butterfly/kb
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Scrape wikipedia-science: 84 new, 846 updated, 958 total (kb-cron)

Browse Source
This commit is contained in:
turtle89431 2026-05-04 20:49:32 -07:00
parent 4a921fdd56
commit 2c1b4c161f
33 changed files with 1258 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

BIN
_index.db
View File

Binary file not shown.

53
data/en.wikipedia.org/wiki/Initiative_for_Open_Citations-0.md Normal file
View File

@ -0,0 +1,53 @@
---
title: "Initiative for Open Citations"
chunk: 1/1
source: "https://en.wikipedia.org/wiki/Initiative_for_Open_Citations"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:49:20.289810+00:00"
instance: "kb-cron"
---
The Initiative for Open Citations (I4OC) is a project launched publicly in April 2017, that describes itself as: "a collaboration between scholarly publishers, researchers, and other interested parties to promote the unrestricted availability of scholarly citation data and to make these data available." It is intended to facilitate improved citation analysis.
== Methodology ==
The citations are stored in Crossref and are made available through the Crossref REST API. They are also available from the OpenCitations Corpus, a database that harvests citation data from Crossref and other sources. The data are considered by those involved in the Initiative to be in the public domain, and so a CC0 licence is used. The stated benefits of this approach are: 1. discoverability of published content; 2. the building of new services, and 3. creation of a public citation graph to explore connections between knowledge fields.
The Royal Society of Chemistry earmarked the I4OC as a feature of excellence in publishing, and IOP Publishing participates to implement their commitment to the San Francisco Declaration on Research Assessment.
== Launch ==
The initiative was established in response to a paper on citations in Wikidata, Citations needed for the sum of all human knowledge: Wikidata as the missing link between scholarly publishing and linked open data, given by Dario Taraborelli, head of research at the Wikimedia Foundation, at the eighth Conference on Open Access Scholarly Publishing, in September 2016. At that time, only 1% of papers in Crossref had citations metadata that were freely available. By the time of the public launch, on 6 April 2017, that had risen to 40% as a result of setting up the initiative.
The founding partners were:
OpenCitations
Wikimedia Foundation
PLOS
eLife
DataCite
The Centre for Culture and Technology at Curtin University
At the time of launch, 64 organisations, including the Wellcome Trust, the Bill And Melinda Gates Foundation and the Alfred P. Sloan Foundation, had endorsed the project and as of May, 2017, Sloan Foundation confirmed it would be providing funding. 29 of these organisations were publishers who had agreed to share their citation metadata openly. These include Springer Nature, Taylor & Francis, and Wiley. On 11 July 2017, the Initiative announced that a further sixteen publishers had signed up. On 8 August 2017, the Initiative released on open letter to stakeholders. The same month, the British Library became a member organisation.
== Growth ==
Elsevier, who contribute 30% of the citation metadata in Crossref, did not initially join the initiative. In April 2017, Elsevier's vice-president of corporate relations, Tom Reller, said:
We are aware of the initiative but want to learn more before making a decision on whether to participate.
In January 2019, the Editorial board of Elsevier's Journal of Informetrics resigned and launched the new journal Quantitative Science Studies, citing Elsevier's lack of support for the I4OC as one of the main reasons for the move. Elsevier claimed in response that they could not release their data for free due to loss of licensing revenue from their proprietary Scopus citation services.
Elsevier finally joined the initiative in January 2021 after the data was already available with an Open Data Commons license in Microsoft Academic.
In August 2022, the number of articles whose reference lists were free to access and reuse exceeded 60 million, out of 134 million articles indexed by Crossref.
== See also ==
Citebase
Initiative for Open Abstracts
== References ==
== External links ==
Official website
Video of Taraborelli's 2016 presentation, Citations needed for the sum of all human knowledge: Wikidata as the missing link between scholarly publishing and linked open data
Slides for the above on Figshare

26
data/en.wikipedia.org/wiki/International_HapMap_Project-0.md Normal file
View File

@ -0,0 +1,26 @@
---
title: "International HapMap Project"
chunk: 1/2
source: "https://en.wikipedia.org/wiki/International_HapMap_Project"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:49:21.458141+00:00"
instance: "kb-cron"
---
The International HapMap Project is an organization that aimed to develop a haplotype map (HapMap) of the human genome, to describe the common patterns of human genetic variation. HapMap is used to find genetic variants affecting health, disease and responses to drugs and environmental factors. The information produced by the project is made freely available for research.
The International HapMap Project is a collaboration among researchers at academic centers, non-profit biomedical research groups and private companies in Canada, China (including Hong Kong), Japan, Nigeria, the United Kingdom, and the United States. It officially started with a meeting on October 27 to 29, 2002, and was expected to take about three years. It comprises three phases; the complete data obtained in Phase I were published on 27 October 2005. The analysis of the Phase II dataset was published in October 2007. The Phase III dataset was released in spring 2009 and the publication presenting the final results published in September 2010.
== Background ==
Unlike with the rarer Mendelian diseases, combinations of different genes and the environment play a role in the development and progression of common diseases (such as diabetes, cancer, heart disease, stroke, depression, and asthma), or in the individual response to pharmacological agents. To find the genetic factors involved in these diseases, one could in principle do a genome-wide association study: obtain the complete genetic sequence of several individuals, some with the disease and some without, and then search for differences between the two sets of genomes. At the time, this approach was not feasible because of the cost of full genome sequencing. The HapMap project proposed a shortcut.
Although any two unrelated people share about 99.5% of their DNA sequence, their genomes differ at specific nucleotide locations. Such sites are known as single nucleotide polymorphisms (SNPs), and each of the possible resulting gene forms is called an allele. The HapMap project focuses only on common SNPs, those where each allele occurs in at least 1% of the population.
Each person has two copies of all chromosomes, except the sex chromosomes in males. For each SNP, the combination of alleles a person has is called a genotype. Genotyping refers to uncovering what genotype a person has at a particular site. The HapMap project chose a sample of 269 individuals and selected several million well-defined SNPs, genotyped the individuals for these SNPs, and published the results.
The alleles of nearby SNPs on a single chromosome are correlated. Specifically, if the allele of one SNP for a given individual is known, the alleles of nearby SNPs can often be predicted, a process known as genotype imputation. This is because each SNP arose in evolutionary history as a single point mutation, and was then passed down on the chromosome surrounded by other, earlier, point mutations. SNPs that are separated by a large distance on the chromosome are typically not very well correlated, because recombination occurs in each generation and mixes the allele sequences of the two chromosomes. A sequence of consecutive alleles on a particular chromosome is known as a haplotype.
To find the genetic factors involved in a particular disease, one can proceed as follows. First a certain region of interest in the genome is identified, possibly from earlier inheritance studies. In this region one locates a set of tag SNPs from the HapMap data; these are SNPs that are very well correlated with all the other SNPs in the region. Using these, genotype imputation can be used to determine (impute) the other SNPs and thus the entire haplotype with high confidence. Next, one determines the genotype for these tag SNPs in several individuals, some with the disease and some without. By comparing the two groups, one determines the likely locations and haplotypes that are involved in the disease.
== Samples used ==
Haplotypes are generally shared between populations, but their frequency can differ widely. Four populations were selected for inclusion in the HapMap: 30 adult-and-both-parents Yoruba trios from Ibadan, Nigeria (YRI), 30 trios of Utah residents of northern and western European ancestry (CEU), 44 unrelated Japanese individuals from Tokyo, Japan (JPT) and 45 unrelated Han Chinese individuals from Beijing, China (CHB). Although the haplotypes revealed from these populations should be useful for studying many other populations, parallel studies are currently examining the usefulness of including additional populations in the project.
All samples were collected through a community engagement process with appropriate informed consent. The community engagement process was designed to identify and attempt to respond to culturally specific concerns and give participating communities input into the informed consent and sample collection processes.
In phase III, 11 global ancestry groups have been assembled: ASW (African ancestry in Southwest USA); CEU (Utah residents with Northern and Western European ancestry from the CEPH collection); CHB (Han Chinese in Beijing, China); CHD (Chinese in Metropolitan Denver, Colorado); GIH (Gujarati Indians in Houston, Texas); JPT (Japanese in Tokyo, Japan); LWK (Luhya in Webuye, Kenya); MEX (Mexican ancestry in Los Angeles, California); MKK (Maasai in Kinyawa, Kenya); TSI (Tuscans in Italy); YRI (Yoruba in Ibadan, Nigeria).
Three combined panels have also been created, which allow better identification of SNPs in groups outside the nine homogenous samples: CEU+TSI (Combined panel of Utah residents with Northern and Western European ancestry from the CEPH collection and Tuscans in Italy); JPT+CHB (Combined panel of Japanese in Tokyo, Japan and Han Chinese in Beijing, China) and JPT+CHB+CHD (Combined panel of Japanese in Tokyo, Japan, Han Chinese in Beijing, China and Chinese in Metropolitan Denver, Colorado). CEU+TSI, for instance, is a better model of UK British individuals than is CEU alone.

45
data/en.wikipedia.org/wiki/International_HapMap_Project-1.md Normal file
View File

@ -0,0 +1,45 @@
---
title: "International HapMap Project"
chunk: 2/2
source: "https://en.wikipedia.org/wiki/International_HapMap_Project"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:49:21.458141+00:00"
instance: "kb-cron"
---
== Scientific strategy ==
It was expensive in the 1990s to sequence patients' whole genomes. So the National Institutes of Health embraced the idea for a "shortcut", which was to look just at sites on the genome where many people have a variant DNA unit. The theory behind the shortcut was that, since the major diseases are common, so too would be the genetic variants that caused them. Natural selection keeps the human genome free of variants that damage health before children are grown, the theory held, but fails against variants that strike later in life, allowing them to become quite common (In 2002 the National Institutes of Health started a $138 million project called the HapMap to catalog the common variants in European, East Asian and African genomes).
For the Phase I, one common SNP was genotyped every 5,000 bases. Overall, more than one million SNPs were genotyped. The genotyping was carried out by 10 centres using five different genotyping technologies. Genotyping quality was assessed by using duplicate or related samples and by having periodic quality checks where centres had to genotype common sets of SNPs.
The Canadian team was led by Thomas J. Hudson at McGill University in Montreal and focused on chromosomes 2 and 4p. The Chinese team was led by Huanming Yang in Beijing and Shanghai, and Lap-Chee Tsui in Hong Kong and focused on chromosomes 3, 8p and 21. The Japanese team was led by Yusuke Nakamura at the University of Tokyo and focused on chromosomes 5, 11, 14, 15, 16, 17 and 19. The British team was led by David R. Bentley at the Sanger Institute and focused on chromosomes 1, 6, 10, 13 and 20. There were four United States' genotyping centres: a team led by Mark Chee and Arnold Oliphant at Illumina Inc. in San Diego (studying chromosomes 8q, 9, 18q, 22 and X), a team led by David Altshuler and Mark Daly at the Broad Institute in Cambridge, USA (chromosomes 4q, 7q, 18p, Y and mitochondrion), a team led by Richard Gibbs at the Baylor College of Medicine in Houston (chromosome 12), and a team led by Pui-Yan Kwok at the University of California, San Francisco (chromosome 7p).
To obtain enough SNPs to create the Map, the Consortium funded a large re-sequencing project to discover millions of additional SNPs. These were submitted to the public dbSNP database. As a result, by August 2006, the database included more than ten million SNPs, and more than 40% of them were known to be polymorphic. By comparison, at the start of the project, fewer than 3 million SNPs were identified, and no more than 10% of them were known to be polymorphic.
During Phase II, more than two million additional SNPs were genotyped throughout the genome by David R. Cox, Kelly A. Frazer and others at Perlegen Sciences and 500,000 by the company Affymetrix.
== Data access ==
All of the data generated by the project, including SNP frequencies, genotypes and haplotypes, were placed in the public domain and are available for download. The website was retired in 2016, however the original data is still available for download. The website used to contain a genome browser which allows to find SNPs in any region of interest, their allele frequencies and their association to nearby SNPs. A tool that can determine tag SNPs for a given region of interest was also provided. These data can also be directly accessed from the widely used Haploview program.
== Publications ==
International HapMap Consortium (2003). "The International HapMap Project" (PDF). Nature. 426 (6968): 789–796. Bibcode:2003Natur.426..789G. doi:10.1038/nature02168. hdl:2027.42/62838. PMID 14685227. S2CID 4387110.
International HapMap Consortium (2004). "Integrating ethics and science in the International HapMap Project". Nature Reviews Genetics. 5 (6): 467–475. doi:10.1038/nrg1351. PMC 2271136. PMID 15153999.
International HapMap Consortium (2005). "A haplotype map of the human genome". Nature. 437 (7063): 1299–1320. Bibcode:2005Natur.437.1299T. doi:10.1038/nature04226. PMC 1880871. PMID 16255080.
International HapMap Consortium (2007). "A second generation human haplotype map of over 3.1 million SNPs". Nature. 449 (7164): 851–861. Bibcode:2007Natur.449..851F. doi:10.1038/nature06258. PMC 2689609. PMID 17943122.
International HapMap 3 Consortium (2010). "Integrating common and rare genetic variation in diverse human populations". Nature. 467 (7311): 52–58. Bibcode:2010Natur.467...52T. doi:10.1038/nature09298. PMC 3173859. PMID 20811451.{{cite journal}}: CS1 maint: numeric names: authors list (link)
Deloukas P, Bentley D (2004). "The HapMap project and its application to genetic studies of drug response". The Pharmacogenomics Journal. 4 (2): 88–90. doi:10.1038/sj.tpj.6500226. PMID 14676823.
Thorisson GA, Smith AV, Krishnan L, Stein LD (2005). "The International HapMap Project Web site". Genome Research. 15 (11): 1592–1593. doi:10.1101/gr.4413105. PMC 1310647. PMID 16251469.
Terwilliger JD, Hiekkalinna T (2006). "An utter refutation of the 'Fundamental Theorem of the HapMap'". European Journal of Human Genetics. 14 (4): 426–437. doi:10.1038/sj.ejhg.5201583. PMID 16479260.
Secko, David (2005). "Phase I of the HapMap Complete" Archived 2011-05-14 at the Wayback Machine. The Scientist
== See also ==
Genealogical DNA test
The 1000 Genomes Project
Population groups in biomedicine
Human Variome Project
Human genetic variation
== References ==
== External links ==
International HapMap Project (HapMap Homepage) Archived 2014-04-16 at the Wayback Machine
National Human Genome Research Institute (NHGRI) HapMap Page
Browsing HapMap Data Using the Genome Browser
The Mexican Genome Diversity Project

88
data/en.wikipedia.org/wiki/Journal_Article_Tag_Suite-0.md Normal file
View File

@ -0,0 +1,88 @@
---
title: "Journal Article Tag Suite"
chunk: 1/2
source: "https://en.wikipedia.org/wiki/Journal_Article_Tag_Suite"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:49:22.745801+00:00"
instance: "kb-cron"
---
The Journal Article Tag Suite (JATS) is format used to describe scientific literature published online. It is a technical standard developed by the National Information Standards Organization (NISO) and approved by the American National Standards Institute with the code Z39.96-2012.
The NISO project was a continuation of the work done by NLM/NCBI, and popularized by the NLM's PubMed Central as a de facto standard for archiving and interchange of scientific open-access journals and its contents with XML.
With the NISO standardization the NLM initiative has gained a wider reach, and several other repositories, such as SciELO and Redalyc, adopted the XML formatting for scientific articles.
The JATS provides a set of XML elements and attributes for describing the textual and graphical content of journal articles
as well as some non-article material such as letters, editorials, and book and product reviews.
JATS allows for descriptions of the full article content or just the article header metadata;
and allows other kinds of contents, including research and non-research articles, letters, editorials, and book and product reviews.
== History ==
Since its introduction, NCBI's NLM Archiving and Interchange DTD suite has become the de facto standard for journal article markup in scholarly publishing. With the introduction of NISO JATS, it has been elevated to a true standard.
Even without public data interchange, the advantages of NISO JATS adoption affords publishers in terms of streamlining production workflows and optimizing system interoperability.
=== Timeline ===
NLM JATS
NLM JATS, version 1
March 31, 2003 (2003-03-31): NLM DTD v1.0 introduced.
November 5, 2003 (2003-11-05): Version 1.1 update released.
NLM JATS, version 2
December 30, 2004 (2004-12-30): Version 2.0 major update released. It is designed to support customization best-practices.
November 14, 2005 (2005-11-14): Version 2.1 update released with the addition the Article Authoring DTD.
June 8, 2006 (2006-06-08): Version 2.2 update released.
March 28, 2007 (2007-03-28): Version 2.3 update released.
NLM JATS, version 3
November 21, 2008 (2008-11-21): Version 3.0 major update released.
NISO JATS
NISO JATS, version 1.0
March 30, 2011 (2011-03-30) – September 30, 2011 (2011-09-30): First draft, NISO Z39.96.201x version 0.4 released; six-month comment period.
July 15, 2012 (2012-07-15): NISO JATS, v1.0 received NISO approval.
August 9, 2012 (2012-08-09): NISO JATS, v1.0 received ANSI approval.
August 22, 2012 (2012-08-22): ANSI/NISO Z39.96-2012, JATS: Journal Article Tag Suite (version 1.0) published. It supports full backward-compatibility with NLM JATS v3.0.
NISO JATS, version 1.1
December 9, 2013 (2013-12-09): First draft, NISO JATS, v1.1d1 released.
December 29, 2014 (2014-12-29): Second draft, NISO JATS, v1.1d2 released.
April 14, 2015 (2015-04-14): Third draft, NISO JATS, v1.1d released.
October 22, 2015 (2015-10-22): NISO JATS, v1.1 received NISO approval.
November 19, 2015 (2015-11-19): NISO JATS, v1.1 received ANSI approval
January 6, 2016 (2016-01-06): ANSI/NISO Z39.96-2015, JATS: Journal Article Tag Suite, version 1.1 published.
NISO JATS, version 1.2
July 20, 2017 (2017-07-20): First draft, NISO JATS, v1.2d1 released.
May 23, 2018 (2018-05-23): First draft, NISO JATS, v1.2d2 released.
February 8, 2019 (2019-02-08): ANSI/NISO Z39.96-2019, JATS: Journal Article Tag Suite, version 1.2 published.
NISO JATS, version 1.3
July 7, 2021 (2021-07-07): ANSI/NISO Z39.96-2021, JATS: Journal Article Tag Suite, version 1.3 published.
NISO JATS, version 1.4
October 31, 2024: ANSI/NISO Z39.96-2024, JATS: Journal Article Tag Suite, version 1.4 published.
== Technical scope ==
By design, this is a model for journal articles, such as the typical research article found in an STM journal, and not a model for complete journals.
=== Tag sets ===
There are three tag sets:
Journal Archiving and Interchange (Green)
"The most permissive of the Tag Sets," primarily intended for the capture and archiving of extant journal data.
Journal Publishing (Blue)
"A moderately prescriptive Tag Set," intended for general use in journal production and publication.
Formally this model is a subset of the Archiving model. This is the most frequently used JATS variant.
Article Authoring (Orange)
"The most prescriptive [tightest and smallest] of the Tag Sets," intended for the relatively lightweight creation of journal articles valid to JATS.
Formally this model a subset of the Publishing model.
Document type definitions (also released in the form of RELAX NG and XML schema) define each set and incorporate other standards such as MathML and XHTML Tables (although not in the XHTML namespace).
=== Document structure ===
JATS Publishing set defines a document that is a top-level component of a journal such as an article, a book or product review, or a letter to the editor. Each such document is composed of front matter (required) and up to three optional parts. These must appear in the following order:
Front matter
The article front matter contains the metadata for the article (also called article header information), for example, the article title, the journal in which it appears, the date and issue of publication for that issue of that journal, a copyright statement, etc. Both article-level and issue-level metadata (in the element <article-meta>) and journal-level metadata (in the element <journal-meta>) may be captured.
Body (of the article)
The body of the article is the main textual and graphic content of the article. This usually consists of paragraphs and sections, which may themselves contain figures, tables, sidebars (boxed text), etc. The body of the article is optional to accommodate those repositories that just keep article header information and do not tag the textual content.
Back matter
If present, the article back matter contains information that is ancillary to the main text, such as a glossary, appendix, or list of cited references.
Floating material
A publisher may choose to place all the floating objects in an article and its back matter (such as tables, figures, boxed text sidebars, etc.) into a separate container element outside the narrative flow for convenience of processing.
Following the front, body, back, and floating material, there may be either one or more responses to the article or one or more subordinate articles.
== Example ==
This is the minimal article's structure,

103
data/en.wikipedia.org/wiki/Journal_Article_Tag_Suite-1.md Normal file
View File

@ -0,0 +1,103 @@
---
title: "Journal Article Tag Suite"
chunk: 2/2
source: "https://en.wikipedia.org/wiki/Journal_Article_Tag_Suite"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:49:22.745801+00:00"
instance: "kb-cron"
---
The DOCTYPE header is optional, a legacy from SGML and DTD-oriented validators. The dtd-version attribute can be used even without a DTD header.
The root element article is common for any version of JATS or "JATS family", as NLM DTDs. The rules for front, body and back tags validation, depends on the JATS version, but all versions have similar structure, with good compatibility in a range of years. The evolution of the schema preserves an overall stability.
Less common, "only front", "only front and back" variations are also used for other finalities than full-content representation. The general article composition (as an DTD-content expression) is
(front, body?, back?, floats-group?, (sub-article* | response*))
== Tools ==
There are a variety of tools for create, edit, convert and transform JATS.
They range from simple forms to complete conversion automation:
=== Conversion to JATS ===
Take as input a scientific document, and, with some human support, produce a JATS output.
OpenOffice (LibreOffice) and MS Word documents to JATS:
Typeset: provides automated set of converters for MS-Word to JATS XML.
OxGarage: can convert documents from various formats into "National Library of Medicine (NLM) DTD 3.0".
meTypeset: meTypeset "is a fork of the OxGarage stack" "to convert from Microsoft Word .docx format to NLM/JATS-XML".
eXtyles: automates time-consuming aspects of document editing in Microsoft Word and exports to JATS XML (as well as many other DTDs).
Markdown to JATS: Pandoc 2.0 can convert a number of input formats to JATS.
PDF to JATS: this is a very difficult problem to solve. Success depends on how well structured your PDFs are and, for batch conversion, how consistently structured your PDFs are.
Shabash Merops
Typeset's PDF to JATS XML Converter
The Public Knowledge Project is developing a pipeline for converting PDF to JATS. It will include use of pdfx.
CERMINE Content ExtRactor and MINEr
=== Conversion from JATS ===
Take JATS as input, produce another kind of document as output.
from JATS to HTML
JATS Preview Stylesheets (canonical XSLT conversion), see classical (2013) conversor.
eLife Lens converts NLM XML to JSON for displaying using HTML and Javascript.
from JATS to PDF: some JATS Preview Stylesheets, XSLT + XSL-FO conversion.
from JATS to EPUB.
Generic (from JATS DTD): DtdAnalyzer — compare JATS with other DTDs and helps into create a XML representation, XSLT and Schematron generation, and other tools.
=== Editors ===
Typeset provides a WYSIWYM editor for scholarly articles. Supports XML exports in NISO JATS and NLM JATS standards. It is mostly used by Journals and Publishers looking to convert author submitted MS-Word files to XML, PDF, HTML and ePuB.
JATS Framework for oXygen XML Editor: users of oXygen XML Editor and oXygen XML Author can now install support for current versions of NISO JATS (and as a bonus, NLM BITS). Based on an identifier given in a DOCTYPE declaration, oXygen will detect that you are editing a JATS document and provide stylesheets and utilities.
FontoXML for JATS: WYSIWYS editor for editing and reviewing JATS content:
PubRef "Pipeline": Browser-based realtime-preview JATS editor:
Annotum: a WordPress theme that contains WYSIWYG authoring in JATS (Kipling subset), peer-review and editorial management, and publishing.
JATS edition for web-based XML editor Xeditor.
Texture Editor of the Substance Consortium. The first online "born to JATS" editor.
Libero Editor, developed by eLife describes itself as 'A user-friendly editing interface designed for publishing staff and authors for the production of high-quality JATS XML.'
=== Preview ===
Tools that render JATS as HTML, usually on fly.
JATS Preview Stylesheets: the JATS Preview Stylesheets are a series of .xsl, .xpl, .css, and .sch files that will create .html or .pdf versions of valid NISO Z39.96-2012 JATS 1.0 files. It is primarily intended for internal use by publishers and a basis for customization.
Typeset - Allows to generate HTML from JATS XML within a click. Also, offers capacity to generate custom HTML based on the requirements of the journal.
PubReader – "The PubReader view is an alternative web presentation ... Designed particularly for enhancing readability on tablet and other small screen devices, PubReader can also be used on desktops and laptops and from multiple web browsers".
=== Customization ===
Jatsdoc - Produces documentation for any particular JATS customization. Jatsdoc is integrated with NCBI's DtdAnalyzer.
== JATS central repositories ==
As NISO JATS began the de facto and de jure standard for open access journals, the scientific community has adopted the JATS repositories as a kind of legal deposit, sometimes deemed more valuable than the traditional digital libraries where only a PDF version is stored. Open knowledge need richer and structured formats as JATS: PDF and JATS must be certified as "same content", and the set "PDF+JATS" forming the unit of legal deposit.
List of JATS repositories and its contained:
PubMed Central: (please check these numbers)
US PubMed Central: in 2016 ~3.8 million articles
Europe PubMed Central: in 2016 ~3,7 million articles
SciELO: in 2016 ~0.6 million articles
These repositories do overlap and the same article can be held by more than one repository.
== Alternatives and semantic ==
There are some effort and experiments using RDF conversion in the 2012, with no impact in the JATS community.
Later, in ~2016, for Semantic Web context, with SchemaOrg initiative, the class ScholarlyArticle was defined, receiving better reception. It is an initial "JATS-like standardization" for RDF contexts of use.
== See also ==
== References ==
== Further reading ==
Packer, Abel L.; Salgado, Eliana; Araujo, Javani; Aquino, Letícia; Almeida, Renata; Santos, Jesner; Lucena, Suely; Soares, Caroline M. (4 April 2014). "Why XML?". SciELO in Perspective.
Sharp, Molly (4 June 2013). "Structured Documents for Science: JATS XML as Canonical Content Format". PLOS Tech.
== External links ==
NLM Journal Article Tag Suite – NCBI's information and documentation site.
NISO JATS Version 1.1 (current standard):
Archiving and Interchange tag library
Publishing tag library
Article Authoring tag library
Styles and customization:
SciELO Publishing Schema (SPS) – SciELO's customization.
Tagging Guidelines of PubMed Central's preferred XML tagging style
ISO Standards Tag Set (ISOSTS) as a customization of NISO JATS
NISO Book Interchange Tag Suite (BITS), based on JATS.
TextureJATS, a minimal coherent subset of JATS.
JATS open community:
"JATS for Reuse" (JATS4R) community, validator
SchemaOrg community, ScholarlyArticle
PeerJ's XML-JATS to HTML5-ScholarlyArticle

34
data/en.wikipedia.org/wiki/MetaNetX-0.md Normal file
View File

@ -0,0 +1,34 @@
---
title: "MetaNetX"
chunk: 1/1
source: "https://en.wikipedia.org/wiki/MetaNetX"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:49:24.009125+00:00"
instance: "kb-cron"
---
MetaNetX is a database maintained by the SIB Swiss Institute of Bioinformatics for the automated model construction, and the genome annotation for large-scale metabolic networks. MetaNetX provides a number of tools to access, analyse and manipulate metabolic networks.
MetaNetX provides a bunch of pre-mapped metabolic models.
To ease model comparison, MetaNetX has developed a resource to unify metabolites and biochemical reactions in the context of metabolic models. This unified namespace is called MetaNetX/MNXref.
MNXref reconciles chemical compounds by structural similarity and biochemical reaction context. Then reconciles biochemical reactions on the basis of the chemical compound reconciliation in an iterative way. Each reconciled group of chemical compounds, biochemical reactions and cellular compartments is a bag of similar items. MNXref sets a referent for each group.
MetaNetX allows search in MNXref by chemical compounds, biochemical reactions and cellular compartments.
Currently, MetaNetX/MNXref reconciles those resources:
BiGG
ChEBI
enviPath
HMDB
GO
KEGG
LipidMaps
MetaCyc
Reactome
Rhea
SABIO-RK
SwissLipids
The SEED
VMH
== References ==

0
data/en.wikipedia.org/wiki/Mixed_Signals Normal file
View File

30
data/en.wikipedia.org/wiki/Open_Energy_Modelling_Initiative-0.md Normal file
View File

@ -0,0 +1,30 @@
---
title: "Open Energy Modelling Initiative"
chunk: 1/2
source: "https://en.wikipedia.org/wiki/Open_Energy_Modelling_Initiative"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:49:27.553982+00:00"
instance: "kb-cron"
---
The Open Energy Modelling Initiative (openmod) is a grassroots community of energy system modellers from universities and research institutes across Europe and elsewhere. The initiative promotes the use of open-source software and open data in energy modelling for research and policy advice. The Open Energy Modelling Initiative documents a variety of open-source energy models and addresses practical and conceptual issues regarding their development and application. The initiative runs an email list, an internet forum, and a wiki and hosts occasional academic workshops. A statement of aims is available.
== Context ==
The application of open-source development to energy modelling dates back to around 2003. This section provides some background for the growing interest in open methods.
=== Growth in open energy modelling ===
Just two active open energy modelling projects were cited in a 2011 paper: OSeMOSYS and TEMOA. Balmorel was also public at that time, having been made available on a website in 2001. As of October 2021, the Open Energy Platform lists 17 open energy frameworks and about 50 open energy models.
=== Academic literature ===
This 2012 paper presents the case for using "open, publicly accessible software and data as well as crowdsourcing techniques to develop robust energy analysis tools". The paper claims that these techniques can produce high-quality results and are particularly relevant for developing countries.
There is an increasing call for the energy models and datasets used for energy policy analysis and advice to be made public in the interests of transparency and quality. A 2010 paper concerning energy efficiency modelling argues that "an open peer review process can greatly support model verification and validation, which are essential for model development". One 2012 study argues that the source code and datasets used in such models should be placed under publicly accessible version control to enable third-parties to run and check specific models. Another 2014 study argues that the public trust needed to underpin a rapid transition in energy systems can only be built through the use of transparent open-source energy models. The UK TIMES project (UKTM) is open source, according to a 2014 presentation, because "energy modelling must be replicable and verifiable to be considered part of the scientific process" and because this fits with the "drive towards clarity and quality assurance in the provision of policy insights". In 2016, the Deep Decarbonization Pathways Project (DDPP) is seeking to improve its modelling methodologies, a key motivation being "the intertwined goals of transparency, communicability and policy credibility." A 2016 paper argues that model-based energy scenario studies, wishing to influence decision-makers in government and industry, must become more comprehensible and more transparent. To these ends, the paper provides a checklist of transparency criteria that should be completed by modellers. The authors note however 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." An editorial from 2016 opines that closed energy models providing public policy support "are inconsistent with the open access movement [and] publically [sic] funded research". A 2017 paper lists the benefits of open data and models and the reasons that many projects nonetheless remain closed. The paper makes a number of recommendations for projects wishing to transition to a more open approach. The authors also conclude that, in terms of openness, energy research has lagged behind other fields, most notably physics, biotechnology, and medicine. Moreover:
Given the importance of rapid global coordinated action on climate mitigation and the clear benefits of shared research efforts and transparently reproducible policy analysis, openness in energy research should not be for the sake of having some code or data available on a website, but as an initial step towards fundamentally better ways to both conduct our research and engage decision-makers with [our] models and the assumptions embedded within them.
A one-page opinion piece in Nature News from 2017 advances the case for using open energy data and modelling to build public trust in policy analysis. The article also argues that scientific journals have a responsibility to require that data and code be submitted alongside text for scrutiny, currently only Energy Economics makes this practice mandatory within the energy domain.
=== Copyright and open energy data ===
Issues surrounding copyright remain at the forefront with regard to open energy data. Most energy datasets are collated and published by official or semi-official sources, for example, national statistics offices, transmission system operators, and electricity market operators. The doctrine of open data requires that these datasets be available under free licences (such as CC BY 4.0) or be in the public domain. But most published energy datasets carry proprietary licences, limiting their reuse in numerical and statistical models, open or otherwise. Measures to enforce market transparency have not helped because the associated information is normally licensed to preclude downstream usage. Recent transparency measures include the 2013 European energy market transparency regulation 543/2013  and a 2016 amendment to the German Energy Industry Act  to establish a nation energy information platform, slated to launch on 1 July 2017. Energy databases may also be protected under general database law, irrespective of the copyright status of the information they hold.
In December 2017, participants from the Open Energy Modelling Initiative and allied research communities made a written submission to the European Commission on the re-use of public sector information. The document provides a comprehensive account of the data issues faced by researchers engaged in open energy system modelling and energy market analysis and quoted extensively from a German legal opinion.
In May 2020, participants from the Open Energy Modelling Initiative made a further submission on the European strategy for data. In mid‑2021, participants made two written submissions on a proposed Data Act — legislative work-in-progress intended primarily to improve public interest business-to-government (B2G) information transfers within the European Economic Area (EEA). More specifically, the two Data Act submissions drew attention to restrictive but nonetheless compliant public disclosure reporting practices deployed by the European Energy Exchange (EEX).

62
data/en.wikipedia.org/wiki/Open_Energy_Modelling_Initiative-1.md Normal file
View File

@ -0,0 +1,62 @@
---
title: "Open Energy Modelling Initiative"
chunk: 2/2
source: "https://en.wikipedia.org/wiki/Open_Energy_Modelling_Initiative"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:49:27.553982+00:00"
instance: "kb-cron"
---
=== Public policy support ===
In May 2016, the European Union announced that "all scientific articles in Europe must be freely accessible as of 2020". This is a step in the right direction, but the new policy makes no mention of open software and its importance to the scientific process. In August 2016, the United States government announced a new federal source code policy which mandates that at least 20% of custom source code developed by or for any agency of the federal government be released as open-source software (OSS). The US Department of Energy (DOE) is participating in the program. The project is hosted on a dedicated website and subject to a three-year pilot. Open-source campaigners are using the initiative to advocate that European governments adopt similar practices. In 2017 the Free Software Foundation Europe (FSFE) issued a position paper calling for free software and open standards to be central to European science funding, including the flagship EU program Horizon 2020. The position paper focuses on open data and open data processing and the question of open modelling is not traversed per se.
=== Adoption by regulators and industry generally ===
A trend evident by 2023 is the adoption of regulators within the European Union and North America. Fairley (2023), writing in the IEEE Spectrum publication, provides an overview. And as one example, the Canada Energy Regulator is using the PyPSA framework for systems analysis.
== Workshops ==
The Open Energy Modelling Initiative participants take turns to host regular academic workshops.
The Open Energy Modelling Initiative also holds occasional specialist meetings.
== See also ==
Crowdsourcing
Energy system – the interpretation of the energy sector in system terms
Free Software Foundation Europe – a non-profit organisation advocating for free software in Europe
Open data
Open energy system models – a review of energy models that are also open source
Open energy system databases – database projects which collect, clean, and republish energy-related datasets
== Notes ==
== Further reading ==
Generation R open science blog on the openmod community 
Introductory video on open energy system modelling using the python language as an example 
Introductory video on the Open Energy Outlook (OEO) project specific to the United States
== External links ==
=== Official openmod ===
Official website
Wiki
Discussion forum
Email list archive
YouTube channel
GitHub account
Twitter feed
Manifesto written in 2014
=== Open energy data ===
Open Energy Platform – a collaborative versioned database for storing open energy system model datasets
Energypedia – a wiki-based collaborative knowledge exchange covering sustainable energy topics in developing countries
Open Power System Data project – triggered by the work of the Open Energy Modelling Initiative 
OpenEI – a US-based open energy data portal
=== Similar initiatives ===
soundsoftware.ac.uk – an open modelling community for acoustic and music software
=== Other ===
REEEM – a scientific project modelling sustainable energy futures for Europe
EERAdata – a project exploring FAIR energy data for Europe
== References ==

32
data/en.wikipedia.org/wiki/Open_Notebook_Science_Challenge-0.md Normal file
View File

@ -0,0 +1,32 @@
---
title: "Open Notebook Science Challenge"
chunk: 1/1
source: "https://en.wikipedia.org/wiki/Open_Notebook_Science_Challenge"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:49:31.605154+00:00"
instance: "kb-cron"
---
The Open Notebook Science Challenge is a crowdsourcing research project which collects measurements of the non-aqueous solubility of organic compounds and publishes these as open data; findings are reported in an open notebook science manner. Although anyone may contribute research data, the competition is only open to post-secondary students in the US and UK.
The challenge in turn forms part of the UsefulChem project, an ongoing open notebook science effort to synthesize and screen potential new anti-malarial drugs. Data from the Solubility Challenge will be used to build predictive computational models of solubility for use in optimising syntheses.
The challenge began on September 28, 2008 and, as of February 2014, involves researchers and their students from at least 4 different institutions and has resulted in the acquisition of over 7672 solubility measurements.
== Prizes ==
To encourage participation, each month an award is given to the student who does, in the opinion of the judges, the best work. In order to participate, students have to be a US or UK resident. The award is a US$500 cash prize. The first three winners also received a year's subscription to the journal Nature. The awards are sponsored by Submeta and Nature.
== Request an experiment ==
As well as concentrating on compounds related to the Ugi reaction, the ONSchallenge allows anyone to request a solubility measurement experiment.
== Chemical donations ==
Sigma Aldrich is also an official sponsor of the Open Notebook Science Challenge. Sigma Aldrich is participating by donating and shipping requested chemicals to any experimenters in the US or UK for free.
== References ==
== External links ==
Open Notebook Science Challenge

44
data/en.wikipedia.org/wiki/Open_energy_system_databases-0.md Normal file
View File

@ -0,0 +1,44 @@
---
title: "Open energy system databases"
chunk: 1/5
source: "https://en.wikipedia.org/wiki/Open_energy_system_databases"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:49:28.737080+00:00"
instance: "kb-cron"
---
Open energy system database projects employ open data methods to collect, clean, and republish energy-related datasets for open use. The resulting information is then available, given a suitable open license, for statistical analysis and for building numerical energy system models, including open energy system models. Permissive licenses like Creative Commons CC0 and CC BY are preferred, but some projects will house data made public under market transparency regulations and carrying unqualified copyright.
The databases themselves may furnish information on national power plant fleets, renewable generation assets, transmission networks, time series for electricity loads, dispatch, spot prices, and cross-border trades, weather information, and similar. They may also offer other energy statistics including fossil fuel imports and exports, gas, oil, and coal prices, emissions certificate prices, and information on energy efficiency costs and benefits.
Much of the data is sourced from official or semi-official agencies, including national statistics offices, transmission system operators, and electricity market operators. Data is also crowdsourced using public wikis and public upload facilities. Projects usually also maintain a strict record of the provenance and version histories of the datasets they hold. Some projects, as part of their mandate, also try to persuade primary data providers to release their data under more liberal licensing conditions.
Two drivers favor the establishment of such databases. The first is a wish to reduce the duplication of effort that accompanies each new analytical project as it assembles and processes the data that it needs from primary sources. And the second is an increasing desire to make public policy energy models more transparent to improve their acceptance by policymakers and the public. Better transparency dictates the use of open information, able to be accessed and scrutinized by third-parties, in addition to releasing the source code for the models in question.
== General considerations ==
=== Background ===
In the mid-1990s, energy models used structured text files for data interchange but efforts were being made to migrate to relational database management systems for data processing. These early efforts however remained local to a project and did not involve online publishing or open data principles.
The first energy information portal to go live was OpenEI in late 2009, followed by reegle in 2011.
A 2012 paper marks the first scientific publication to advocate the crowdsourcing of energy data. The 2012 PhD thesis by Chris Davis also discusses the crowdsourcing of energy data in some depth. A 2016 thesis surveyed the spatial (GIS) information requirements for energy planning and finds that most types of data, with the exception of energy expenditure data, are available but nonetheless remain scattered and poorly coordinated.
In terms of open data, a 2017 paper concludes that energy research has lagged behind other fields, most notably physics, biotechnology, and medicine. The paper also lists the benefits of open data and open models and discusses the reasons that many projects nonetheless remain closed. A one-page opinion piece from 2017 advances the case for using open energy data and modeling to build public trust in policy analysis. The article also argues that scientific journals have a responsibility to require that data and code be submitted alongside text for peer review.
=== Database design ===
Data models are central to the design and organization of databases. Open energy database projects generally try to develop and adhere to well resolved data models, using de facto and published standards where applicable. Some projects attempt to coordinate their data models in order to harmonize their data and improve its utility. Defining and maintaining suitable metadata is also a key issue. The life-cycle management of data includes, but is not limited to, the use of version control to track the provenance of incoming and cleansed data. Some sites allow users to comment on and rate individual datasets.
=== Dataset copyright and database rights ===
Issues surrounding copyright remain at the forefront with regard to open energy data. As noted, most energy datasets are collated and published by official or semi-official sources. But many of the publicly available energy datasets carry no license, limiting their reuse in numerical and statistical models, open or otherwise. Copyright protected material cannot lawfully be circulated, nor can it be modified and republished.
Measures to enforce market transparency have not helped much because the associated information is again not licensed to enable modification and republication. Transparency measures include the 2013 European energy market transparency regulation 543/2013. Indeed, 543/2013 "is only an obligation to publish, not an obligation to license". Notwithstanding, 543/2013 does enable downloaded data to be computer processed with legal certainty.
Energy databases with hardware located with the European Union are protected under a general database law, irrespective of the legal status of the information they hold.
Database rights not waived by public sector providers significantly restrict the amount of data a user can lawfully access.
A December 2017 submission by energy researchers in Germany and elsewhere highlighted a number of concerns over the re-use of public sector information within the Europe Union.
The submission drew heavily on a recent legal opinion covering electricity data.
=== Energy statistics ===
National and international energy statistics are published regularly by governments and international agencies, such as the IEA. In 2016 the United Nations issued guidelines for energy statistics. While the definitions and sectoral breakdowns are useful when defining models, the information provided is rarely in sufficient detail to enable its use in high-resolution energy system models.
=== Published standards ===
There are few published standards covering the collection and structuring of high-resolution energy system data. The IEC Common Information Model (CIM) defines data exchange protocols for low and high voltage electricity networks.
=== Non-open data ===
Although this page is about genuinely open data, some important databases remain closed.
Data collected by the International Energy Agency (IEA) is widely quoted in policy studies but remains nonetheless paywalled.
Researchers at Oxford University have called for this situation to change.

42
data/en.wikipedia.org/wiki/Open_energy_system_databases-1.md Normal file
View File

@ -0,0 +1,42 @@
---
title: "Open energy system databases"
chunk: 2/5
source: "https://en.wikipedia.org/wiki/Open_energy_system_databases"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:49:28.737080+00:00"
instance: "kb-cron"
---
== Open energy system database projects ==
Energy system models are data intensive and normally require detailed information from a number of sources. Dedicated projects to collect, collate, document, and republish energy system datasets have arisen to service this need. Most database projects prefer open data, issued under free licenses, but some will accept datasets with proprietary licenses in the absence of other options.
The OpenStreetMap project, which uses the Open Database License (ODbL), contains geographic information about energy system components, including transmission lines. Wikimedia projects such as Wikidata and Wikipedia have a growing set of information related to national energy systems, such as descriptions of individual power stations.
The following table summarizes projects that specifically publish open energy system data. Some are general repositories while others (for instance, oedb) are designed to interact with open energy system models in real-time.
Three of the projects listed work with linked open data (LOD), a method of publishing structured data on the web so that it can be networked and subject to semantic queries. The overarching concept is termed the semantic web. Technically, such projects support RESTful APIs, RDF, and the SPARQL query language. A 2012 paper reviews the use of LOD in the renewable energy domain.
=== Climate Compatible Growth starter datasets ===
The Climate Compatible Growth (CCG) programme provides starter kits for the following 69 countries: Algeria, Angola, Argentina, Benin, Botswana, Bolivia, Brazil, Burkina Faso, Burundi, Cambodia, Cameroon, Central African Republic, Chad, Chile, Colombia, Côte d'Ivoire, Democratic Republic of Congo, Djibouti, Ecuador, Egypt, Equatorial Guinea, Eritrea, Eswatini, Ethiopia, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Indonesia, Kenya, Laos, Lesotho, Liberia, Libya, Malawi, Malaysia, Mali, Mauritania, Morocco, Mozambique, Myanmar, Namibia, Niger, Nigeria, Papua New Guinea, Paraguay, Peru, Philippines, Republic of Congo, Republic of Korea, Rwanda, Senegal, Sierra Leone, Somalia, South Africa, South Sudan, Sudan, Taiwan, Tanzania, Thailand, Togo, Tunisia, Uganda, Uruguay, Venezuela, Viet Nam, Zambia, and Zimbabwe.
The datasets are hosted on the Zenodo science archive site, visit that site and search for "ccg starter kit".
=== Energy Research Data Portal for South Africa ===
The Energy Research Data Portal for South Africa is being developed by the Energy Research Centre, University of Cape Town, Cape Town, South Africa. Coverage includes South Africa and certain other African countries where the Centre undertakes projects. The website uses the CKAN open source data portal software. A number of data formats are supported, including CSV and XLSX. The site also offers an API for automated downloads. As of March 2017, the portal contained 65 datasets.
=== energydata.info ===
The energydata.info project from the World Bank Group, Washington, DC, USA is an energy database portal designed to support national development by improving public access to energy information. As well as sharing data, the platform also offers tools to visualize and analyze energy data. Although the World Bank Group has made available a number of dataset and apps, external users and organizations are encouraged to contribute. The concepts of open data and open source development are central to the project. energydata.info uses its own fork of the CKAN open source data portal as its web-based platform. The Creative Commons CC BY 4.0 license is preferred for data but other open licenses can be deployed. Users are also bound by the terms of use for the site.
As of January 2017, the database held 131 datasets, the great majority related to developing countries. The datasets are tagged and can be easily filtered. A number of download formats, including GIS files, are supported: CSV, XLS, XLSX, ArcGIS, Esri, GeoJSON, KML, and SHP. Some datasets are also offered as HTML. Again, as of January 2017, four apps are available. Some are web-based and run from a browser.
=== Enipedia ===
The semantic wiki-site and database Enipedia lists energy systems data worldwide. Enipedia is maintained by the Energy and Industry Group Archived 29 November 2016 at the Wayback Machine, Faculty of Technology, Policy and Management, Delft University of Technology, Delft, the Netherlands. A key tenet of Enipedia is that data displayed on the wiki is not trapped within the wiki, but can be extracted via SPARQL queries and used to populate new tools. Any programming environment that can download content from a URL can be used to obtain data. Enipedia went live in March 2011, judging by traffic figures quoted by Davis. 
A 2010 study describes how community driven data collection, processing, curation, and sharing is revolutionizing the data needs of industrial ecology and energy system analysis. A 2012 chapter introduces a system of systems engineering (SoSE) perspective and outlines how agent-based models and crowdsourced data can contribute to the solving of global issues.
As of April 2019, the site has gone offline pending a move to the enipedia.org domain.
=== Open Energy Platform ===
The Open Energy Platform (OEP) is a collaborative versioned dataset repository for storing open energy system model datasets. A dataset is presumed to be in the form of a database table, together with metadata. Registered users can upload and download datasets manually using a web-interface or programmatically via an API using HTTP POST calls. Uploaded datasets are screened for integrity using deterministic rules and then subject to confirmation by a moderator. The use of versioning means that any prior state of the database can be accessed (as recommended in this 2012 paper). Hence, the repository is specifically designed to interoperate with energy system models. The backend is a PostgreSQL object-relational database under subversion version control. Open-data licenses are specific to each dataset. Unlike other database projects, users can download the current version (the public tables) of the entire PostgreSQL database or any previous version. The development is being led by a cross-project community.
=== Open Data Energy Networks ===

39
data/en.wikipedia.org/wiki/Open_energy_system_databases-2.md Normal file
View File

@ -0,0 +1,39 @@
---
title: "Open energy system databases"
chunk: 3/5
source: "https://en.wikipedia.org/wiki/Open_energy_system_databases"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:49:28.737080+00:00"
instance: "kb-cron"
---
The Open Data Energy Networks (Open Data Réseaux Énergies or ODRÉ) portal is run by eight partners, led by the French national transmission system operator (TSO) Réseau de Transport d'Électricité (RTE). The portal was previously known as Open Data RTE. The site offers electricity system datasets under a Creative Commons CC BY 2.0 compatible license, with metadata, an RSS feed for notifying updates, and an interface for submitting questions. Re-users of information obtained from the site can also register third-party URLs (be they publications or webpages) against specific datasets.
The portal uses the French Government Licence Ouverte license and this is explicitly compatible with the United Kingdom Open Government Licence (OGL), the Creative Commons CC BY 2.0 license (and thereby later versions), and the Open Data Commons ODC-BY license.
The site hosts electricity, gas, and weather information related to France.
=== UK Power Networks Open Data Portal ===
The Open Data Portal is run by UK Power Networks, a GB Distribution Network Operator (DNO), hosted on the OpenDataSoft platform. The Portal offers electricity network datasets under a Creative Commons CC BY 4.0 compatible license, with metadata, a newsfeed, and a data request form. Re-users of information obtained from the site can also register third-party URLs (be they publications or webpages) against specific datasets. A number of download formats, including GIS files, are supported: CSV, XLS, GeoJSON, KML, and SHP. The site also offers an API for automated downloads.
The portal uses the Creative Commons License and also hosts datasets from other sources which are licensed under the Open Government Licence (OGL).
The site hosts electricity datasets related to UK Power Networks' three license areas in London, the East and South East of England.
=== Open Power System Data ===
The Open Power System Data (OPSD) project seeks to characterize the German and western European power plant fleets, their associated transmission network, and related information and to make that data available to energy modelers and analysts. The platform was originally implemented by the University of Flensburg, DIW Berlin, Technische Universität Berlin, and the energy economics consultancy Neon Neue Energieökonomik, all from Germany. The first phase of the project, from August 2015 to July 2017, was funded by the Federal Ministry for Economic Affairs and Energy (BMWi) for €490000. The project later received funding for a second phase, from January 2018 to December 2020, with ETH Zurich replacing Flensburg University as a partner.
Developers collate and harmonize data from a range of government, regulatory, and industry sources throughout Europe. The website and the metadata utilize English, whereas the original material can be in any one of 24 languages. Datasets follow the emerging frictionless data package standard being developed by Open Knowledge Foundation (OKF). The website was launched on 28 October 2016. As of June 2018, the project offers the following primary packages, for Germany and other European countries:
details, including geolocation, of conventional power plants and renewable energy power plants
aggregated generation capacity by technology and country
hourly time series covering electrical load, day-ahead electricity spot prices, and wind and solar resources
a script to filter and download NASA MERRA-2 satellite weather data
In addition, the project hosts selected contributed packages:
electricity demand and self-generation time series for representative south German households
simulated PV and wind generation capacity factor time series for Europe, generated by the Renewables.ninja project
To facilitate analysis, the data is aggregated into large structured files (in CSV format) and loaded into data packages with standardized machine-readable metadata (in JSON format). The same data is usually also provided as XLSX (Excel) and SQLite files. The datasets can be accessed in real-time using stable URLs. The Python scripts deployed for data processing are available on GitHub and carry an MIT license. The licensing conditions for the data itself depends on the source and varies in terms of openness. Previous versions of the datasets and scripts can be recovered in order to track changes or replicate earlier studies. The project also engages with energy data providers, such as transmission system operators (TSO) and ENTSO-E, to encourage them to make their data available under open licenses (for instance, Creative Commons and ODbL licenses).
In a 2019 publication, OPSD developers describe their design choices, implementation, and provisioning. Information integrity remains key, with each data package having traceable provenance, curation, and packing. From October 2018, each new or revised data package is assigned a unique DOI to ensure that external references to current and prior versions remain stable.
A number of published electricity market modeling analyses are based on OPSD data.
In 2017, the Open Power System Data project won the Schleswig-Holstein Open Science Award  and the Germany Land of Ideas award.
=== OpenEI ===

39
data/en.wikipedia.org/wiki/Open_energy_system_databases-3.md Normal file
View File

@ -0,0 +1,39 @@
---
title: "Open energy system databases"
chunk: 4/5
source: "https://en.wikipedia.org/wiki/Open_energy_system_databases"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:49:28.737080+00:00"
instance: "kb-cron"
---
Open Energy Information (OpenEI) is a collaborative website, run by the US government, providing open energy data to software developers, analysts, users, consumers, and policymakers. The platform is sponsored by the United States Department of Energy (DOE) and is being developed by the National Renewable Energy Laboratory (NREL). OpenEI launched on 9 December 2009. While much of its data is from US government sources, the platform is intended to be open and global in scope.
OpenEI provides two mechanisms for contributing structured information: a semantic wiki (using MediaWiki and the Semantic MediaWiki extension) for collaboratively-managed resources and a dataset upload facility for contributor-controlled resources. US government data is distributed under a CC0 public domain dedication, whereas other contributors are free to select an open data license of their choice. Users can rate data using a five-star system, based on accessibility, adaptability, usefulness, and general quality. Individual datasets can be manually downloaded in an appropriate format, often as CSV files. Scripts for processing data can also be shared through the site. In order to build a community around the platform, a number of forums are offered covering energy system data and related topics.
Most of the data on OpenEI is exposed as linked open data (LOD) (described elsewhere on this page). OpenEI also uses LOD methods to populate its definitions throughout the wiki with real-time connections to DBPedia, reegle, and Wikipedia.
OpenEI has been used to classify geothermal resources in the United States. And to publicize municipal utility rates, again within the US.
=== OpenGridMap ===
OpenGridMap employs crowdsourcing techniques to gather detailed data on electricity network components and then infer a realistic network structure using methods from statistics and graph theory. The scope of the project is worldwide and both distribution and transmission networks can be reverse engineered. The project is managed by the Chair of Business Information Systems, TUM Department of Informatics, Technical University of Munich, Munich, Germany. The project maintains a website and a Facebook page and provides an Android mobile app to help the public document electrical devices, such as transformers and substations. The bulk of the data is being made available under a Creative Commons CC BY 3.0 IGO license. The processing software is written primarily in Python and MATLAB and is hosted on GitHub.
OpenGridMap provides a tailored GIS web application, layered on OpenStreetMap, which contributors can use to upload and edit information directly. The same database automatically stores field recordings submitted by the mobile app. Subsequent classification by experts allows normal citizens to document and photograph electrical components and have them correctly identified. The project is experimenting with the use of hobby drones to obtain better information on associated facilities, such as photovoltaic installations. Transmission line data is also sourced from and shared with OpenStreetMap. Each component record is verified by a moderator.
Once sufficient data is available, the transnet software is run to produce a likely network, using statistical correlation, Voronoi partitioning, and minimum spanning tree (MST) algorithms. The resulting network can be exported in CSV (separate files for nodes and lines), XML, and CIM formats. CIM models are well suited for translation into software-specific data formats for further analysis, including power grid simulation. Transnet also displays descriptive statistics about the resulting network for visual confirmation.
The project is motivated by the need to provide datasets for high-resolution energy system models, so that energy system transitions (like the German Energiewende) can be better managed, both technically and policy-wise. The rapid expansion of renewable generation and the anticipated uptake of electric vehicles means that electricity system models must increasingly represent distribution and transmission networks in some detail.
As of 2017, OpenGridMap techniques have been used to estimate the low voltage network in the German city of Garching and to estimate the high voltage grids in several other countries.
=== Power Explorer ===
The Power Explorer portal is a part of the larger Resource Watch platform, hosted by the World Resources Institute. The initial Global Power Plant Database, an open source database of the power plants globally, was released in April 2018. As of May 2021, the portal itself is still under development.
Power Explorer is also supported by Google with various research partners, including KTH, Global Energy Observatory, Enipedia, and OPSD.
=== The Public Utility Data Liberation Project (PUDL) ===
The Public Utility Data Liberation Project (PUDL) maintains an open source data processing pipeline that cleans, integrates, and standardizes some of the most widely used public energy datasets in the US. This includes data from FERC, EIA, EPA, the SEC, the Pipeline and Hazardous Materials Safety Administration, the Rural Utilities Service, and other public agencies, as well as privately created datasets that have been published under Creative Commons licenses. Data products are updated frequently, with nightly build outputs published to free, public cloud buckets and quarterly versioned releases archived on Zenodo. PUDL's primary focus is on providing programmatically usable bulk data describing the physical, financial, and operational characteristics of the electricity system. The project also includes information about the US natural gas system and utility parent-subsidiary relationships. The data is intended to serve public-interest users that lack the technical expertise, time, or financial resources to process the data themselves or obtain it from commercial data providers.
The PUDL project was initiated by Catalyst Cooperative in 2017 and the worker co-op remains the primary maintainer. It has been supported by the Alfred P. Sloan Foundation, the National Science Foundation, the Mozilla Foundation, Climate Change AI, GridLab, RMI, and the AWS Open Data Registry.
=== PowerGenome ===
The PowerGenome project aims to provide a coherent dataset covering the United States electricity system. PowerGenome was initially designed to service the GenX model, but support for other modeling frameworks is in planning. The PowerGenome utility also pulls from upstream datasets hosted by the Public Utility Data Liberation project (PUDL) and the EIA, so those dependencies need to be met by users. Datasets are occasionally archived on Zenodo. A video describing the project is available.
=== reegle ===

50
data/en.wikipedia.org/wiki/Open_energy_system_databases-4.md Normal file
View File

@ -0,0 +1,50 @@
---
title: "Open energy system databases"
chunk: 5/5
source: "https://en.wikipedia.org/wiki/Open_energy_system_databases"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:49:28.737080+00:00"
instance: "kb-cron"
---
reegle is a clean energy information portal covering renewable energy, energy efficiency, and climate compatible development topics. reegle was launched in 2006 by REEEP and REN21 with funding from the Dutch (VROM), German (BMU), and UK (Defra) environment ministries. Originally released as a specialized internet search engine, reegle was relaunched in 2011 as an information portal.
reegle offers and utilizes linked open data (LOD) (described elsewhere on this page). Sources of data include UN and World Bank databases, as well as dedicated partners around the world. reegle maintains a comprehensive structured glossary (driven by an LOD-compliant thesaurus) of energy and climate compatible development terms to assist with the tagging of datasets. The glossary also facilitates intelligent web searches.
reegle offers country profiles which collate and display energy data on a per-country basis for most of the world. These profiles are kept current automatically using LOD techniques. As of 2021, the portal is no longer active.
=== Renewables.ninja ===
Renewables.ninja is a website that can calculate the hourly power output from solar photovoltaic installations and wind farms located anywhere in the world. The website is a joint project between the Department of Environmental Systems Science, ETH Zurich, Zürich, Switzerland and the Centre for Environmental Policy, Imperial College London, London, United Kingdom. The website went live during September 2016. The resulting time series are provided under a Creative Commons CC BY-NC 4.0 license (which is unfortunately not open data conformant) and the underlying power plant models are published using a BSD-new license. As of February 2017, only the solar model, written in Python, has been released.
The project relies on weather data derived from meteorological reanalysis models and weather satellite images. More specifically, it uses the 2016 MERRA-2 reanalysis dataset from NASA  and satellite images from CM-SAF SARAH. For locations in Europe, this weather data is further "corrected" by country so that it better fits with the output from known PV installations and windfarms. Two 2016 papers describe the methods used in detail in relation to Europe. The first covers the calculation of PV power. And the second covers the calculation of wind power.
The website displays an interactive world map to aid the selection of a site. Users can then choose a plant type and enter some technical characteristics. As of February 2017, only year 2014 data can be served, due to technical restrictions. The results are automatically plotted and are available for download in hourly CSV format with or without the associated weather information. The site offers an API for programmatic dataset recovery using token-based authorization. Examples deploying cURL and Python are provided.
A number of studies have been undertaking using the power production datasets underpinning the website (these studies predate the launch of the website), with the bulk focusing on energy options for Great Britain.
=== SMARD ===
The SMARD site (pronounced "smart") serves electricity market data from Germany, Austria, and Luxembourg and also provides visual information. The electricity market plots and their underlying time series are released under a permissive CC BY 4.0 license. The site itself was launched on 3 July 2017 in German and an English translation followed shortly. The data portal is mandated under the German Energy Industry Act (Energiewirtschaftsgesetz or EnWG) section §111d, introduced as an amendment on 13 October 2016. Four table formats are offered: CSV, XLS, XML, and PDF. The maximum sampling resolution is 15 min. Market data visuals or plots can be downloaded in PDF, SVG, PNG, and JPG formats. Representative output is shown in the thumbnail (on the left), in this case mid-winter dispatch over two days for the whole of Germany. The horizontal ordering by generation type is first split into renewable and conventional generation and then based on merit. A user guide is updated as required.
== See also ==
Comprehensive Knowledge Archive Network (CKAN) – a web-based open data management system
Climate change mitigation scenarios
Crowdsourcing
Energy modeling – the process of building computer models of energy systems
Energy system – the interpretation of the energy sector in system terms
Open Energy Modelling Initiative – a European-based energy modeling community
Open energy system models – a review of energy system models that are also open source
Open Knowledge Foundation – a global non-profit network that promotes and shares information
== Notes ==
== References ==
== Further information ==
Open energy data wiki maintained by the Open Energy Modelling Initiative
De Felice, Matteo (2020). "Freely available datasets of energy variables". openmod forum. Open Energy Modelling Initiative. Retrieved 1 December 2020. The list is under a Creative Commons CC‑BY‑4.0 license and many of the datasets cited are similarly licensed.
== External links ==
De-risking Energy Efficiency Platform (DEEP) – an open energy efficiency data platform for Europe
European Climatic Energy Mixes project (ECEM) — the role that climate change may play on future energy systems
OpenEnergy Database (oedb) – an open energy system database being developed in Germany
OpenEnergyMonitor – an open source energy use monitoring project
Domain‑wide data projects – a list of data related projects designed to support open energy system modeling

37
data/en.wikipedia.org/wiki/Open_energy_system_models-0.md Normal file
View File

@ -0,0 +1,37 @@
---
title: "Open energy system models"
chunk: 1/16
source: "https://en.wikipedia.org/wiki/Open_energy_system_models"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:49:30.157219+00:00"
instance: "kb-cron"
---
Open energy-system models are energy-system models that are open source. 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, which facilitates open science.
Energy-system models are used to explore future energy systems and are often applied to questions involving energy and climate policy. The models themselves vary widely in terms of their type, design, programming, application, scope, level of detail, sophistication, and shortcomings. For many models, some form of 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‑2020s. Open models and open data are increasingly being used by government agencies to guide the develop of net‑zero public policy as well. Companies and engineering consultancies are likewise adopting open models for analysis.
== General considerations ==
=== 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 public policy energy models more transparent to improve their acceptance by policymakers and the public. There is also a desire to leverage the benefits that open data and open software development can bring, including reduced duplication of effort, better sharing of ideas and information, improved quality, and wider engagement and adoption. Model development is therefore usually a team effort and constituted as either an academic project, a commercial venture, or a genuinely inclusive community initiative.
This article does not cover projects which simply make their source code or spreadsheets available for public download, but which omit a recognized free and open-source software license. The absence of a license agreement creates a state of legal uncertainty whereby potential users cannot know which limitations the owner may want to enforce in the future. 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. The paper makes a number of recommendations for projects wishing to transition to a more open approach. The authors also conclude that, in terms of openness, energy research has lagged behind other fields, most notably physics, biotechnology, and medicine.
=== 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. Balmorel was also active at that time, having been made public in 2001. As of July 2022, 31 such undertakings are listed here (with an approximately equal number waiting to be added).
Chang et al (2021) survey modeling trends and find the open to closed division about even after reviewing 54 frameworks — although that interpretation is based on project count and not on uptake and use. A 2022 model comparison exercise in Germany reported eight from 40 modeling projects (20%) were open source, these projects also had active communities behind them.
=== 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.
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". To further honor the process of peer review, researchers argue, in a 2012 paper, that it is essential to place both the source code and datasets under publicly accessible version control so that third-parties can run, verify, and scrutinize specific models. A 2016 paper contends that model-based energy scenario studies, seeking to influence decision-makers in government and industry, must become more comprehensible and more transparent. To these ends, the paper provides a checklist of transparency criteria that should be completed by modelers. The authors however state that they "consider open source approaches to be an extreme case of transparency that does not automatically facilitate the comprehensibility of studies for policy advice."
A one-page opinion piece from 2017 advances the case for using open energy data and modeling to build public trust in policy analysis. The article also argues that scientific journals have a responsibility to require that data and code be submitted alongside text for peer review. And an academic commentary from 2020 argues that distributed development would facilitate a more diverse contributor base and thus improve model quality — a process supported by online platforms and enabled by open data and code.
=== 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. As of February 2017, 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. The flagship JRC-EU-TIMES model however remains closed source.
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.
A 2021 research call from the European Union Horizon Europe scientific research funding program expressly sought energy system models that are open source.

41
data/en.wikipedia.org/wiki/Open_energy_system_models-1.md Normal file
View File

@ -0,0 +1,41 @@
---
title: "Open energy system models"
chunk: 2/16
source: "https://en.wikipedia.org/wiki/Open_energy_system_models"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:49:30.157219+00:00"
instance: "kb-cron"
---
=== Surveys ===
A survey completed in 2021 investigated the degree to which open energy-system modeling frameworks support flexibility options, broken down by supply, demand, 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 behavioral responses.
== Electricity sector models ==
Open electricity sector models are confined to just the electricity sector. These models invariably have a temporal resolution of one hour or less. Some models concentrate on the engineering characteristics of the system, including a good representation of high-voltage transmission networks and AC power flow. Others models depict electricity spot markets and are known as dispatch models. While other models embed autonomous agents to capture, for instance, bidding decisions using techniques from bounded rationality. The ability to handle variable renewable energy, transmission systems, and grid storage are becoming important considerations.
=== AMIRIS ===
AMIRIS is the open Agent-based Market model for the Investigation of Renewable and Integrated energy Systems. The AMIRIS simulation framework was first developed by the German Aerospace Center (DLR) in 2008 and later released as an open source project in 2021.
AMIRIS enables researchers to address questions regarding future energy markets, their market design, and energy-related policy instruments.
In particular, AMIRIS is able to capture market effects that may arise from the integration of 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. AMIRIS may also uncover complex effects that may emerge from the inter‑dependencies of the energy market participants.
The embedded market clearing algorithm computes electricity prices based on the bids of prototyped market actors. These bids may not only reflect the 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 official support instruments or exploit market power opportunities.
Actors in AMIRIS are represented as 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 electricity market. Finally, flexibility providers, such as storage operators, use forecasts to determine bidding patterns to match their particular objectives, for instance, projected profit maximization.
AMIRIS is based on the open Framework for distributed Agent-based Modelling of Energy systems or FAME.
AMIRIS can simulate large‑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.
=== Breakthrough Energy Model ===
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 and the DCOPF optimization problem is created via 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.
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. 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.
=== 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 power storage and other flexibility options in a future greenfield setting with high shares of renewable generation. DIETER is being developed at the German Institute for Economic Research (DIW), Berlin, Germany. The codebase and 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. DIETER is written in GAMS and was developed using the CPLEX commercial solver.
DIETER is framed as a pure linear (no integer variables) cost minimization problem. In the initial formulation, the decision variables include the investment in and dispatch of generation, storage, and DSM capacities in the German wholesale and balancing electricity markets. Later model extensions include vehicle-to-grid interactions and prosumage of solar electricity.
The first study using DIETER examines the power storage requirements for renewables uptake ranging from 60% to 100%. Under the baseline scenario of 80% (the lower bound German government target for 2050), grid 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.
=== Dispa-SET ===

28
data/en.wikipedia.org/wiki/Open_energy_system_models-10.md Normal file
View File

@ -0,0 +1,28 @@
---
title: "Open energy system models"
chunk: 11/16
source: "https://en.wikipedia.org/wiki/Open_energy_system_models"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:49:30.157219+00:00"
instance: "kb-cron"
---
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 Cbc solver. Alternatively, the GLPK, or CPLEX solvers can be employed. EnergyPATHWAYS utilizes the PostgreSQL object-relational database management system (ORDBMS) to manage its data.
EnergyPATHWAYS is a comprehensive accounting framework used to construct economy-wide energy infrastructure scenarios. While portions of the model do use linear programming techniques, for instance, for electricity dispatch, the EnergyPATHWAYS model is not fundamentally an optimization model and embeds few decision dynamics. EnergyPATHWAYS offers detailed energy, cost, and emissions accounting for the energy flows from primary supply to final demand. The energy system representation is flexible, allowing for differing levels of detail and the nesting of cities, states, and countries. The model uses hourly least-cost electricity dispatch and supports power-to-gas, short-duration energy storage, long-duration energy storage, and demand response. Scenarios typically run to 2050.
A predecessor of the EnergyPATHWAYS software, named simply PATHWAYS, has been used to construct policy models. The California PATHWAYS model was used to inform Californian state climate targets for 2030. And the US PATHWAYS model contributed to the United Nations Deep Decarbonization Pathways Project (DDPP) assessments for the United States. As of 2016, the DDPP plans to employ EnergyPATHWAYS for future analysis.
=== 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 July 2016, this looks out of date). A manual is available with the software. ETEM is written in MathProg. Presentations describing ETEM are available.
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 years, in two or five year steps, with time slices of four seasons using typically individual days or finer. The spatial resolution can be highly detailed. Electricity and heat are both supported, as are district heating networks, household energy systems, and grid storage, including the use of plug-in hybrid electric vehicles (PHEV). ETEM-SG, a development, supports demand response, an option which would be enabled by the development of smart grids.
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 model (CGEM) to complete the analysis. A 2012 study examines the design of smart grids. As distribution systems become more intelligent, so must the models needed to analysis them. ETEM is used to assess the potential of smart grid technologies using a case study, roughly calibrated on the Geneva canton, under three scenarios. These scenarios apply different constraints on CO2 emissions and electricity imports. A stochastic approach is used to deal with the uncertainty in future electricity prices and the uptake of electric vehicles.
=== ficus ===
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, Munich, Germany. The project maintains a website. The project is hosted on GitHub. ficus is written in Python and uses the Pyomo library. The user can choose between the open source GLPK solver or the commercial 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.
=== GENeSYS-MOD ===
The Global Energy System Model (GENeSYS‑MOD) is a linear cost-minimizing optimization model being developed at Technische Universität Berlin, Germany. The project was originally based on the OSeMOSYS framework and the first version was released in 2017 using GAMS. The codebase was later translated into Julia. Both versions and a representative dataset are available on GitHub.

33
data/en.wikipedia.org/wiki/Open_energy_system_models-11.md Normal file
View File

@ -0,0 +1,33 @@
---
title: "Open energy system models"
chunk: 12/16
source: "https://en.wikipedia.org/wiki/Open_energy_system_models"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:49:30.157219+00:00"
instance: "kb-cron"
---
GENeSYS‑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.
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 2019 study examined the low‑carbon transition of the European energy system and specifically the problem of stranded assets under a range of scenarios. It found that up to €200 billion in fossil-fueled capacities could be stranded by 2035 unless stronger policy signals are able to address short‑term planning biases. 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 photovoltaics and onshore wind, emerge as cost-effective solutions, but overcoming local resistance and increasing stakeholder engagement remain crucial for success. A 2021 study investigates the European Green Deal goal of achieving 100% 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 electrification combined with near‑term technology deployment to achieve the necessarily rapid decarbonization.
=== GenX ===
GenX is multi‑commodity sector capacity expansion model originally developed by researchers in the United States. The framework is written in Julia and deploys the JuMP library for building the underlying optimization problem. GenX through JuMP can utilize various open source (including CBC/CLP) and commercial optimization solvers (including CPLEX). In June 2021, the project launched as an active open source project and test suites are available to assist onboarding.
In parallel, the PowerGenome project is designed to provide GenX with a comprehensive current state dataset of the United States electricity system. 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, to explore the value of 'firm' low-carbon power generation options, and a variety of other applications. While North America remains a key focus, the software has been applied to problems in India, Italy, and Spain.
GenX was deployed in a 2021 case study with Louisville Gas and Electric and Kentucky Utilities that showed that stakeholder-driven modeling utilizing open‑source tools and public data can contribute productively to utility‑led analysis and planning.
A mid‑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 2022. 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 drought conditions on hydro generation and the system more generally.
=== oemof ===
oemof stands for Open Energy Modelling Framework. The project is managed by the Reiner Lemoine Institute, Berlin, Germany and the Center for Sustainable Energy Systems (CSES or ZNES) at the 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 COIN-OR components for optimization. Energy systems can be represented using spreadsheets (CSV) which should simplify data preparation. Version 0.1.0 was released on 1 December 2016.
oemof classes as an energy modeling framework. It consists of a 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‑Saharan Africa. A masters project in 2020 compared oemof and OSeMOSYS.
=== 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, Stockholm, Sweden. The project maintains a website providing background. The project also offers several active internet forums on Google Groups. OSeMOSYS was originally written in MathProg, a high-level mathematical programming language. It was subsequently reimplemented in GAMS and Python and all three codebases are now maintained. The project also provides a test model called UTOPIA. A manual is available.
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 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 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 lines of code. OSeMOSYS has been used as a base for constructing reduced models of energy systems.

24
data/en.wikipedia.org/wiki/Open_energy_system_models-12.md Normal file
View File

@ -0,0 +1,24 @@
---
title: "Open energy system models"
chunk: 13/16
source: "https://en.wikipedia.org/wiki/Open_energy_system_models"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:49:30.157219+00:00"
instance: "kb-cron"
---
A key paper describing OSeMOSYS is available. A 2011 study uses OSeMOSYS to investigate the role of household investment decisions. A 2012 study extends OSeMOSYS to capture the salient features of a smart grid. The paper explains how to model variability in generation, flexible demand, and grid storage and how these impact on the stability of the grid. 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. In a 2016 study, OSeMOSYS is modified to take into account realistic consumer behavior. 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. 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. 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.
OSeMOSYS has been used to construct wide-area electricity models for Africa, comprising 45 countries and South America, comprising 13 countries. It has also been used to support United Nations' regional climate, land, energy, and water strategies (CLEWS) for the Sava river basin, central Europe, the Syr Darya river basin, eastern Europe, and Mauritius. Models have previously been built for the Baltic States, Bolivia, Nicaragua, Sweden, and Tanzania. A 2021 paper summarizes recent applications and also details various versions, forks, and local enhancements related to the OSeMOSYS codebase. An electricity sector analysis for Bangladesh completed in 2021 concluded that solar power is economically competitive under every investigated scenario. A 2022 study looked at the effects of a changing climate on the Ethiopian power system. OSeMOSYS has also been applied variously in Zimbabwe  and Ecuador. Another 2022 study examined water usage, split by withdraws and consumption, for several low carbon energy strategies for Africa. Another study that year examined renewable energy in Egypt. And another the Dominican Republic. The Italian island of Pantelleria was used as a case study to compare battery and hydrogen storage and found that a hybrid system was least cost.
In 2016, work started on a 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. A simplified GUI interface named clicSAND and utilizing Excel and Access was released in March 2021. A CLI workflow tool named otoole bundles several dedicated utilities, including one that can convert between OKI frictionless data and GNU MathProg data formats. In 2022, the project released starter kits for modeling selected countries in Africa, East Asia, and South America.
The OSeMBE reference model covering western and central Europe was announced on 27 April 2018. The model uses the MathProg implementation of OSeMOSYS but requires a small 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. 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.
==== OSeMOSYS Global project ====
The OSeMOSYS community launched the OSeMOSYS Global project in 2022 to create a global model and associated workflows. As of late‑2022, OSeMOSYS Global is limited in scope to the electricity sector and the world system provided comprises 164 countries separated by 265 nodes.
=== 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. It supports conventional generation, variable wind and solar generation, electricity storage, 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), Karlsruhe, Germany, although the project itself exists independently under its own name and accounts. The project maintains a website and runs an email list. PyPSA itself is written in Python and uses the linopy library. The source code is hosted on GitHub and is also released periodically as a PyPI package.

29
data/en.wikipedia.org/wiki/Open_energy_system_models-13.md Normal file
View File

@ -0,0 +1,29 @@
---
title: "Open energy system models"
chunk: 14/16
source: "https://en.wikipedia.org/wiki/Open_energy_system_models"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:49:30.157219+00:00"
instance: "kb-cron"
---
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 price inelastic.
A 2018 study examines potential synergies between sector coupling and transmission reinforcement in a future European energy system constrained to reduce carbon emissions by 95%. The PyPSA-Eur-Sec-30 model captures the demand-side management potential of battery electric vehicles (BEV) as well as the role that power-to-gas, long-term 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.
As of January 2018, PyPSA is used by more than a dozen research institutes and companies worldwide. Some research groups have independently extended the software, for instance to model integer transmission expansion.
In 2020, the PyPSA‑Eur‑Sec model for Europe was used to analyze several Paris Agreement Compatible Scenarios for Energy Infrastructure  and determined that early action should pay off.
On 9 January 2019, the project released an interactive web-interfaced "toy" model, using the Cbc solver, to allow the public to experiment with different future costs and technologies. The site was relaunched on 5 November 2019 with some internal improvements, a new URL, and faster solver now completing in about 12 s. A newer version now uses the HiGHS solver.
During September 2021, PyPSA developers announced the PyPSA‑Server project to provide a web interface to a simplified version of their PyPSA‑Eur‑Sec sector‑coupled European model. Users need not install software and can define fresh scenarios "by difference" using a forms‑based webpage. Previously run scenarios are stored for future reference. The implementation as of October 2021 is essentially proof‑of‑concept.
In late‑2021, PyPSA‑Eur developers reported their investigation into integrated high-voltage electricity and hydrogen grid expansion options for Europe and the United Kingdom and the impact of the kind of trade‑offs that might stem from limited public acceptance of new infrastructure. Subsequent work added endogenous learning effects and identified steeper technology cost reductions than those anticipated by the European Commission. Work published in 2024 integrated PyPSA‑Eur with the global energy supply chain model TRACE and highlighted the need to coordinate infrastructure policies and import strategies.
A December 2021 study and ongoing work deployed a PyPSA‑PL model to assess policy options for Poland. Edinburgh University researchers published an independent power system model for Britain named PyPSA‑GB in 2024, together with assessments of official net‑zero Future Energy Scenarios (FES) from the UK National Grid.
Several PyPSA maintainers announced a new non‑profit startup in June 2023 to provide consulting services using PyPSA.
==== PyPSA meets Earth initiative ====
The PyPSA meets Earth initiative arose in October 2022 as a means of gathering together several historically disjoint PyPSA applications. One key strand is the PyPSA‑Africa project (previously PyPSA-meets-Africa), launched some months earlier to provide a single model and dataset spanning the African continent. A July 2022 webinar co‑hosted by CPEEL, Nigeria advanced this agenda. The first research paper, released in 2022, examines various pathways for Africa to be net zero by 2060 — with solar power and battery storage expected to be the predominant technologies.
Another key strand of the initiative is the PyPSA‑Earth project which seeks to create a global energy systems model at high spatial and temporal resolution. The project hopes to encourage large‑scale collaboration by providing software and processes that can capture the global energy system and thus also any subset of it. The codebase supports system integration studies that draw together electricity generation, storage, and transmission expansion. A sector-coupled version includes demand from transport, buildings, industry, services and agriculture. It includes hydrogen transmission and repurposing of gas infrastructure for hydrogen.
=== REMix ===
REMix stands for "Renewable Energy Mix". It is an open source framework developed by the German Aerospace Center for setting up linear or mixed integer optimization models written in GAMS. A framework is understood as a collection of mutually compatible source codes 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, 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.

42
data/en.wikipedia.org/wiki/Open_energy_system_models-14.md Normal file
View File

@ -0,0 +1,42 @@
---
title: "Open energy system models"
chunk: 15/16
source: "https://en.wikipedia.org/wiki/Open_energy_system_models"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:49:30.157219+00:00"
instance: "kb-cron"
---
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 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.
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 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 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-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.
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, for example, the role of hydrogen in the energy system of the future has also been examined.
For the purpose of validating the REMix model, German Aerospace Center has participated in various model comparisons.
=== TEMOA ===
TEMOA stands for Tools for Energy Model Optimization and Analysis. The software is being developed by the Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, North Carolina, USA. The project runs a website and a forum. The source code is hosted on GitHub. The model is programmed in Pyomo, an optimization components library written in Python. TEMOA can be used with any solver that Pyomo supports, including the open source GLPK solver. TEMOA uses version control to publicly archive source code and datasets and thereby enable third-parties to verify all published modeling work.
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. TEMOA is "strongly influenced by the well-documented MARKAL/TIMES model generators".
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.
From mid‑2021, an interactive interface located on the main website allows registered users to manipulate scenario data locally, upload structured SQLite files, and then run these scenarios using the TEMOA software. The service also provides some limited data visualization and project management functionality.
== 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 ===
RAMP is an open-source software suite for the stochastic simulation of user‑driven energy demand time series based on few simple inputs. For example, a minimal definition of a user type — say, a particular category of household — 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  or when looking forward to future electric-vehicle fleets. The limited data requirements also allow for a greater flexibility in scenario selection and development than similar but more data-intensive characterizations.
RAMP has been used in scientific research for a variety of use cases, 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‑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 San Simón.
RAMP runs on Python and requires input in tabular form. Graphical user interfaces (GUI) are available, allowing the software to be run from web browsers.
=== venco.py ===
The venco.py model framework can be used to investigate interactions between the uptake of battery electric vehicles (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.
Venco.py has been applied to various scenarios in Germany in 2030 using a projected 9 million BEVs in service and an annual fleet power consumption of 27 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. The mathematical formulation is available. Venco.py builds on an earlier spreadsheet prototype.
== Project statistics ==
Statistics for the 30 open energy modeling projects listed (given sufficient information is available) are as follows:

79
data/en.wikipedia.org/wiki/Open_energy_system_models-15.md Normal file
View File

@ -0,0 +1,79 @@
---
title: "Open energy system models"
chunk: 16/16
source: "https://en.wikipedia.org/wiki/Open_energy_system_models"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:49:30.157219+00:00"
instance: "kb-cron"
---
The GAMS language requires a proprietary environment and its significant cost effectively limits participation to those who can access an institutional copy.
== Programming components ==
Programming components, in this context, are coherent blocks of code or compiled libraries that can be relatively easily imported or linked to by higher‑level modeling frameworks in order to obtain some well‑defined functionality.
=== Technology modules ===
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
pvlib photovoltaics facility library
hplib heat pump facility library
windpowerlib wind turbine library
hydropowerlib hydroelectricity library
=== Auction models ===
A number of electricity auction models have been written in GAMS, AMPL, MathProg, and other languages. These include:
the EPOC nodal pricing model
Australian National Electricity Market examples using MathProg can be found at b:GLPK/Electricity markets
=== Open solvers ===
Many projects rely on a 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. From mid‑2022, the HiGHS open source solver offers another option. HiGHS is used by the web‑based version of the PyPSA European multi‑sector model
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.
The flexible SMS++ optimization toolbox, written in C++17, is being developed specifically to meet the needs of energy system modeling.
== See also ==
General
Building energy simulation – the modeling of energy flows in buildings
Climate change mitigation scenarios
Energy modeling – the process of building computer models of energy systems
Energy system – the interpretation of the energy sector in system terms
Open Energy Modelling Initiative – a European-based energy modeling community
Open energy system databases – database projects which collect, clean, and republish energy-related datasets
Unit commitment problem in electrical power production
Software
List of free and open-source optimization solvers
Cbc (COIN-OR Branch and Cut) – an open source optimization solver
Clp (COIN-OR LP) – an open source linear optimization solver
Community Climate System Model – a mostly open source coupled global climate model
ESMF (Earth System Modeling Framework) – open source software for building climate, numerical weather prediction, and data assimilation applications
GHGProof – an open source land-use model
GLPK (GNU Linear Programming Kit) – an open source linear and mixed integer optimization solver
GridLAB-D – an open source simulation and analysis tool for smart grid energy technologies
GridSpice – an open source cloud-based simulation package for modelling smart grids
HiGHS – an open source optimization solver
People
Joe DeCarolis – energy system modeler and current head of the United States Energy Information Administration
== Notes ==
== References ==
== Further information ==
The following lists and databases cover energy system models to varying degrees of completeness and usually with a focus on open source:
Open energy models wiki maintained by the Open Energy Modelling Initiative
Open Energy Platform factsheets — structured summaries covering a range of open and closed energy system models
Global Power System Transformation Consortium database — filterable database of open models and related projects
Linux Foundation Energy inventory — allied projects with an emphasis on industrial rather than policy applications
== External links ==
Modeling efforts by region
Africa: reports and publications — broken down by region and country
Latin America: reports and publications — broken down by region and country
Oceania: reports and publications — broken down by region and country

30
data/en.wikipedia.org/wiki/Open_energy_system_models-2.md Normal file
View File

@ -0,0 +1,30 @@
---
title: "Open energy system models"
chunk: 3/16
source: "https://en.wikipedia.org/wiki/Open_energy_system_models"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:49:30.157219+00:00"
instance: "kb-cron"
---
Under development at the European Commission's Joint Research Centre (JRC), Petten, the Netherlands, Dispa-SET is a unit commitment and dispatch model intended primarily for Europe. It is written in Python (with Pyomo) and GAMS and uses Python for data processing. A valid GAMS license is required. The model is formulated as a mixed integer problem and JRC uses the proprietary CPLEX sover although open source libraries may also be deployed. Technical descriptions are available for versions 2.0  and 2.1. Dispa-SET is hosted on GitHub, together with a trial dataset, and third-party contributions are encouraged. The codebase has been tested on Windows, macOS, and Linux. Online documentation is available.
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.
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 inelastic. The system is subject to intra-period and inter-period unit commitment constraints (the latter covering nuclear and thermal generation for the most part) and operated under economic dispatch. 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 hours ahead but retaining results for just the first 24 hours.
Two related publications describe the role and representation of flexibility measures within power systems facing ever greater shares of variable renewable energy (VRE). These flexibility measures comprise: dispatchable generation (with constraints on efficiency, ramp rate, part load, and up and down times), conventional storage (predominantly pumped-storage hydro), cross-border interconnectors, demand side management, renewables curtailment, last resort load shedding, and nascent power-to-X solutions (with X being gas, heat, or mobility). The modeler can set a target for renewables and place caps on CO2 and other pollutants. Planned extensions to the software include support for simplified AC power flow  (transmission is currently treated as a transportation problem), new constraints (like cooling water supply), stochastic scenarios, and the inclusion of markets for ancillary services.
Dispa-SET has been or is being applied to case studies in Belgium, Bolivia, Greece, Ireland, and the Netherlands. A 2014 Belgium study investigates 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.
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.
Dispa-SET has been applied in Africa with soft linking to the LISFLOOD model to examine water‑energy nexus problems in the context of a changing climate.
=== E4ST ===
See e4st.org or www.rff.org/topics/data-and-decision-tools/e4st/.
=== EMLab-Generation ===
EMLab-Generation is an agent-based model covering two interconnected electricity markets – be they two adjoining countries or two groups of countries. The software is being developed at the Energy Modelling Lab, Delft University of Technology, Delft, the Netherlands. A factsheet is available. And software documentation is available. EMLab-Generation is written in Java.
EMLab-Generation simulates the actions of power companies investing in generation capacity and uses this to explore the long-term effects of various energy and climate protection policies. These policies may target renewable generation, CO2 emissions, security of supply, and/or energy affordability. The power companies are the main agents: they bid into power markets and they invest based on the net present value (NPV) of prospective power plant projects. They can adopt a variety of technologies, using scenarios from the 2011 IEA World Energy Outlook. 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 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.
=== EMMA ===

23
data/en.wikipedia.org/wiki/Open_energy_system_models-3.md Normal file
View File

@ -0,0 +1,23 @@
---
title: "Open energy system models"
chunk: 4/16
source: "https://en.wikipedia.org/wiki/Open_energy_system_models"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:49:30.157219+00:00"
instance: "kb-cron"
---
EMMA is the European Electricity Market Model. It is a techno-economic model covering the integrated Northwestern European power system. EMMA is being developed by the energy economics consultancy Neon Neue Energieökonomik, Berlin, Germany. The source code and datasets can be downloaded from the project website. A manual is available. EMMA is written in GAMS and uses the CPLEX commercial solver.
EMMA models electricity dispatch and investment, minimizing the total cost with respect to investment, generation, and trades between market areas. In economic terms, EMMA classifies as a partial equilibrium model of the wholesale electricity market with a focus on the supply-side. EMMA identifies short-term or long-term optima (or equilibria) and estimates the corresponding capacity mix, hourly prices, dispatch, and cross-border trading. Technically, EMMA is a pure linear program (no integer variables) with about two million non-zero variables. As of 2016, the model covers Belgium, France, Germany, the Netherlands, and Poland and supports conventional generation, renewable generation, and cogeneration.
EMMA has been used to study the economic effects of the increasing penetration of variable renewable energy (VRE), specifically solar power and wind power, in the Northwestern European power system. A 2013 study finds that increasing VRE shares will depress prices and, as a consequence, the competitive large-scale deployment of renewable generation will be more difficult to accomplish than many anticipate. 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.
An independent 2015 study reviews the EMMA model and comments on the high assumed specific costs for renewable investments.
=== GENESYS ===
GENESYS stands for Genetic Optimisation of a European Energy Supply System. The software is being developed jointly by the Institute of Power Systems and Power Economics (IAEW) and the Institute for Power Electronics and Electrical Drives (ISEA), both of RWTH Aachen University, Aachen, Germany. The project maintains a website where potential users can request access to the codebase and the dataset for the 2050 base scenario only. Detailed descriptions of the software are available. GENESYS is written in C++ and uses Boost libraries, the MySQL relational database, the Qt 4 application framework, and optionally the CPLEX solver.
The GENESYS simulation tool is designed to optimize a future EUMENA (Europe, Middle East, and North Africa) power system and assumes a high share of renewable generation. It is able to find an economically optimal distribution of generator, storage, and transmission capacities within a 21 region EUMENA. It allows for the optimization of this energy system in combination with an evolutionary method. The optimization is based on a covariance matrix adaptation evolution strategy (CMA-ES), while the operation is simulated as a hierarchical set-up of system elements which balance the load between the various regions at minimum cost using the network simplex algorithm. GENESYS ships with a set of input time series and a set of parameters for the year 2050, which the user can modify.
A future EUMENA energy supply system with a high share of renewable energy sources (RES) will need a strongly interconnected energy transport grid and significant energy storage capacities. GENESYS was used to dimension the storage and transmission between the 21 different regions. Under the assumption of 100% self-supply, about 2500 GW of RES in total and a storage capacity of about 240000 GWh are needed, corresponding to 6% of the annual energy demand, and a HVDC transmission grid of 375000 GW·km. The combined cost estimate for generation, storage, and transmission, excluding distribution, is 6.87 ¢/kWh.
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.
=== NEMO ===

23
data/en.wikipedia.org/wiki/Open_energy_system_models-4.md Normal file
View File

@ -0,0 +1,23 @@
---
title: "Open energy system models"
chunk: 5/16
source: "https://en.wikipedia.org/wiki/Open_energy_system_models"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:49:30.157219+00:00"
instance: "kb-cron"
---
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), Sydney, Australia since 2011. The project maintains a small website and runs an email list. NEMO is written in Python. NEMO itself is described in two publications. The data sources are also noted. Optimizations are carried out using a single-objective evaluation function, with penalties. The solution space of generator capacities is searched using the CMA-ES (covariance matrix adaptation evolution strategy) algorithm. The timestep is arbitrary but one hour is normally employed.
NEMO has been used to explore generation options for the year 2030 under a variety of renewable energy (RE) and abated fossil fuel technology scenarios. A 2012 study investigates the feasibility of a fully renewable system using concentrated solar power (CSP) with thermal storage, windfarms, photovoltaics, existing hydroelectricity, and biofuelled gas turbines. 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. A 2014 study investigates three scenarios using coal-fired thermal generation with carbon capture and storage (CCS) and gas-fired gas turbines with and without capture. These scenarios are compared to the 2012 analysis using fully renewable generation. The study finds that "only under a few, and seemingly unlikely, combinations of costs can any of the fossil fuel scenarios compete economically with 100% renewable electricity in a carbon constrained world". 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%.
=== OnSSET ===
OnSSET is the OpeN Source Spatial Electrification Toolkit. OnSSET is being developed by the division of Energy Systems, KTH Royal Institute of Technology, Stockholm, Sweden. The software is used to examine areas not served by grid-based electricity and identify the technology options and investment requirements that will provide least-cost access to electricity services. OnSSET is designed to support the United Nations' SDG 7: the provision of affordable, reliable, sustainable, and modern energy for all. The toolkit is known as OnSSET and was released on 26 November 2016. OnSSET does not ship with data, but suitable datasets are available from energydata.info. The project maintains a website and runs a mailing list.
OnSSET can estimate, analyze, and visualize the most cost-effective electrification access options, be they conventional grid, mini-grid, or stand-alone. The toolkit supports a range of conventional and renewable energy technologies, including photovoltaics, wind turbines, and small hydro generation. As of 2017, bioenergy and hybrid technologies, such as wind-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 GIS information can be supported using the proprietary ArcGIS package or an open source equivalent such as GRASS or QGIS. OnSSET has been applied to microgrids using a three‑tier analysis starting with settlement archetypes.
OnSSET has been used for case studies in Afghanistan, Bolivia, Cameroon, Ethiopia, Malawi, Nigeria, and Tanzania. OnSSET has also been applied in India, Kenya, and Zimbabwe. In addition, continental studies have been carried out for Sub-Saharan Africa and Latin America. A 4‑way GIS‑based study set in Nigeria reported that OnSSET offered the best set of capabilities.
OnSSET results have contributed to the IEA World Energy Outlook reports for 2014  and 2015, the World Bank Global Tracking Framework report in 2015, and the IEA Africa Energy Outlook report in 2019. OnSSET also forms part of the nascent GEP platform.
=== pandapower ===

28
data/en.wikipedia.org/wiki/Open_energy_system_models-5.md Normal file
View File

@ -0,0 +1,28 @@
---
title: "Open energy system models"
chunk: 6/16
source: "https://en.wikipedia.org/wiki/Open_energy_system_models"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:49:30.157219+00:00"
instance: "kb-cron"
---
pandapower is a power system analysis and optimization program being jointly developed by the Energy Management and Power System Operation research group, University of Kassel and the Department for Distribution System Operation, Fraunhofer Institute for Energy Economics and Energy System Technology (IEE), both of Kassel, Germany. The codebase is hosted on GitHub and is also available as a package. The project maintains a website, an emailing list, and online documentation. pandapower is written in Python. It uses the pandas library for data manipulation and analysis and the PYPOWER library  to solve for power flow. Unlike some open source power system tools, pandapower does not depend on proprietary platforms like MATLAB.
pandapower supports the automated analysis and optimization of 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 matplotlib and 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 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.
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-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 jupyter notebooks.
As of 2018, BNetzA, the German network regulator, is using pandapower for automated grid analysis. Energy research institutes in Germany are also following the development of pandapower.
=== PowerMatcher ===
The PowerMatcher software implements a smart grid coordination mechanism which balances distributed energy resources (DER) and flexible loads through autonomous bidding. The project is managed by the Flexiblepower Alliance Network (FAN) in Amsterdam, the Netherlands. The project maintains a website and the source code is hosted on GitHub. As of June 2016, existing datasets are not available. PowerMatcher is written in 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.
=== Power TAC ===
Power TAC stands for Power Trading Agent Competition. Power TAC is an agent-based model simulating the performance of retail markets in an increasingly 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 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.
=== renpass ===

28
data/en.wikipedia.org/wiki/Open_energy_system_models-6.md Normal file
View File

@ -0,0 +1,28 @@
---
title: "Open energy system models"
chunk: 7/16
source: "https://en.wikipedia.org/wiki/Open_energy_system_models"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:49:30.157219+00:00"
instance: "kb-cron"
---
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), University of Flensburg, Germany. The project runs a website, from where the codebase can be download. renpass is written in R and links to a MySQL database. A PDF manual is available. renpass is also described in a PhD thesis. 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 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 €/MWh. A 2014 study uses renpass to model Germany and its neighbors. A 2014 thesis uses renpass to examine the benefits of both a new cable between Germany and Norway and new pumped storage capacity in Norway, given 100% renewable electricity systems in both countries. Another 2014 study uses renpass to examine the German Energiewende, the transition to a sustainable energy system for Germany. The study also argues that the public trust needed to underpin such a transition can only be built through the use of transparent open source energy models.
=== sci2grid ===
sci2grid is a software tool for extracting, filtering, and processing data to build open-source grid models. The main objective of sci2grid is to offer easily accessible and transparent models that support further applications in science, industry, and society.
The sci2grid data models are designed to primarily process georeferenced information for objects such as supply lines, substations or compressor stations. This information is automatically extracted from open data sources and may include geo-coordinates, line lengths and diameters, or installed capacities. In addition, complementary information is manually collected from individual online sources, such as press articles, verified, and integrated into the models. Missing data is estimated using heuristic methods to provide consistent and comprehensive data models for subsequent analyses. At present, it provides gas transport and electricity transmission grid models for Europe that can be used to investigate grid-related challenges.
Such openly available and transparent grid models have a wide range of applications. They can be used for the assessment of energy system scenarios, the simulation of grid operation, the identification of bottlenecks in energy supply, the evaluation of grid development plans, and the determination of future grid expansion needs.
The current focus of sci2grid is on electricity transmission and gas transport infrastructures in Europe. However, the methods developed can also be applied to individual countries or other geographical regions worldwide.
=== SIREN ===
SIREN stands for SEN Integrated Renewable Energy Network Toolkit. The project is run by Sustainable Energy Now, an NGO based in Perth, Australia. The project maintains a website. SIREN runs on Windows and the source code is hosted on SourceForge. The software is written in Python and uses the SAM model (System Advisor Model) from the US National Renewable Energy Laboratory to perform energy calculations. SIREN uses hourly datasets to model a given geographic region. Users can use the software to explore the location and scale of renewable energy sources to meet a specified electricity demand. SIREN utilizes a number of open or publicly available data sources: maps can be created from OpenStreetMap tiles and weather datasets can be created using NASA MERRA-2 satellite data.
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 million tonnes of CO2eq emissions would be avoided. The modeling assumes a carbon price of AUD $30/tCO2. Further scenarios examine the goal of 100% renewable generation.
=== SWITCH ===

27
data/en.wikipedia.org/wiki/Open_energy_system_models-7.md Normal file
View File

@ -0,0 +1,27 @@
---
title: "Open energy system models"
chunk: 8/16
source: "https://en.wikipedia.org/wiki/Open_energy_system_models"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:49:30.157219+00:00"
instance: "kb-cron"
---
SWITCH is a loose acronym for solar, wind, conventional and hydroelectric generation, and transmission. SWITCH is an optimal planning model for power systems with large shares of renewable energy. SWITCH is being developed by the Department of Electrical Engineering, University of Hawaiʻi at Mānoa, Hawaii, USA. The project runs a small website and hosts its codebase and datasets on GitHub. SWITCH is written in Pyomo, an optimization components library programmed in Python. It can use either the open source GLPK solver or the commercial 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 pumped hydro facility, or transfer along each transmission interconnector. The system must also ensure enough generation and transmission capacity to provide a planning reserve margin of 15% above the load forecasts. For each sampled hour, SWITCH uses electricity demand and renewable power production based on actual measurements, so that the weather-driven correlations between these elements remain intact.
Following the optimization phase, SWITCH is used in a second phase to test the proposed investment plan against a more complete set of weather conditions and to add backstop generation capacity so that the planning reserve margin is always met. Finally, in a third phase, the costs are calculated by freezing the investment plan and operating the proposed power system over a full set of weather conditions.
A 2012 paper uses California from 2012 to 2027 as a case study for SWITCH. The study finds that there is no ceiling on the amount of wind and solar power that could be used and that these resources could potentially reduce emissions by 90% or more (relative to 1990 levels) without reducing reliability or severely raising costs. Furthermore, policies that encourage electricity customers to shift demand to times when renewable power is most abundant (for example, though the well-timed charging of electric vehicles) could achieve radical emission reductions at moderate cost.
SWITCH was used more recently to underpin consensus-based power system planning in Hawaii. The model is also being applied in Chile, Mexico, and elsewhere.
Major version 2.0 was released in late‑2018. An investigation that year favorably compared SWITCH with the proprietary General Electric MAPS model using Hawaii as a case study.
=== URBS ===
URBS, Latin for city, is a linear programming model for exploring capacity expansion and unit commitment problems and is particularly suited to distributed energy systems (DES). It is being developed by the Institute for Renewable and Sustainable Energy Systems, Technical University of Munich, Germany. The codebase is hosted on GitHub. URBS is written in 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.
The software has been used to explore cost-optimal extensions to the European transmission grid using projected wind and solar capacities for 2020. A 2012 study, using high spatial and technological resolutions, found variable renewable energy (VRE) additions cause lower revenues for conventional power plants and that grid extensions redistribute and alleviate this effect. The software has also been used to explore energy systems spanning Europe, the Middle East, and North Africa (EUMENA) and Indonesia, Malaysia, and Singapore.
== 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 district heating. Other models add gas networks. With the advent of emobility, other models still include aspects of the transport sector. Indeed, coupling these various sectors using power-to-X technologies is an emerging area of research.
=== AnyMOD.jl ===

29
data/en.wikipedia.org/wiki/Open_energy_system_models-8.md Normal file
View File

@ -0,0 +1,29 @@
---
title: "Open energy system models"
chunk: 9/16
source: "https://en.wikipedia.org/wiki/Open_energy_system_models"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:49:30.157219+00:00"
instance: "kb-cron"
---
AnyMOD.jl is a framework for planning macro‑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 sector coupling technologies. It can be used to plan long‑term pathways under perfect foresight.
AnyMOD.jl is implemented in 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 HiGHS or commercial solvers like CPLEX. To increase accessibility and enable version-controlled development, specific models are fully defined using CSV files.
Compared to similar tools, AnyMOD.jl puts an emphasis on innovative methods to achieve high detail and capture 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. Methods from stochastic programming are now being implemented to better address the uncertainties associated with renewable generation.
As of 2022, most studies deploying the tool have focused on the German energy system in a European context, for instance investigating the trade‑offs between centralized and decentralized designs, the role of grid planning, and the potential of sufficiency measures. In addition, AnyMOD.jl has been used to support policy reports from the German Institute for Economic Research (DIW) on the European Green Deal and the coordination of the German Energiewende.
=== Backbone ===
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 2015–2018 in an Academy of Finland‑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 RUB.
The framework is agnostic about what is modeled, but still has capabilities to represent a large range of energy system characteristics — 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. — 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.
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  and district heating and cooling model for the Finnish capital region.
=== Balmorel ===
Balmorel is a market-based energy system model from Denmark. Development was originally financed by the Danish Energy Research Program in 2001. The codebase was made public in March 2001. The Balmorel project maintains an extensive website, from where the codebase and datasets can be download as a zip file. Users are encouraged to register. Documentation is available from the same site. Balmorel is written in GAMS.
The original aim of the Balmorel project was to construct a partial equilibrium model of the electricity and CHP sectors in the Baltic Sea region, for the purposes of policy analysis. These ambitions and limitations have long since been superseded and Balmorel is no longer tied to its original geography and policy questions. 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 program (no integer variables).
As of 2016, Balmorel has been the subject of some 22 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. A 2010 study uses Balmorel to examine the integration of plug-in hybrid vehicles (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. 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.
=== Calliope ===

34
data/en.wikipedia.org/wiki/Open_energy_system_models-9.md Normal file
View File

@ -0,0 +1,34 @@
---
title: "Open energy system models"
chunk: 10/16
source: "https://en.wikipedia.org/wiki/Open_energy_system_models"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:49:30.157219+00:00"
instance: "kb-cron"
---
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 Department of Environmental Systems Science, ETH Zurich, Zürich, Switzerland. The project maintains a website, hosts the codebase at GitHub, operates an issues tracker, and runs two email lists. Calliope is written in Python and uses the Pyomo library. It can link to the open source GLPK solver and the commercial CPLEX solver. PDF documentation is available.
And a two‑page software review is available.
A Calliope model consists of a collection of structured text files, in YAML and CSV formats, that define the technologies, locations, and resource potentials. Calliope takes these files, constructs a pure linear optimization (no integer variables) problem, solves it, and reports the results in the form of pandas data structures for analysis. The framework contains five abstract base technologies – supply, demand, conversion, storage, transmission – from which new concrete technologies can be derived. The design of Calliope enforces the clear separation of framework (code) and model (data).
A 2015 study uses Calliope to compare the future roles of nuclear power and 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. A second 2015 study compares a large number of cost-optimal future power systems for Great Britain. Three generation technologies are tested: renewables, nuclear power, and fossil fuels with and without carbon capture and storage (CCS). The scenarios are assessed on financial cost, emissions reductions, and energy security. Up to 60% of variable renewable capacity is possible with little increase in cost, while higher shares require large-scale storage, imports, and/or dispatchable renewables such as tidal range.
Calliope co‑developer Stefan Pfenninger discusses the role that energy system models can play in supporting real‑world decisions at a seminar held in mid‑2021. One study cited investigates the consequences of pursuing energy self‑sufficiency by duly adding increasingly restrictive internal constraints. Another at near optimal solutions for Italy. A 2023 video describes recent developments, many of which are designed to benefit users.
=== DESSTinEE ===
DESSTinEE stands for Demand for Energy Services, Supply and Transmission in EuropE. DESSTinEE is a model of the European energy system in 2050 with a focus on the electricity system. DESSTinEE is being developed primarily at the Imperial College Business School, Imperial College London (ICL), London, United Kingdom. The software can be downloaded from the project website. DESSTinEE is written in Excel/VBA and comprises a set of standalone spreadsheets. A flier is available.
DESSTinEE is designed to investigate assumptions about the technical requirements for energy transport – particularly electricity – and the scale of the economic challenge to develop the necessary infrastructure. Forty countries are considered in and around Europe and ten forms of primary and secondary energy are supported. The model uses a predictive simulation technique, rather than solving for either partial or 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.
A 2016 study using DESSTinEE (and a second model eLOAD) examines the evolution of electricity load curves in Germany and Britain from the present until 2050. In 2050, peak loads and ramp rates rise 20–60% and system utilization falls 15–20%, in part due to the substantial uptake of heat pumps and electric vehicles. These are significant changes.
=== Energy Transition Model ===
The Energy Transition Model (ETM) is an interactive web-based model using a holistic description of a country's energy system. It is being developed by Quintel Intelligence, Amsterdam, the Netherlands. The project maintains a project website, an interactive website, and a GitHub repository. ETM is written in Ruby (on Rails) and displays in a web browser. ETM consists of several software components as described in the documentation.
ETM is fully interactive. After selecting a region (France, Germany, the Netherlands, Poland, Spain, United Kingdom, EU-27, or Brazil) and a year (2020, 2030, 2040, or 2050), the user can set 300 sliders (or enter numerical values) to explore the following:
targets: set goals for the scenario and see if they can be achieved, targets comprise: CO2 reductions, renewables shares, total cost, and caps on imports
demands: expand or restrict energy demand in the future
costs: project the future costs of energy carriers and energy technologies, these costs do not include taxes or subsidies
supplies: select which technologies can be used to produce heat or electricity
ETM is based on an energy graph (digraph) where nodes (vertices) can convert from one type of energy to another, possibly with losses. The connections (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).
=== EnergyPATHWAYS ===

36
data/en.wikipedia.org/wiki/Peter_Murray-Rust-0.md Normal file
View File

@ -0,0 +1,36 @@
---
title: "Peter Murray-Rust"
chunk: 1/1
source: "https://en.wikipedia.org/wiki/Peter_Murray-Rust"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:49:26.337246+00:00"
instance: "kb-cron"
---
Peter Murray-Rust is a chemist currently working at the University of Cambridge. As well as his work in chemistry, Murray-Rust is also known for his support of open access and open data.
== Education ==
He was educated at Bootham School, a private school in York, and at Balliol College, Oxford. After obtaining a Doctor of Philosophy with a thesis entitled A structural investigation of some compounds showing charge-transfer properties, he became lecturer in chemistry at the (new) University of Stirling and was first warden of Andrew Stewart Hall of Residence. In 1982, he moved to Glaxo Group Research at Greenford to head Molecular Graphics, Computational Chemistry and later protein structure determination. He was Professor of Pharmacy in the University of Nottingham from 1996 to 2000, setting up the Virtual School of Molecular Sciences. He is now Reader Emeritus in Molecular Informatics at the University of Cambridge and Senior Research Fellow Emeritus at Churchill College, Cambridge.
== Research ==
His research interests have involved the automated analysis of data in scientific publications, creation of virtual communities, e.g. The Virtual School of Natural Sciences in the Globewide Network Academy, and the Semantic Web. With Henry Rzepa, he has extended this to chemistry through the development of markup languages, especially Chemical Markup Language. He campaigns for open data, particularly in science, and is on the advisory board of the Open Knowledge International and a co-author of the Panton Principles for Open scientific data. Together with a few other chemists, he was a founder member of the Blue Obelisk movement in 2005.
In 2002, Peter Murray-Rust and his colleagues proposed an electronic repository for unpublished chemical data called the World Wide Molecular Matrix (WWMM). In January 2011, a symposium around his career and visions was organized, called Visions of a Semantic Molecular Future. In 2011, he and Henry Rzepa were joint recipients of the Herman Skolnik Award of the American Chemical Society. In 2014, he was awarded a Fellowship by the Shuttleworth Foundation to develop the automated mining of science from the literature.
In 2009 Murray-Rust coined the term "Doctor Who" model for the phenomenon exhibited by the Blue Obelisk project and other Open Science projects, where when a project leader does not have the resources to continue to lead a project (e.g. because he or she has moved to another university with other tasks), someone else will stand up to become the new leader and continue the project. This is a reference to the long-running British science fiction television series Doctor Who, in which the main character periodically regenerates into a different form, which is played by a different actor.
As of 2014, Murray-Rust was granted a Fellowship by Shuttleworth Foundation in relation to the ContentMine project which uses machines to liberate 100,000,000 facts from the scientific literature.
== Activism ==
Murray-Rust is also known for his work on making scientific knowledge from literature freely available, and in such taking a stance against publishers that are not fully compliant with the Berlin Declaration on Open Access. In 2014, he actively raised awareness of glitches in the publishing system of Elsevier, where restrictions were imposed by Elsevier on the reuse of papers after the authors had paid Elsevier to make the paper freely available.
== References ==
== External links ==
Cambridge university page
Peter Murray-Rust on X
Doctoral thesis, "A structural investigation of some compounds showing charge-transfer properties"