

In 2016, work started on a [[Software:Web browser|browser]]-based interface to OSeMOSYS, known as the Model Management Infrastructure (MoManI). Led by the 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> A simplified GUI interface named clicSAND and utilizing Excel and Access was released in March{{nbsp}}2021.<ref name="climate-compatible-growth-ongoing">
{{cite web
 | author = Climate Compatible Growth
 | title = clicSAND
 | work = GitHub
 | url = https://github.com/ClimateCompatibleGrowth/clicSAND
 | access-date = 2021-05-17}}
</ref><ref name="cannone-etal-2024">
{{cite journal
 | last1 = Cannone | first1 = Carla
 | last2 = Allington | first2 = Lucy
 | last3 = de Wet | first3 = Nicki
 | last4 = Shivakumar | first4 = Abhishek
 | last5 = Goyns | first5 = Philip
 | last6 = Valderrama | first6 = Cesar
 | last7 = Kell | first7 = Alexander
 | last8 = Plazas Niño | first8 = Fernando Antonio
 | last9 = Mohanty | first9 = Reema
 | last10 = Kapor | first10 = Vedran
 | last11 = Wright | first11 = Jarrad
 | last12 = Yeganyan | first12 = Rudolf
 | last13 = Tan | first13 = Naomi
 | last14 = To | first14 = Long Seng
 | last15 = Harrison | first15 = John
 | last16 = Howells | first16 = Mark
 | title = clicSAND for OSeMOSYS: A user-friendly interface using open-source optimisation software for energy system modelling analysis
 | date = January 2024
 | journal = Energies
 | volume = 17
 | issue = 16
 | pages = 3923
 | doi = 10.3390/en17163923
 | doi-access = free
 | issn = 1996-1073
 | url = https://www.mdpi.com/1996-1073/17/16/3923/pdf?version=1723108318
 | access-date = 2024-08-15
| hdl = 2117/417085
 | hdl-access = free
 }}  {{open access}}
</ref> A [[Command-line interface|CLI]] workflow tool named otoole bundles several dedicated utilities, including one that can convert between [[Comma-separated values#OKI frictionless tabular data package|OKI frictionless data]] and [[Software:GNU Linear Programming Kit|GNU MathProg]] data formats.<ref name="osemosys-ongoing">
{{cite web
 | author = OSeMOSYS
 | title = otoole: OSeMOSYS tools for energy work: a Python toolkit to support use of OSeMOSYS
 | date = nd
 | url = https://otoole.readthedocs.io/en/latest/
 | access-date = 2021-05-26}}</ref><ref name="niet-etal-2021">
{{cite journal
 | last1 = Niet | first1 = Taco
 | last2 = Shivakumar | first2 = Abhishek
 | last3 = Gardumi | first3 = Francesco
 | last4 = Usher | first4 = Will
 | last5 = Williams | first5 = Eric
 | last6 = Howells | first6 = Mark
 | title = Developing a community of practice around an open source energy modelling tool
 | date = 1 May 2021
 | journal = Energy Strategy Reviews
 | volume = 35
 | pages = 100650
 | doi = 10.1016/j.esr.2021.100650
 | doi-access=free
 | bibcode = 2021EneSR..3500650N
 | s2cid = 233562043
 | issn = 2211-467X}}</ref>{{rp|3}} In 2022, the project released starter{{nbsp}}kits for modeling selected countries in Africa, East Asia, and South America.<ref name="allington-etal-2022">
{{cite journal
 | last1 = Allington | first1 = Lucy
 | last2 = Cannone | first2 = Carla
 | last3 = Pappis | first3 = Ioannis
 | last4 = Barron | first4 = Karla Cervantes
 | last5 = Usher | first5 = Will
 | last6 = Pye | first6 = Steve
 | last7 = Brown | first7 = Edward
 | last8 = Howells | first8 = Mark
 | last9 = Walker | first9 = Miriam Zachau
 | last10 = Ahsan | first10 = Aniq
 | last11 = Charbonnier | first11 = Flora
 | last12 = Halloran | first12 = Claire
 | last13 = Hirmer | first13 = Stephanie
 | last14 = Cronin | first14 = Jennifer
 | last15 = Taliotis | first15 = Constantinos
 | last16 = Sundin | first16 = Caroline
 | last17 = Sridharan | first17 = Vignesh
 | last18 = Ramos | first18 = Eunice
 | last19 = Brinkerink | first19 = Maarten
 | last20 = Deane | first20 = Paul
 | last21 = Gritsevskyi | first21 = Andrii
 | last22 = Moura | first22 = Gustavo
 | last23 = Rouget | first23 = Arnaud
 | last24 = Wogan | first24 = David
 | last25 = Barcelona | first25 = Edito
 | last26 = Niet | first26 = Taco
 | last27 = Rogner | first27 = Holger
 | last28 = Bock | first28 = Franziska
 | last29 = Quirós-Tortós | first29 = Jairo
 | last30 = Angulo-Paniagua | first30 = Jam
 | last31 = Krishnamurthy | first31 = Satheesh
 | last32 = Harrison | first32 = John
 | last33 = To | first33 = Long Seng
 | title =  Selected 'Starter kit' energy system modelling data for selected countries in Africa, East Asia, and South America (#CCG, 2021)
 | journal = Data in Brief
 | date = 12 February 2022
 | volume = 42
 | page = 108021
 | doi = 10.1016/j.dib.2022.108021
 | pmid = 35341031
 | pmc = 8943422
 | bibcode = 2022DIB....4208021A
 | s2cid = 246709496}}</ref>

[[File:Regional nodes available for model generation using OSeMOSYS Global.png|right|thumb|Regional nodes available for model generation using the OSeMOSYS Global base{{nnbsp}}<ref name="barnes-etal-2022"/>{{rp|3}}]]  <!-- in preceding section to give better visual layout -->

The OSeMBE reference model covering western and central Europe was announced on 27 April 2018.<ref name="osembe-2018">
{{cite web
 | author = OSeMOSYS
 | title = The open source energy model base for the European Union (OSeMBE)
 | date = 2018
 | work = OSeMOSYS
 | location = Stockholm, Sweden
 | url = http://www.osemosys.org/osembe.html
 | access-date = 2018-04-30}}</ref><ref name="beltramo-2018">
{{cite mailing list
 | last1 = Beltramo | first1 = Agnese
 | title = first OSeMBE EU-28 model released
 | date = 27 April 2018
 | mailing-list = openmod-initiative@googlegroups.com
 | url = https://groups.google.com/forum/#!topic/openmod-initiative/LIdokK1UY5I
 | access-date = 2018-04-30}}</ref> The model uses the MathProg implementation of OSeMOSYS but requires a small [[Patch (computing)|patch]] first. 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 until mid-2020.

A 2021 paper reviews the OSeMOSYS community, its composition, and its governance activities. And also describes the use of OSeMOSYS in education and for building analytical capacity within developing countries.<ref name="niet-etal-2021"/>

====OSeMOSYS Global project====
The OSeMOSYS community launched the OSeMOSYS Global project in 2022 to create a global model and associated workflows. As of late{{nbhyph}}2022, OSeMOSYS Global is limited in scope to the electricity sector and the world system provided comprises 164{{nbsp}}countries separated by 265{{nbsp}}nodes.<ref name="barnes-etal-2022">
{{cite journal
 | last1 = Barnes | first1 = Trevor
 | last2 = Shivakumar | first2 = Abhishek
 | last3 = Brinkerink | first3 = Maarten
 | last4 = Niet | first4 = Taco
 | title = OSeMOSYS Global, an open-source, open data global electricity system model generator
 | date = 14 October 2022
 | journal = Scientific Data
 | volume = 9
 | issue = 623
 | page = 623
 | doi = 10.1038/s41597-022-01737-0
 | pmid = 36241673
 | pmc = 9568605
 | bibcode = 2022NatSD...9..623B
 | issn = 2052-4463
 | url = https://www.nature.com/articles/s41597-022-01737-0.pdf
 | access-date = 2022-10-14}}</ref>
{{clear}}

===PyPSA===
{| class="infobox" style="width: 28em"
|-
! style="width: 35%" | Project
| style="width: 55%" | PyPSA
|-
! Host
| Technische Universität Berlin
|-
! Status
| active
|-
! Scope/type
| electric power systems with sector coupling
|-
! Code license
| MIT
|-
! Language
| [[Python (programming language)|Python]]
|-
! Code website
| {{URL|http://pypsa.org}}
|-
! Project website
| {{URL|https://pypsa-meets-earth.github.io}}
|-
! Repository
| {{URL|https://github.com/PyPSA/PyPSA}}
|-
! Documentation
| {{URL|https://pypsa.readthedocs.io}}
|-
! Python package
| {{URL|https://pypi.org/project/pypsa}}
|-
! Mailing list
| {{URL|https://groups.google.com/g/pypsa}}
|-
! Chat
| {{URL|https://discord.gg/AnuJBk23FU}}
|-
! Discussion
| {{URL|https://forum.openmod.org/tag/pypsa}}
|}

PyPSA stands for Python for Power System Analysis. PyPSA is a free software toolbox for simulating and optimizing electric power systems and allied sectors.<ref name="brown-2022-video">
{{cite AV media
 | last1 = Brown | first1 = Tom
 | title = Open energy modelling: discussion and examples from PyPSA modelling for Europe
 | date = 28 October 2022
 | publisher = Technische Universität Berlin
 | location = Berlin, Germany
 | url = https://www.youtube.com/watch?v=HOH_eyUZcYc
 | access-date = 2022-11-07}} YouTube video. Duration 00:21:09. {{open access}}
</ref><ref name="brown-2022-slides">
{{cite book
 | last1 = Brown | first1 = Tom
 | title = Open energy modelling: discussion and examples from PyPSA modelling for Europe
 | date = 28 October 2022
 | publisher = Technische Universität Berlin
 | location = Berlin, Germany
 | url = https://nworbmot.org/energy/brown-openmod_za.pdf
 | access-date = 2022-11-07}} Accompanying slide deck. {{open access}}</ref> It supports conventional generation, variable wind and solar generation, electricity storage, [[Engineering:Power-to-X#Sector coupling|coupling]] to the natural gas, hydrogen, heat, and transport sectors, and hybrid alternating and direct current networks. Moreover, PyPSA is designed to scale well. The project is managed by the Institute for Automation and Applied Informatics (IAI), Karlsruhe Institute of Technology (KIT), [[Place:Karlsruhe|Karlsruhe]], Germany, although the project itself exists independently under its own name and accounts. The project maintains a website and runs an [[Electronic mailing list|email list]]. PyPSA itself is written in [[Python (programming language)|Python]] and uses the [[Pyomo]] library. The [[Source code|source code]] is hosted on [[GitHub]] and is also released periodically as a [[Python Package Index|PyPI]] package.

[[File:European power system infrastructure.png|alt=European power system map created by and prepared for energy system model runs with PyPSA-Eur.|left|thumb|[[European Union|European]] power system map created by and prepared for energy system model runs with PyPSA-Eur]]

The basic functionality of PyPSA is described in a 2018 paper. PyPSA bridges traditional steady-state power flow analysis software and full multi-period energy system models. It can be invoked using either non-linear power flow equations for system simulation or linearized approximations to enable the joint optimization of operations and investment across multiple periods. Generator ramping and multi-period up and down-times can be specified, DSM is supported, but demand remains [[Finance:Price elasticity of demand|price inelastic]].<ref name="brown-etal-2018-pypsa">
{{cite journal
 | last1 = Brown | first1 = Tom
 | last2 = Hörsch | first2 = Jonas
 | last3 = Schlachtberger | first3 = David
 | s2cid = 67101943
 | title = PyPSA: Python for Power System Analysis
 | date = 16 January 2018
 | journal = Journal of Open Research Software
 | volume = 6
 | issue = 1
 | pages = 4
 | doi = 10.5334/jors.188
 | doi-access=free
 | issn = 2049-9647
 | arxiv = 1707.09913
}}</ref>

A 2018 study examines potential synergies between [[Engineering:Power-to-X#sector coupling concepts|sector coupling]] and [[Finance:Electric power transmission|transmission]] reinforcement in a future European energy system constrained to reduce [[Physics:Carbon dioxide equivalent|carbon]] emissions by 95%. The PyPSA-Eur-Sec-30 model captures the [[Finance:Energy demand management|demand-side management]] potential of [[Engineering:Battery electric vehicle|battery electric vehicle]]s (BEV) as well as the role that [[Physics:Power-to-gas|power-to-gas]], long-term [[Physics:Thermal energy storage|thermal energy storage]], and related technologies can play. Results indicate that BEVs can smooth the daily variations in solar power while the remaining technologies smooth the synoptic and seasonal variations in both demand and renewable supply. Substantial buildout of the electricity grid is required for a least-cost configuration. More generally, such a system is both feasible and affordable. The underlying datasets are available from [[Zenodo]].<ref name="brown-etal-2018-synergies">
{{cite journal
 | last1 = Brown | first1 = Tom
 | last2 = Schlachtberger | first2 = David
 | last3 = Kies | first3 = Alexander
 | last4 = Schramm | first4 = Stefan
 | last5 = Greiner | first5 = Martin
 | s2cid = 55251011
 | title = Synergies of sector coupling and transmission reinforcement in a cost-optimised, highly renewable European energy system
 | date = 1 October 2018
 | journal = Energy
 | volume = 160
 | pages = 720–739
 | doi = 10.1016/j.energy.2018.06.222
 | issn = 0360-5442
 | bibcode = 2018Ene...160..720B
 | arxiv = 1801.05290
}} {{closed access}} Content identical arXiv [https://arxiv.org/abs/1801.05290v2 postprint].</ref>

{{as of|2018|01}}, PyPSA is used by more than a dozen research institutes and companies worldwide.<ref name="brown-etal-2018-pypsa"/>{{rp|2}} Some research groups have independently extended the software, for instance to model integer transmission expansion.<ref name="gorenstein-dedecca-etal-2017">
{{cite journal
 | last1 = Gorenstein Dedecca | first1 = João
 | last2 = Hakvoort | first2 = Rudi A
 | last3 = Herder | first3 = Paulien M
 | title = Transmission expansion simulation for the European Northern Seas offshore grid
 | date = 15 April 2017
 | journal = Energy
 | volume = 125
 | pages = 805–824
 | doi = 10.1016/j.energy.2017.02.111
 | issn = 0360-5442
 | doi-access = free
 | bibcode = 2017Ene...125..805G
}}</ref>

In 2020, the PyPSA‑Eur‑Sec model for Europe was used to analyze several Paris Agreement Compatible Scenarios for Energy Infrastructure{{nnbsp}}<ref name="zeyen-etal-2020">
{{cite book
 | last1 = Zeyen | first1 = Elisabeth
 | last2 = Victoria | first2 = Marta
 | last3 = Brown | first3 = Tom
 | title = PAC scenarios with PyPSA‑Eur‑Sec — Presentation
 | date = 15 October 2020
 | publisher =
 | location =
 | url = https://www.pac-scenarios.eu/fileadmin/user_upload/KIT_Aarhus_University_PAC_scenarios_with_PyPSA-Eur-Sec_15oct20.pdf
 | access-date = 2021-02-25
}} Presentation to 4th PAC scenario workshop. {{open access}}</ref> and determined that early action should pay off.<ref name="victoria-etal-2020">
{{cite journal
 | last1 = Victoria | first1 = Marta
 | last2 = Zhu | first2 = Kun
 | last3 = Brown | first3 = Tom
 | last4 = Andresen | first4 = Gorm B
 | last5 = Greiner | first5 = Martin
 | title = Early decarbonisation of the European energy system pays off
 | date = 4 December 2020
 | journal = Nature Communications
 | volume = 11
 | issue = 1
 | pages = 6223
 | doi = 10.1038/s41467-020-20015-4
 | doi-access=free
 | pmid = 33277493
 | pmc = 7718908
 | arxiv = 2004.11009
 | bibcode = 2020NatCo..11.6223V
 | issn = 2041-1723
}}</ref>

On 9{{nbsp}}January 2019, the project released an interactive web-interfaced "toy" model, using the [[Organization:COIN-OR#CBC|Cbc]] solver, to allow the public to experiment with different future costs and technologies.<ref name="whobs-server">
{{cite web
 | author = ((PyPSA contributors))
 | title = Meet constant demand from wind+solar+storage with zero-direct-emissions using your own assumptions
 | work = PyPSA project
 | url = https://whobs.org
 | access-date = 2019-01-07
}} Caveats apply.
</ref><ref name="whobs-github">
{{cite web
 | author = PyPSA constributors
 | title = Online optimisation tool for wind+solar+storage systems: PyPSA/whobs-server
 | work = PyPSA Project
 | url = https://github.com/PyPSA/whobs-server
 | access-date = 2019-01-07
}} GitHub repository.</ref> The site was relaunched on 5{{nbsp}}November 2019 with some internal improvements, a new URL, and faster solver now completing in about {{val|12|u=second}}.<ref name="brown-2019">
{{cite web
 | last1 = Brown | first1 = Tom
 | title = model.energy: live optimisation of electricity systems
 | date = 5 November 2019
 | work = Open Energy Modelling Initiative mailing list
 | url = https://groups.google.com/g/openmod-initiative/c/COkLmrs2t_A/m/T2p-AZC_AQAJ
 | access-date = 2021-02-23
}} New URL: {{URL|https://model.energy}}
</ref> A newer version now uses the [[Organization:HiGHS optimization solver|HiGHS]] solver.<ref name="brown-toy-model">
{{cite web
 | last1 = Brown | first1 = Tom
 | title = Build your own zero-emission electricity supply
 | url = https://model.energy
 | access-date = 2022-07-22
}} Web interface to highly simplified PyPSA model.</ref>

[[File:Africa map showing population and grid infrastructure.png|right|thumb|Africa with population densities in red and existing high-voltage transmission networks in blue{{nnbsp}}<ref name="parzen-etal-2022"/>]]  <!-- in preceding section to give better visual layout -->

{{anchor|PyPSA-Server}}
During September 2021, PyPSA developers announced the PyPSA{{nbhyph}}Server project to provide a web interface to a simplified version of their PyPSA{{nbhyph}}Eur{{nbhyph}}Sec [[Engineering:Power-to-X|sector{{nbhyph}}coupled]] European model.<ref name="brown-2021">
{{cite web
 | last1 = Brown | first1 = Tom
 | title = PyPSA-Server: customisable energy scenarios, solved while you wait
 | date = 14 September 2021
 | work = Open Energy Modelling Initiative mailing list
 | url = https://groups.google.com/g/openmod-initiative/c/eYxN7nHnSXE/m/R-Hi8ungAwAJ
 | access-date = 2021-02-23
}}</ref><ref name="pypsa-project-2021">
{{cite web
 | author = PyPSA project
 | title = PyPSA-Eur-Sec optimisation server
 | date = September 2021
 | work = Technische Universität Berlin
 | location = Berlin, Germany
 | url = https://model.energy/scenarios/
 | access-date = 2021-09-28
}}
</ref> Users need not install software and can define fresh scenarios "by difference" using a forms{{nbhyph}}based webpage. Previously run scenarios are stored for future reference. The implementation {{as of|2021|10|lc=yes}} is essentially proof{{nbhyph}}of{{nbhyph}}concept.

In late‑2021, PyPSA{{nbhyph}}Eur developers reported their investigation into integrated high-voltage electricity and [[Physics:Hydrogen economy|hydrogen grid]] expansion options for Europe and the United Kingdom and the impact of the kind of trade{{nbhyph}}offs that might stem from limited public acceptance of new infrastructure.<ref name="brown-etal-2021">
{{cite book
 | last1 = Brown | first1 = Tom
 | last2 = Victoria | first2 = Marta
 | last3 = Neumann | first3 = Fabian
 | last4 = Zeyen | first4 = Lisa
 | last5 = Frysztacki | first5 = Martha
 | title = Can a hydrogen network replace electricity transmission network expansion in a climate-neutral scenario for Europe?
 | date = 27 October 2021
 | publisher = Technische Universität Berlin
 | location = Berlin, Germany
 | url = https://nworbmot.org/energy/brown-empe.pdf
 | access-date = 2021-10-27
}}  Presentation to the EMP‑E 2021 conference. {{open access}}</ref><ref name="neumann-etal-2022">
{{cite journal
 | last1 = Neumann | first1 = Fabian
 | last2 = Zeyen | first2 = Elisabeth
 | last3 = Victoria | first3 = Marta
 | last4 = Brown | first4 = Tom
 | title = The potential role of a hydrogen network in Europe
 | journal = Joule
 | year = 2023
 | volume = 7
 | issue = 8
 | pages = 1793–1817
 | doi = 10.1016/j.joule.2023.06.016
 | arxiv = 2207.05816
 | bibcode = 2023Joule...7.1793N
 | s2cid = 257496304
}} {{open access}}
</ref> Subsequent work added [[Social:Experience curve effects|endogenous learning]] effects and identified steeper technology cost reductions than those anticipated by the European Commission.<ref name="zeyen-etal-2023">
{{cite journal
 | last1 = Zeyen | first1 = Elisabeth
 | last2 = Victoria | first2 = Marta
 | last3 = Brown | first3 = Tom
 | title = Endogenous learning for green hydrogen in a sector-coupled energy model for Europe
 | journal = Nature Communications
 | date = 2023
 | volume = 14
 | issue = 1
 | page = 3743
 | doi = 10.1038/s41467-023-39397-2
 | pmid = 37353489
 | pmc = 10290158
 | arxiv = 2205.11901
 | bibcode = 2023NatCo..14.3743Z
}} {{open access}}</ref>  Work published in 2024 integrated PyPSA{{nbhyph}}Eur with the global energy supply chain model TRACE and highlighted the need to coordinate infrastructure policies and import strategies.<ref name="neumann-etal-2024">
{{cite arXiv
 | last1 = Neumann | first1 = Fabian
 | last2 = Hampp | first2 = Johannes
 | last3 = Brown | first3 = Tom
 | title = Energy imports and infrastructure in a carbon-neutral European energy system — Preprint
 | date = 5 April 2024
 | class = physics.soc-ph
 | eprint = 2404.03927
 }}
</ref>

A{{nbsp}}December{{nbsp}}2021 study and ongoing work deployed a PyPSA‑PL model to assess policy options for Poland.<ref name="berus-2021">
{{cite book
 | last1 = Berus | first1 = Patryk
 | title = Failing to switch from coal to wind and solar could cost every Polish family 500 PLN per year by 2030, report — Press release
 | date = 8 December 2021
 | publisher = Instrat Foundation
 | location = Warsaw, Poland
}}
</ref><ref name="czyzak-etal-2021a">
{{cite book
 | last1 = Czyżak | first1 = Paweł
 | last2 = Wrona | first2 = Adrianna
 | last3 = Borkowski | first3 = Michał
 | title = The missing element: energy security considerations — Instrat Policy Paper 09/2021
 | date = December 2021
 | publisher = Instrat Foundation
 | location = Warsaw, Poland
 | isbn = 978-83-962333-3-2
 | url = https://instrat.pl/wp-content/uploads/2021/12/Instrat-Missing-element.pdf
 | access-date = 2021-12-08
}} {{open access}}
</ref><ref name="czyzak-etal-2021b">
{{cite book
 | last1 = Czyżak | first1 = Paweł
 | last2 = Sikorski | first2 = Maciej
 | last3 = Wrona | first3 = Adrianna
 | title = What's next after coal? RES potential in Poland — Instrat Policy Paper 06/2021
 | date = June 2021
 | publisher = Instrat Foundation
 | location = Warsaw, Poland
 | isbn = 978-83-959296-9-4
 | url = https://instrat.pl/wp-content/uploads/2021/06/Instrat-What-next-after-coal-v.1.2.pdf
 | access-date = 2021-12-20
}} {{open access}}
</ref><ref name="kubiczek-2023">
{{cite web
 | last1 = Kubiczek | first1 = Patryk
 | title = Modelling the Polish energy system — new findings of PyPSA-PL
 | date = 19 January 2023
 | work = Instrat
 | location = Warsaw, Poland
 | url = https://instrat.pl/en/modelling-energy-system-opinion/
 | access-date = 2023-01-23
}} Blog. {{open access}}
</ref>  [[Organization:University of Edinburgh|Edinburgh University]] researchers published an independent power system model for Britain named PyPSA{{nbhyph}}GB in 2024, together with assessments of official net{{nbhyph}}zero Future Energy Scenarios (FES) from the UK National Grid.<ref name="lyden-etal-2024">
{{cite journal
 | last1 = Lyden | first1 = Andrew
 | last2 = Sun | first2 = Wei
 | last3 = Struthers | first3 = Iain
 | last4 = Franken | first4 = Lukas
 | last5 = Hudson | first5 = Seb
 | last6 = Wang | first6 = Yifan
 | last7 = Friedrich | first7 = Daniel
 | title = PyPSA-GB: an open-source model of Great Britain's power system for simulating future energy scenarios
 | date = 1 May 2024
 | journal = Energy Strategy Reviews
 | volume = 53
 | pages = 101375
 | doi = 10.1016/j.esr.2024.101375 <!--  | doi-access = free -->
 | issn = 2211-467X
 | url = https://www.sciencedirect.com/science/article/pii/S2211467X24000828/pdfft
 | access-date = 2024-08-23
| doi-access = free
 | bibcode = 2024EneSR..5301375L
 }}  {{open access}}  {{URL|1=https://github.com/andrewlyden/PyPSA-GB|2=GitHub repository}}
</ref>

Several PyPSA maintainers announced a new [[Engineering:Nonprofit organization|non{{nbhyph}}profit]] [[Finance:Startup company|startup]] in June{{nbsp}}2023 to provide consulting services using PyPSA.<ref name="parzen-2023">
{{cite web
 | last1 = Parzen | first1 = Maximilian
 | title = Launch of "Open Energy Transition" and article "'Black-box' US energy planning hinders renewable energy development"
 | date = 27 June 2023
 | work = Google Groups
 | url = https://groups.google.com/g/openmod-initiative/c/JTcXVpccD_o/m/YWvMDKtOBAAJ
 | access-date = 2023-07-01
}}  Mailing list posting.  The registered name is Open Energy Transition{{nbsp}}gGmbH.
</ref>

==== PyPSA meets Earth initiative ====

The PyPSA meets Earth initiative arose in October{{nbsp}}2022 as a means of gathering together several historically disjoint PyPSA applications.<ref name="pypsa-meets-earth-team-ongoing">
{{cite web
 | author = PyPSA team
 | title = PyPSA meets Earth — an open energy-system model initiative for our Earth
 | url = https://pypsa-meets-earth.github.io
 | access-date = 2022-10-18
}}
</ref> One key strand is the PyPSA{{nbhyph}}Africa project (previously PyPSA-meets-Africa), launched some months earlier to provide a single model and dataset spanning the [[Earth:Africa|African continent]].<ref name="pypsa-meets-africa-2021">
{{cite book
 | author = PyPSA meets Africa
 | title = PyPSA meets Africa — Policy brief 07/2021
 | date = July 2021
 | publisher = PyPSA meets Africa
}}
</ref><ref name="pypsa-meets-africa-website">
{{cite web
 | author = PyPSA meets Africa
 | title = PyPSA meets Africa: an open source energy system model initiative for Africa
 | work = PyPSA meets Africa
 | url = https://pypsa-meets-africa.github.io
 | access-date = 2021-08-07
}}
</ref> A{{nbsp}}July{{nbsp}}2022 webinar co{{nbhyph}}hosted by CPEEL, Nigeria advanced this agenda.<ref name="pypsa-meets-africa-and-cpeel-2022">
{{cite AV media
 | author1 = PyPSA-meets-Africa
 | author2 = CPEEL
 | title = Open data, open source sector‑coupled models, and open solvers to fast‑track the Earth energy transition together — Webinar
 | date = 7 July 2022
 | publisher = PyPSA-meets-Africa and Centre for Petroleum Energy Economics and Law (CPEEL), University of Ibadan, Nigeria
 | url = https://www.youtube.com/watch?v=0IB0n00y7CA
 | access-date = 2022-07-12
}} YouTube video. Duration 00:35:15. {{open access}}
</ref><ref name="fioriti-and-schumm-2022">
{{cite book
 | last1 = Fioriti | first1 = Davide
 | last2 = Schumm | first2 = Leon
 | title = Open data, open source sector‑coupled models, and open solvers to fast‑track the Earth energy transition together — Presentation
 | date = 7 July 2022
 | publisher = PyPSA-meets-Africa
 | url = https://drive.google.com/file/d/1HzbPga0hBrw8fZFgIq5xFyN-hdi6y2qE
 | access-date = 2022-07-18
}} {{open access}}
</ref> The first research paper, released in 2022, examines various pathways for Africa to be net{{nbsp}}zero by 2060{{nbsp}}{{mdash}} with solar power and battery storage expected to be the predominant technologies.<ref name="parzen-etal-2022">
{{cite journal
 | last1 = Parzen | first1 = Maximilian
 | last2 = Abdel-Khalek | first2 = Hazem
 | last3 = Fedorova | first3 = Ekaterina
 | last4 = Mahmood | first4 = Matin
 | last5 = Frysztacki | first5 = Martha Maria
 | last6 = Hampp | first6 = Johannes
 | last7 = Franken | first7 = Lukas
 | last8 = Schumm | first8 = Leon
 | last9 = Neumann | first9 = Fabian
 | last10 = Poli | first10 = Davide
 | last11 = Kiprakis | first11 = Aristides
 | last12 = Fioriti | first12 = Davide
 | title = PyPSA-Earth. A new global open energy system optimization model demonstrated in Africa
 | journal = Applied Energy
 | year = 2023
 | volume = 341
 | page = 121096
 | doi = 10.1016/j.apenergy.2023.121096
 | arxiv = 2209.04663
 | bibcode = 2023ApEn..34121096P
 | s2cid = 252199399
}} {{open access}}
</ref>

Another key strand of the initiative is the PyPSA{{nbhyph}}Earth project which seeks to create a global energy systems model at high spatial and temporal resolution.<ref name="pypsa-meets-earth-team-ongoing"/> The project hopes to encourage large{{nbhyph}}scale collaboration by providing software and processes that can capture the global energy system and thus also any subset of it. The codebase currently supports system integration studies that draw together electricity generation, storage, and transmission expansion. And a sector-coupled version of the framework is in development that will also offer a choice between myopic decision{{nbhyph}}taking or perfect foresight.<ref name="parzen-etal-2022"/><ref name="abdel-khalek-etal-2024">
{{cite web
 | last1 = Abdel-Khalek | first1 = Hazem
 | last2 = Schumm | first2 = Leon
 | last3 = Jalbout | first3 = Eddy
 | last4 = Parzen | first4 = Maximilian
 | last5 = Schauß | first5 = Caspar
 | last6 = Fioriti | first6 = Davide
 | title = Sector-coupled PyPSA-Earth, a global open-source multi-energy system model — Preprint
 | date = 29 February 2024
 | journal =
 | volume =
 | issue =
 | pages =
 | doi = 10.2139/ssrn.4743242
 | issn =
 | url = https://doi.org/10.2139/ssrn.4743242
 | access-date = 2024-08-05
}}  [[Social:Social Science Research Network|SSRN repository]].  Account needed for access.
</ref>

{{clear}}

===REMix===
{| class="infobox" style="width: 28em"
|-
! style="width: 35%" | Project
| style="width: 55%" | REMix
|-
! Host
| Deutsches Zentrum für Luft- und Raumfahrt e.V.
|-
! Status
| active
|-
! Scope/type
| system planning and investment in electricity, heat, mobility, gas sectors
|-
! Code license
| BSD 3-Clause
|-
! Language
| [[Python (programming language)|Python]]/[[Software:General algebraic modeling system|GAMS]]
|-
! Website
| {{URL|https://dlr-ve.gitlab.io/esy/remix/framework/}}
|-
! Repository
| {{URL|https://gitlab.com/dlr-ve/esy/remix/framework/}}
|-
! Mailing list
| {{URL|https://forum.openmod.org/tag/remix}}
|}

REMix stands for "Renewable Energy Mix". It is an open source [[Software framework|framework]] developed by the [[Organization:German Aerospace Center|German Aerospace Center]] for setting up [[Linear programming|linear]] or mixed integer [[Optimization problem|optimization]] models written in [[Software:General Algebraic Modeling System|GAMS]].<ref name="wetzel-etal-2023">{{cite journal
 | first1 = Manuel
 | last1 = Wetzel
 | first2 = Hans Christian 
 | last2 = Gils
 | first3 = Valentin 
 | last3 = Bertsch
 | year = 2023
 | title = Green energy carriers and energy sovereignty in a climate neutral European energy system
 | journal = Renewable Energy
 | volume = 210
 | pages = 591–603
 | doi = 10.1016/j.renene.2023.04.015
 | bibcode = 2023REne..210..591W
 | url = https://www.sciencedirect.com/science/article/pii/S0960148123004639
 | access-date = 2024-09-13
 }}</ref> A framework is understood as a collection of mutually compatible [[Source code|source code]]s required for a particular model, which can be combined in a modular manner. In this way, the same modeling concepts, along with the associated [[Source code|source code]], can be reutilized to address various content focuses based on a common set of available model features.

REMix is developed for applications in energy system modeling studies. It is typically used to set up energy system optimization models, although potential applications beyond energy research are conceivable. In particular, these energy system optimization models are often characterized as bottom-up models in terms of explicitly modeling different technologies. In addition, these models are resolved on a spatial and a temporal dimension.
thumb

In practical terms, the framework allows for modeling competition between technologies that can serve the same purpose, such as power generation, while also providing insights into when and where a specific technology is required. Additionally, it can be applied to [[Transportation theory (mathematics)|transportation problems]], where the optimal exchange of a commodity between at least two distinct regions needs to be determined. Furthermore, it addresses storage problems, where the optimal balance between production and consumption at different points in time is calculated.<ref name="wetzel-etal-2024">{{cite journal
 | first1 = Manuel
 | last1 = Wetzel
 | first2 = Eugenio 
 | last2 = Salvador Arellano Ruiz
 | first3 = Francesco 
 | last3 = Witte
 | first4 = Jens 
 | last4 = Schmugge 
 | first5 = Shima 
 | last5 = Sasanpour 
 | first6 = Madhura 
 | last6 = Yeligeti 
 | first7 = Fabia 
 | last7 = Miorelli 
 | first8 = Jan 
 | last8 = Buschmann 
 | first9 = Karl-Kiên 
 | last9 = Cao and 
 | first10 = Niklas 
 | last10 = Wulff 
 | first11 = Hedda 
 | last11 = Gardian 
 | first12 = Alexander 
 | last12 = Rubbert 
 | first13 = Benjamin 
 | last13 = Fuchs
 | first14 = Yvonne 
 | last14 = Scholz
 | first15 = Hans Christian 
 | last15 = Gils
 | year = 2024
 | title = REMix: A GAMS-based framework for optimizing energy system models
 | journal = Journal of Open Source Software
 | volume = 9
 | issue = 99
 | pages = 6330
 | doi = 10.21105/joss.06330
 | doi-access = free 
 | bibcode = 2024JOSS....9.6330W
 }}</ref>

REMix offers several key features that make it a robust tool for energy system modeling. It is designed to handle large-scale models with high [[Spatial resolution|spatial]] and technological resolutions, making it suitable for complex analyses. The framework also incorporates path optimization, allowing for multi-year analyses and strategic planning over extended periods. Ongoing work deals with very large instances involving path optimization using the parallel solver [https://github.com/NCKempke/PIPS-IPMpp PIPS-IPM++]. A notable feature is its custom accounting capability, provided through the indicator module, which enables flexible definitions of what contributes to the objective functions. Additionally, REMix supports flexible modeling, offering multiple approaches to integrate and model technologies, allowing users to tailor the framework to their specific needs. Finally, it supports [[Multi-objective optimization|multi-criteria optimization]], where, beyond cost minimization, additional factors such as ecological impacts or resilience indicators can be considered in the objective function, providing a more comprehensive approach to system [[Optimization problem|optimization]].

In the past, the model has been used to investigate a wide range of research questions. In addition to detailed analyses of the integration of renewable energies into the electricity system,<ref name="gils-etal-2017">{{cite journal
 | first1 = Hans Christian 
 | last1 = Gils
 | first2 = Yvonne
 | last2 = Scholz
 | first3 = Thomas
 | last3 = Pregger
 | first4 = Diego Luca
 | last4 = de Tena
 | first5 = Dominik
 | last5 = Heide 
 | year = 2017
 | title = Integrated modelling of variable renewable energy-based power supply in Europe
 | journal = Energy
 | volume = 123
 | pages = 173–188
 | doi = 10.1016/j.energy.2017.01.115
 | bibcode = 2017Ene...123..173G
 | url = https://www.sciencedirect.com/science/article/pii/S0360544217301238
 | access-date = 2024-09-13
 }}</ref> for example, the role of [[Chemistry:Hydrogen|hydrogen]] in the [[Energy system|energy system]] of the future has also been examined.<ref name="gils-etal-2021">{{cite journal
 | first1 = Hans Christian 
 | last1 = Gils
 | first2 = Hedda 
 | last2 = Gardian
 | first3 = Jens
 | last3 = Schmugge
 | year = 2021
 | title = Interaction of hydrogen infrastructures with other sector coupling options towards a zero-emission energy system in German
 | journal = Renewable Energy
 | volume = 180
 | pages = 140–156
 | doi = 10.1016/j.renene.2021.08.016
 | bibcode = 2021REne..180..140G
 | url = https://www.sciencedirect.com/science/article/pii/S0960148121011769
 | access-date = 2024-09-13
 }}</ref><ref name="sasanpour-etal-2021">{{cite journal
 | first1 = Shima 
 | last1 = Sasanpour  
 | first2 = Hans Christian 
 | last2 = Gils
 | first3 = Karl-Kiên 
 | last3 = Cao and
 | first4 = Patrick
 | last4 = Jochem
 | year = 2021
 | title = Strategic policy targets and the contribution of hydrogen in a 100% renewable European power system
 | journal = Energy Reports
 | volume = 7
 | pages = 4595–4608
 | doi = 10.1016/j.egyr.2021.07.005
 | bibcode = 2021EnRep...7.4595S 
 | url = https://www.sciencedirect.com/science/article/pii/S2352484721004704
 | access-date = 2024-09-13
 }}</ref>

For the purpose of validating the REMix model, [[Organization:German Aerospace Center|German Aerospace Center]] has participated in various model comparisons.<ref name="gils-etal-2022">{{cite journal
 | first1 = Hans Christian 
 | last1 = Gils
 | first2 = Hedda 
 | last2 = Gardian
 | first3 = Martin
 | last3 = Kittel
 | first4 = Wolf-Peter
 | last4 = Schill
 | first5 = Alexander
 | last5 = Zerrahn
 | first6 = Alexander
 | last6 = Murmann
 | first7 = Jann
 | last7 = Launer
 | first8 = Alexander
 | last8 = Fehler
 | first9 = Felix
 | last9 = Gaumnitz
 | first10 = Jonas 
 | last10 = van Ouwerkerk
 | first11 = Christian
 | last11 = Bußar
 | first12 = Jennifer
 | last12 = Mikurda
 | first13 = Laura
 | last13 = Torralba-Díaz
 | first14 = Tomke
 | last14 = Janßen
 | first15 = Christine
 | last15 = Krüger
 | year = 2022
 | title = Modeling flexibility in energy systems — comparison of power sector models based on simplified test cases
 | journal = Renewable and Sustainable Energy Reviews
 | volume = 158
 | pages = 111995
 | doi = 10.1016/j.rser.2021.111995
 | bibcode = 2022RSERv.15811995G
 | url = https://www.sciencedirect.com/science/article/pii/S1364032121012582
 | access-date = 2024-09-13
 }}</ref>

{{clear}}

===TEMOA===
{| class="infobox" style="width: 28em"
|-
! style="width: 35%" | Project
| style="width: 55%" | TEMOA
|-
! Host
| [[Organization:North Carolina State University|North Carolina State University]]
|-
! Key people
| Joseph DeCarolis
|-
! Status
| active
|-
! Scope/type
| system planning
|-
! Code license
| [[Software:GNU General Public License|GPLv2+]]
|-
! Language
| [[Python (programming language)|Python]]
|-
! Website
| {{URL|https://temoacloud.com}}
|-
! Repository
| {{URL|https://github.com/TemoaProject/temoa/}}
|-
! Mailing list
| {{URL|https://groups.google.com/g/temoa-project}}
|}

TEMOA stands for Tools for Energy Model Optimization and Analysis. The software is being developed by the Department of Civil, Construction, and Environmental Engineering, [[Organization:North Carolina State University|North Carolina State University]], Raleigh, North Carolina, USA. The project runs a website and a forum. The [[Source code|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|version control]] to publicly archive [[Source code|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
 | s2cid = 59143900
 | year = 2012
 | title = The case for repeatable analysis with energy economy optimization models
 | journal = Energy Economics
 | volume = 34
 | issue = 6
 | pages = 1845–1853
 | doi = 10.1016/j.eneco.2012.07.004
 | arxiv = 2001.10858
 | bibcode = 2012EneEc..34.1845D
 | url = http://temoaproject.org/publications/DeCarolis_etal_2012.pdf
 | access-date = 2016-07-08
 | archive-date = 19 April 2016
 | archive-url = https://web.archive.org/web/20160419213356/http://temoaproject.org/publications/DeCarolis_etal_2012.pdf
 | url-status = dead
 }}</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 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
 | bibcode = 2013EneEc..40..339H
 | url = http://www4.ncsu.edu/~jfdecaro/papers/Hunter_etal_2013.pdf
 | access-date = 2016-07-08
 | archive-date = 9 August 2017
 | archive-url = https://web.archive.org/web/20170809153955/http://www4.ncsu.edu/~jfdecaro/papers/Hunter_etal_2013.pdf
 | url-status = dead
 }}</ref> TEMOA is "strongly influenced by the well-documented [[Earth: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
 | archive-date = 20 April 2016
 | archive-url = https://web.archive.org/web/20160420031020/http://temoaproject.org/publications/DeCarolis_IEW2010_paper.pdf
 | url-status = dead
 }}</ref>{{rp|4}}

TEMOA forms the basis of the Open Energy Outlook (OEO) research project spanning 2020–2022. The OEO project utilizes open source tools and open data to explore deep decarbonization policy options for the United States.<ref name="decarolis-etal-2020">
{{cite journal
 | last1 = DeCarolis | first1 = Joseph F
 | last2 = Jaramillo | first2 = Paulina
 | last3 = Johnson | first3 = Jeremiah X
 | last4 = McCollum | first4 = David L
 | last5 = Trutnevyte | first5 = Evelina
 | last6 = Daniels | first6 = David C
 | last7 = Akın-Olçum | first7 = Gökçe
 | last8 = Bergerson | first8 = Joule
 | last9 = Cho | first9 = Soolyeon
 | last10 = Choi | first10 = Joon-Ho
 | last11 = Craig | first11 = Michael T
 | last12 = de Queiroz | first12 = Anderson R
 | last13 = Eshraghi | first13 = Hadi
 | last14 = Galik | first14 = Christopher S
 | last15 = Gutowski | first15 = Timothy G
 | last16 = Haapala | first16 = Karl R
 | last17 = Hodge | first17 = Bri-Mathias
 | last18 = Hoque | first18 = Simi
 | last19 = Jenkins | first19 = Jesse D
 | last20 = Jenn | first20 = Alan
 | last21 = Johansson | first21 = Daniel JA
 | last22 = Kaufman | first22 = Noah
 | last23 = Kiviluoma | first23 = Juha
 | last24 = Lin | first24 = Zhenhong
 | last25 = MacLean | first25 = Heather L
 | last26 = Masanet | first26 = Eric
 | last27 = Masnadi | first27 = Mohammad S
 | last28 = McMillan | first28 = Colin A
 | last29 = Nock | first29 = Destenie S
 | last30 = Patankar | first30 = Neha
 | last31 = Patino-Echeverri | first31 = Dalia
 | last32 = Schivley | first32 = Greg
 | last33 = Siddiqui | first33 = Sauleh
 | last34 = Smith | first34 = Amanda D
 | last35 = Venkatesh | first35 = Aranya
 | last36 = Wagner | first36 = Gernot
 | last37 = Yeh | first37 = Sonia
 | last38 = Zhou | first38 = Yuyu
 | title = Leveraging open-source tools for collaborative macro-energy system modeling efforts
 | date = 16 December 2020
 | journal = Joule
 | volume = 4
 | issue = 12
 | pages = 2523–2526
 | doi = 10.1016/j.joule.2020.11.002
 | doi-access=free
 | bibcode = 2020Joule...4.2523D
 | s2cid = 229492155
 | issn = 2542-4785}}</ref><ref name="decarolis-2020">
{{cite AV media
 | last1 = DeCarolis | first1 = Joe
 | title = An Open Energy Outlook for the United States powered by TEMOA
 | publisher = NC State University
 | location = Raleigh, North Carolina, USA
 | date = 24 December 2020
 | url = https://www.youtube.com/watch?v=MKDYNDsqq-U
 | access-date = 2021-02-26}} YouTube video, duration 00:15:16. {{open access}}</ref>

{{anchor|TEMOA-interactive-interface}}
From mid{{nbhyph}}2021, an interactive interface located on the main website allows registered users to manipulate scenario data locally, upload structured [[Software:SQLite|SQLite]] files, and then run these scenarios using the TEMOA software.<ref name="decarolis-2021">
{{cite AV media
 | last1 = DeCarolis | first1 = Joe
 | title = Temoa on the cloud
 | date = 22 September 2021
 | publisher = North Carolina State University
 | location = Raleigh, North Carolina, USA
 | url = https://www.youtube.com/watch?v=fxYO_kIs364
 | access-date = 2021-09-28}} YouTube video, duration 00:10:03.</ref><ref name="temoa-cloud-model">
{{cite web
 | title = TEMOA cloud model
 | url = http://model.temoacloud.com
 | access-date = 2021-10-01}} User registration is required.</ref> The service also provides some limited data visualization and project management functionality.

{{clear}}

==Specialist models==
This section lists specialist modeling frameworks that cover particular aspects of an energy system in more detail than would normally be convenient or feasible with more general frameworks.

=== RAMP ===

{| class="infobox" style="width: 28em"
  |-
  ! style="width: 35%" | Project
  | style="width: 55%" | RAMP
  |-
  ! Host
  | [[Organization:Delft University of Technology|TU Delft]]
  |-
  ! Status
  | active
  |-
  ! Scope/type
  | synthetic demand profiles
  |-
  ! Code license
  | [[Software:European Union Public Licence#Version 1.2|EUPL-1.2]]
  |-
  ! Language
  | [[Python (programming language)|Python]]
  |-
  ! Website
  | {{URL|https://rampdemand.org}}
  |-
  ! Repository
  | {{URL|https://github.com/RAMP-project/RAMP}}
  |-
  ! Documentation
  | {{URL|https://rampdemand.readthedocs.io/}}
  |-
  ! Python package
  | {{URL|https://pypi.org/project/rampdemand/}}
  |-
  ! Chat
  | {{URL|1=https://app.gitter.im/#/room/#RAMP-project_community:gitter.im|2=Gitter/RAMP-project/community}}
|}

[[File:Example output from RAMP demand profile simulation software.svg|thumb|Example output from the RAMP demand profile simulation software]]

RAMP is an open-source software suite for the [[Stochastic simulation|stochastic simulation]] of user{{nbhyph}}driven energy demand [[Time series|time series]] based on few simple inputs. For example, a minimal definition of a user type{{nbsp}}{{emdash}} say, a{{nbsp}}particular category of household{{nbsp}}{{emdash}} requires only information about which energy-consuming devices they own, when they tend to use them on any typical day, and for how long in total. The software then leverages stochasticity to make up for the absence of more detailed information and to include the unpredictability of human behavior.

The RAMP software can then generate synthetic data wherever metered data does not exist, such as when designing systems in remote areas{{nnbsp}}<ref name="lombardi-etal-2019">
{{cite journal
 | last1 = Lombardi | first1 = Francesco
 | last2 = Balderrama | first2 = Sergio
 | last3 = Quoilin | first3 = Sylvain
 | last4 = Colombo | first4 = Emanuela
 | date = 15 June 2019
 | journal = Energy
 | volume = 177
 | pages = 433–444
 | doi = 10.1016/j.energy.2019.04.097
 | title = Generating high-resolution multi-energy load profiles for remote areas with an open-source stochastic model
 | bibcode = 2019Ene...177..433L
 | url = https://www.sciencedirect.com/science/article/abs/pii/S0360544219307303
 | access-date = 2024-06-19
| hdl = 11311/1087101
 | hdl-access = free
 }}  {{closed access}}
</ref> or when looking forward to future electric-vehicle fleets.<ref name="mangipinto-etal-2022">
{{cite journal
 | last1 = Mangipinto | first1 = Andrea
 | last2 = Lombardi | first2 = Francesco
 | last3 = Sanvito | first3 = Francesco Davide
 | last4 = Pavičević | first4 = Matija
 | last5 = Quoilin | first5 = Sylvain
 | last6 = Colombo | first6 = Emanuela
 | date = 15 April 2022
 | journal = Applied Energy
 | volume = 312
 | pages = 118676
 | doi = 10.1016/j.apenergy.2022.118676
 | title = Impact of mass-scale deployment of electric vehicles and benefits of smart charging across all European countries
 | bibcode = 2022ApEn..31218676M
 | url = https://www.sciencedirect.com/science/article/pii/S0306261922001416
 | access-date = 2024-06-19
| hdl = 11311/1218999
 | hdl-access = free
 }}  {{open access}}
</ref>  The limited data requirements also allow for a greater flexibility in scenario selection and development than similar but more data-intensive characterizations.<ref name="lombardi-etal-2024">
{{cite journal
 | last1 = Lombardi | first1 = Francesco
 | last2 = Duc | first2 = Pierre-François
 | last3 = Tahavori | first3 = Mohammad Amin
 | last4 = Sanchez-Solis | first4 = Claudia
 | last5 = Eckhoff | first5 = Sarah
 | last6 = Hart | first6 = Maria CG
 | last7 = Sanvito | first7 = Francesco
 | last8 = Ireland | first8 = Gregory
 | last9 = Balderrama | first9 = Sergio
 | last10 = Kraft | first10 = Johann
 | last11 = Dhungel | first11 = Gokarna
 | last12 = Quoilin | first12 = Sylvain
 | title = RAMP: stochastic simulation of user-driven energy demand time series
 | date = 12 June 2024
 | journal = Journal of Open Source Software
 | volume = 9
 | issue = 98
 | pages = 6418
 | doi = 10.21105/joss.06418
 | issn = 2475-9066
 | doi-access = free
 | bibcode = 2024JOSS....9.6418L
 }}  {{open access}}
</ref>

RAMP has been used in scientific research for a{{nbsp}}variety of [[Use case|use case]]s, including the generation of electricity demand profiles for remote or residential communities, domestic hot water usage, cooking practices, and electric mobility.  Associated geographical scales can range from neighborhoods to continents.

RAMP has several dozen users worldwide.  In the early{{nbhyph}}2020s, the software became part of a multi-institution software development effort, supported by TU Delft, VITO, Reiner Lemoine Institute, University of Liège, Leibniz University Hannover, and Universidad Mayor de{{nbsp}}San Simón.<ref name="lombardi-etal-2024"/>

RAMP runs on [[Python (programming language)|Python]] and requires input in tabular form. Graphical user interfaces (GUI) are available, allowing the software to be run from web browsers.<ref name="ramp-web-interface">
{{cite web
 | title = RAMP: NESSI web interface
 | url = https://nessi.iwi.uni-hannover.de/en/ramp/start/
 | website = NESSI:RAMP
 | access-date=2024-06-19
}}
</ref>

{{clear}}

===venco.py===
{| class="infobox" style="width: 28em"
|-
! style="width: 35%" | Project
| style="width: 55%" | venco.py
|-
! Host
| [[Organization:German Aerospace Center|German Aerospace Center]]
|-
! Status
| active
|-
! Scope/type
| electric vehicle{{nnbsp}}/{{nnbsp}}system interactions
|-
! Code license
| BSD-new
|-
! Language
| [[Python (programming language)|Python]]
|-
! Website
| {{mdash}}
|-
! Mailing list
| {{URL|https://groups.google.com/g/openmod-trans}}
|-
! Repository
| {{URL|https://gitlab.com/dlr-ve/vencopy}}
|-
! Documentation
| {{URL|https://vencopy.readthedocs.io}}
|-
! Discussion
| {{URL|https://forum.openmod.org/tag/vencopy}}
|}

The venco.py model framework can be used to investigate interactions between the uptake of [[Engineering:Battery electric vehicle|battery electric vehicle]]s (BEV) and the electricity system at large. More specifically, BEVs can usefully contribute to short‑haul storage in power systems facing high shares of fluctuating renewable energy. But unlike dedicated grid storage, BEV contributions are highly dependent on the connection and charging choices that individual vehicle owners might make.<ref name="wulff-etal-2020">
{{cite journal
 | last1 = Wulff | first1 = Niklas
 | last2 = Steck | first2 = Felix
 | last3 = Gils | first3 = Hans Christian
 | last4 = Hoyer-Klick | first4 = Carsten
 | last5 = van den Adel | first5 = Bent
 | last6 = Anderson | first6 = John E
 | title = Comparing power-system and user-oriented battery electric vehicle charging representation and its implications on energy system modeling
 | date = March 2020
 | journal = Energies
 | volume = 13
 | issue = 5
 | pages = 1093
 | doi = 10.3390/en13051093
 | issn = 1996-1073
 | doi-access = free}}</ref>

[[File:Vencopy-schema.png| thumb|left | Overall structure of venco.py.]]

Venco.py has been applied to various scenarios in Germany in 2030 using a projected 9{{nbsp}}million BEVs in service and an annual fleet power consumption of {{val|27|u=TWh}}. Simulations show that owner decisions are indeed significant and that some system design variables have more influence than others. For instance, aggregate fleet capacity and the availability of fast charging facilities appear to strongly impact the likely system contribution. Further work is needed to assess the influence of more resolved weather and demand patterns.<ref name="wulff-etal-2020"/> The mathematical formulation is available.<ref name="wulff-etal-2021">
{{cite journal
 | last1 = Wulff | first1 = Niklas
 | last2 = Miorelli | first2 = Fabia
 | last3 = Gils | first3 = Hans Christian
 | last4 = Jochem | first4 = Patrick
 | title = Vehicle Energy Consumption in Python (VencoPy): presenting and demonstrating an open-source tool to calculate electric vehicle charging flexibility
 | date = July 2021
 | journal = Energies
 | volume = 14
 | issue = 14
 | pages = 4349
 | doi = 10.3390/en14144349
 | issn = 1996-1073
 | doi-access = free}}</ref> Venco.py builds on an earlier [[Engineering:Spreadsheet|spreadsheet]] prototype.<ref name="costales-2014">
{{cite thesis
 | last1 = Costales | first1 = Diego Luca de Tena
 | title = Large scale renewable power integration with electric vehicles: long term analysis for Germany with a renewable based power supply
 | type = PhD
 | date = April 2014
 | publisher = Stuttgart University
 | location = Stuttgart, Germany
 | url = https://elib.dlr.de/92151/1/Dissertation_Diego_Luca_de_Tena.pdf
 | access-date = 2021-11-08}} Describes a spreadsheet prototype.</ref>
{{clear}}

==Project statistics==
Statistics for the 30 open energy modeling projects listed (given sufficient information is available) 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" | 14
  |-
  | [[Ruby (programming language)|Ruby]]
  | align="right" |  1
  |-
  | rowspan="1" | [[Multiple dispatch]]
  | [[Julia (programming language)|Julia]]
  | align="right" |  3
  |-
  | rowspan="2" | [[Mathematical optimization|Mathematical programming]]
  | style="background-color: #EED0B2" | [[Software:General Algebraic Modeling System|GAMS]]
  | align="right" |  7
  |-
  | MathProg
  | align="right" |  2
  |-
  | [[Engineering:Spreadsheet|Spreadsheet]]
  | style="background-color: #EED0B2" | [[Software: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
  |-
  | {{Flag|Australia}}                 || style="text-align: right" |  2
  |-
  | {{Flag|Denmark}}                   || style="text-align: right" |  1
  |-
  | {{Flag|European Union}}            || style="text-align: right" |  1
  |-
  | {{Flag|Finland}}                   || style="text-align: right" |  1
  |-
  | {{Flag|Germany}}                   || style="text-align: right" | 14
  |-
  | {{Flag|Netherlands}}               || style="text-align: right" |  5
  |-
  | {{Flag|Sweden}}{{nnbsp}}{{efn|OSeMOSYS is deemed to reside in Sweden due to the influence of the KTH Royal Institute of Technology on the project.}} || style="text-align: right" |  2
  |-
  | {{Flag|Switzerland}}               || style="text-align: right" |  2
  |-
  | {{Flag|United Kingdom}}{{pad|1em}} || style="text-align: right" |  1
  |-
  | {{Flag|United States}}             || style="text-align: right" |  3
  |}

| {{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" | 21
  |-
  | 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 [[Software: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
 | archive-date = 8 February 2016
 | archive-url = https://web.archive.org/web/20160208000745/http://www.gams.de/sales/commercialp.pdf
 | url-status = dead
 }}</ref>

==Programming components==
[[Component-based software engineering|Programming components]], in this context, are coherent blocks of code or compiled libraries that can be relatively easily imported or linked to by higher{{nbhyph}}level modeling frameworks in order to obtain some well{{nbhyph}}defined functionality.

===Technology modules <span class="anchor" id="Component models"></span>===
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. Technology modules 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>
* [[Software:Pvlib python|pvlib]] [[Chemistry:Photovoltaics|photovoltaics]] facility library<ref name="holmgren-etal-2018">
{{cite journal
 | last1 = Holmgren | first1 = William F
 | last2 = Hansen | first2 = Clifford W
 | last3 = Mikofski | first3 = Mark A
 | title = pvlib python: a python package for modeling solar energy systems
 | date = 2018
 | journal = Journal of Open Source Software
 | volume = 3
 | issue = 29
 | pages = 884
 | issn = 2475-9066
 | url = https://joss.theoj.org/papers/10.21105/joss.00884.pdf
 | access-date = 2021-09-27
 | doi = 10.21105/joss.00884
 | bibcode = 2018JOSS....3..884F
 | s2cid = 240160353}}</ref>
* hplib [[Physics:Heat pump|heat pump]] facility library<ref name="tjaden-and-hoops-2021">
{{cite journal
 | last1 = Tjaden | first1 = Tjarko
 | last2 = Hoops | first2 = Hauke
 | title = hplib: heat pump library
 | date = 22 September 2021
 | journal = Zenodo
 | doi = 10.5281/zenodo.5521598
 | url = https://zenodo.org/record/5521598
 | access-date = 2021-09-27}}</ref>
* windpowerlib [[Physics:Wind turbine|wind turbine]] library <ref name="windpowerlib-2019">
{{cite web
 | title = windpowerlib
 | date = 9 March 2021
 | work = GitHub
 | url = https://github.com/wind-python/windpowerlib/
 | access-date = 2022-01-03}}</ref>
* hydropowerlib hydroelectricity library <ref name="hydropowerlib">
{{cite web
 | title = hydropowerlib
 | date = 11 June 2019
 | work = GitHub
 | url = https://github.com/hydro-python/hydropowerlib
 | access-date = 2022-01-03}}</ref>

===Auction models===
A number of electricity auction models have been written in [[Software:General Algebraic Modeling System|GAMS]], [[Software:AMPL|AMPL]], MathProg, and other languages.{{efn|
MathProg is a subset of [[Software:AMPL|AMPL]].  It is sometimes possible to convert an AMPL model into MathProg without much effort.}} These include:
* the EPOC 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>
* Australian National Electricity Market examples using MathProg can be found at b:GLPK/Electricity markets

===Open solvers===
Many projects rely on a [[Linear programming|pure linear]] or 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, ETEM, ficus, OSeMOSYS, SWITCH, and 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> From mid{{nbhyph}}2022, the [[Organization:HiGHS optimization solver|HiGHS]] open source solver offers another option. HiGHS is used by the web{{nbhyph}}based version of the PyPSA European multi{{nbhyph}}sector model<ref name="brown-ongoing">
{{cite web
 | last1 = Brown | first1 = Tom
 | title = PyPSA-Eur-Sec optimization server
 | url = https://model.energy/scenarios/
 | access-date = 2022-07-22}} A web interface to PyPsa{{nbhyph}}Eur{{nbhyph}}Sec model.</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 speed, memory consumption, and perhaps even tractability.<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
 | s2cid = 45013649
 | 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>

The flexible SMS++ optimization toolbox, written in [[C++17]], is being developed specifically to meet the needs of energy system modeling.<ref name="frangioni-and-charousset-2021">
{{cite book
 | last1 = Frangioni | first1 = Antonio
 | last2 = Charousset | first2 = Sandrine
 | title = plan4res SMS++: an open modelling library for evaluating long term electricity system costs and flexibilities
 | date = 28 October 2021
 | publisher = University of Pisa
 | location = Italy
 | url = https://prod5.assets-cdn.io/event/6546/assets/8364055013-e2eb25f20c.pdf
 | access-date = 2021-11-10}} EMP‑E 2021 presentation. {{open access}}</ref>

==See also==
'''General'''
* Building energy simulation – the modeling of energy flows in buildings
* [[Earth:Climate change mitigation scenarios|Climate change mitigation scenarios]]
* [[Earth:Energy modeling|Energy modeling]] – the process of building computer models of energy systems
* [[Energy system]] – the interpretation of the energy sector in system terms
* [[Organization:Open Energy Modelling Initiative|Open Energy Modelling Initiative]] – a European-based energy modeling community
* [[Open energy system databases]] – database projects which collect, clean, and republish energy-related datasets
* [[Finance:Unit commitment problem in electrical power production|Unit commitment problem in electrical power production]]

'''Software'''
* [[Software:List of optimization software#Free and open-source software|List of free and open-source optimization solvers]]
* [[Organization:COIN-OR#CBC|Cbc]] (COIN-OR Branch and Cut) – an open source optimization solver
* [[Organization:COIN-OR#CLP|Clp]] (COIN-OR LP) – an open source linear optimization solver
* [[Earth:Community Climate System Model|Community Climate System Model]] – a mostly open source coupled global climate model
* [[Earth:Earth System Modeling Framework|ESMF]] (Earth System Modeling Framework) – open source software for building [[Earth:Climate model|climate]], [[Earth:Numerical weather prediction|numerical weather prediction]], and [[Earth:Data assimilation|data assimilation]] applications
* GHGProof – an open source land-use model
* GLPK (GNU Linear Programming Kit) – an open source linear and mixed integer optimization solver
* [[Software:GridLAB-D|GridLAB-D]] – an open source simulation and analysis tool for smart grid energy technologies
* [[Software:GridSpice|GridSpice]] – an open source cloud-based simulation package for modelling smart grids
* [[Organization:HiGHS optimization solver|HiGHS]] – an open source optimization solver

'''People'''
* Joe DeCarolis – energy system modeler and current head of the United States Energy Information Administration

==Notes==
{{notelist}}

==References==
{{reflist|30em}}

==Further information==
The following lists and databases cover energy system models to varying degrees of completeness and usually with a focus on open source:
* [http://wiki.openmod-initiative.org/wiki/Open_Models Open energy models wiki] maintained by the [[Organization:Open Energy Modelling Initiative|Open Energy Modelling Initiative]]
* [https://openenergy-platform.org/factsheets/models/ Open Energy Platform factsheets] — structured summaries covering a range of open and closed energy system models
* [https://g-pst.github.io/tools/ Global Power System Transformation Consortium database] — filterable database of open models and related projects

==External links==

'''Modeling efforts by region'''
* [https://forum.u4ria.org/t/africa-reports-and-publications/27 Africa: reports and publications] — broken down by region and country
* [https://forum.u4ria.org/t/latin-america-reports-and-publications/60 Latin America: reports and publications] — broken down by region and country
* [https://forum.u4ria.org/t/oceania-reports-and-publications/29 Oceania: reports and publications] — broken down by region and country

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