Open Energy Modelling Initiative

The Open Energy Modelling Initiative (openmod) is a grass roots 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 system 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.[1]

Open Energy Modelling Initiative
FormationSeptember 2014 [a]
TypeInternet-based with periodic workshops
PurposePromote open-source energy models and open energy sector data
Official language
Email list!forum/openmod-initiative


The application of open-source development to energy modelling dates back to around 2010. 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.[2]:5861 Balmorel was also open at that time, having been made public in 2001.[b] As of November 2016, the openmod wiki lists 24 such undertakings.[3]

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".[4]:149 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.[5] 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".[6]:17[7] 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.[8] 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.[9] 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".[10]:8 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."[11]:S27 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 modelers. 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."[12]:4 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".[13]:2 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.[14] 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.[14]:214

A one-page opinion piece in Nature News 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 scrutiny, currently only Energy Economics makes this practice mandatory within the energy domain.[15]

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 licenses (such as CC BY 4.0) or be in the public domain. But most published energy datasets carry proprietary licenses, 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 [16] and a 2016 amendment to the German Energy Industry Act [17] to establish a nation energy information platform, slated to launch on 1 July 2017. Energy databases are protected under general database law, irrespective of the copyright status of the information they hold.[18]

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.[19] The document provides a comprehensive account of the data issues faced by researchers engaged in open energy system modeling and energy market analysis and quoted extensively from a German legal opinion.[20]

Public policy support

In May 2016 the European Union announced that "all scientific articles in Europe must be freely accessible as of 2020".[21] 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.[22] 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).[23] 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.[23][24] Open-source campaigners are using the initiative to advocate that European governments adopt similar practices.[25] 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 modeling is not traversed per se.[26]


The Open Energy Modelling Initiative participants take turns to host regular academic workshops.

  Date Host City Country
1 18–19 September 2014 DIW Berlin Berlin Germany
2 13–14 April 2015 MCC Berlin[27] Berlin Germany
3 10–11 September 2015 Imperial College London (ICL) [28] London United Kingdom
4 28–29 April 2016 KTH Royal Institute of Technology[29] Stockholm Sweden
5 27–28 October 2016 Department of Energy, Politecnico di Milano Milan Italy
6 20–21 April 2017 Frankfurt Institute for Advanced Studies (FIAS) [30] Frankfurt Germany
7 12–13 October 2017 Technical University of Munich (TUM) Munich Germany
8 6–8 June 2018 Climate Policy Group, ETH Zurich Zürich Switzerland
9 22–24 May 2019 (forthcoming) Department of Engineering, Aarhus University Aarhus Denmark

See also


  1. ^ The first workshop was held on 18–19 September 2014 and the first posting to the openmod mailing list occurred on 4 October 2014.
  2. ^ NEMO was also under development in 2011 but it is unclear whether its codebase was public at that point.

External links

Related to openmod

Open energy data

  • Enipedia – a semantic wiki-site and database covering energy systems data worldwide
  • 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[31][32]
  • OpenEI – a US-based open energy data portal

Similar initiatives


  • REEEM – a scientific project modeling sustainable energy futures for Europe


  1. ^ "openmod — Open Energy Modelling Initiative". Open Energy Modelling Initiative. Retrieved 2016-10-10.
  2. ^ Howells, Mark; Rogner, Holger; Strachan, Neil; Heaps, Charles; Huntington, Hillard; Kypreos, Socrates; Hughes, Alison; Silveira, Semida; DeCarolis, Joe; Bazilian, Morgan; Roehrl, Alexander (2011). "OSeMOSYS: the open source energy modeling system : an introduction to its ethos, structure and development". Energy Policy. 39 (10): 5850–5870. doi:10.1016/j.enpol.2011.06.033. The misspelling of Morgan Bazillian has been corrected in the citation. ResearchGate version.
  3. ^ "Open Models". Open Energy Modelling Initiative. Retrieved 2016-11-03.
  4. ^ Bazilian, Morgan; Rice, Andrew; Rotich, Juliana; Howells, Mark; DeCarolis, Joseph; Macmillan, Stuart; Brooks, Cameron; Bauer, Florian; Liebreich, Michael (2012). "Open source software and crowdsourcing for energy analysis" (PDF). Energy Policy. 49: 149–153. doi:10.1016/j.enpol.2012.06.032. Retrieved 2016-06-17.
  5. ^ acatech; Lepoldina; Akademienunion, eds. (2016). Consulting with energy scenarios: requirements for scientific policy advice (PDF). Berlin, Germany: acatech — National Academy of Science and Engineering. ISBN 978-3-8047-3550-7. Retrieved 2016-12-19.
  6. ^ Mundaca, Luis; Neij, Lena; Worrell, Ernst; McNeil, Michael A (1 August 2010). "Evaluating energy efficiency policies with energy-economy models — Report number LBNL-3862E". Annual Review of Environment and Resources. 35: 305–344. doi:10.1146/annurev-environ-052810-164840. OSTI 1001644. Retrieved 2016-12-19.
  7. ^ Mundaca, Luis; Neij, Lena; Worrell, Ernst; McNeil, Michael A (22 October 2010). "Evaluating energy efficiency policies with energy-economy models". Annual Review of Environment and Resources. 35 (1): 305–344. doi:10.1146/annurev-environ-052810-164840. ISSN 1543-5938.
  8. ^ DeCarolis, Joseph F; Hunter, Kevin; Sreepathi, Sarat (2012). "The case for repeatable analysis with energy economy optimization models" (PDF). Energy Economics. 34 (6): 1845–1853. doi:10.1016/j.eneco.2012.07.004. ISSN 0140-9883. Retrieved 2016-07-08.
  9. ^ Wiese, Frauke; Bökenkamp, Gesine; Wingenbach, Clemens; Hohmeyer, Olav (2014). "An open source energy system simulation model as an instrument for public participation in the development of strategies for a sustainable future". Wiley Interdisciplinary Reviews: Energy and Environment. 3 (5): 490–504. doi:10.1002/wene.109. ISSN 2041-840X.
  10. ^ Strachan, Neil; Fais, Birgit; Daly, Hannah (18 November 2014). Redefining the energy modelling-policy interface: developing a fully open source UK TIMES model — Presentation (PDF). Energy Technology Systems Analysis Programme (ETSAP) Workshop, Technical University of Denmark (DTU). Copenhagen, Denmark. Retrieved 2016-11-08.
  11. ^ Pye, Steve; Bataille, Chris (2016). "Improving deep decarbonization modelling capacity for developed and developing country contexts". Climate Policy. 16 (S1): S27–S46. doi:10.1080/14693062.2016.1173004.
  12. ^ Cao, Karl-Kiên; Cebulla, Felix; Gómez Vilchez, Jonatan J; Mousavi, Babak; Prehofer, Sigrid (28 September 2016). "Raising awareness in model-based energy scenario studies — a transparency checklist". Energy, Sustainability and Society. 6 (1): 28–47. doi:10.1186/s13705-016-0090-z. ISSN 2192-0567. open access
  13. ^ Strachan, Neil; Fais, Birgit; Daly, Hannah (29 February 2016). "Reinventing the energy modelling–policy interface". Nature Energy. 1 (3): 16012. Bibcode:2016NatEn...116012S. doi:10.1038/nenergy.2016.12. ISSN 2058-7546.
  14. ^ a b Pfenninger, Stefan; DeCarolis, Joseph; Hirth, Lion; Quoilin, Sylvain; Staffell, Iain (February 2017). "The importance of open data and software: is energy research lagging behind?" (PDF). Energy Policy. 101: 211–215. doi:10.1016/j.enpol.2016.11.046. ISSN 0301-4215. Retrieved 2017-02-03. open access
  15. ^ Pfenninger, Stefan (23 February 2017). "Energy scientists must show their workings" (PDF). Nature News. 542 (7642): 393. Bibcode:2017Natur.542..393P. doi:10.1038/542393a. PMID 28230147. Retrieved 2017-02-26.
  16. ^ "Commission Regulation (EU) No 543/2013 of 14 June 2013 on submission and publication of data in electricity markets and amending Annex I to Regulation (EC) No 714/2009 of the European Parliament and of the Council". Official Journal of the European Union (L 163): 1–12. 15 June 2013. Retrieved 2016-12-01.
  17. ^ § 111d Energiewirtschaftsgesetz (EnWG) [ Energy Industry Act] of 13 October 2016. p. 115–116. Einrichtung einer nationalen Informationsplattform [Establishment of a national information platform].
  18. ^ Boecker, Lina (21 November 2016). Energy databases: protection and licensing (PDF). Berlin, Germany: JBB Rechtsanwaelte.
  19. ^ Morrison, Robbie; Brown, Tom; De Felice, Matteo (10 December 2017). Submission on the re-use of public sector information: with an emphasis on energy system datasets — Release 09 (PDF). Berlin, Germany. Retrieved 2017-12-13. open access
  20. ^ Jaeger, Till (24 July 2017). Legal aspects of European electricity data — Legal opinion (PDF). Berlin, Germany: JBB Rechtsanwälte. Retrieved 2017-10-13.
  21. ^ Hendrikx, Michiel (27 May 2016). "All European scientific articles to be freely accessible by 2020" (PDF) (Press release). The Netherlands: Ministry of Education, Culture and Science. Retrieved 2016-08-07.
  22. ^ Albers, Erik (2 June 2016). "There is no open science without the use of open standards and free software". Retrieved 2016-08-07.
  23. ^ a b Scott, Tony; Rung, Anne E (8 August 2016). Federal Source Code Policy: Achieving Efficiency, Transparency, and Innovation through Reusable and Open Source Software — Memorandum for the Heads of Departments and Agencies — M-16-21 (PDF). Washington DC, USA: Office of Budget and Management, Executive Office of the President. Archived from the original (PDF) on 20 September 2016. Retrieved 14 September 2016. Also available as HTML at:
  24. ^ "The People's Code: Unlock the tremendous potential of the Federal Government's software". USA. Retrieved 2016-11-24.
  25. ^ New, William (22 August 2016). "New US government source code policy could provide model for Europe". Intellectual Property Watch. Geneva, Switzerland. Retrieved 2016-09-14.
  26. ^ Gkotsopoulou, Olga; Albers, Erik; Di Cosmo, Roberto; Malaja, Polina; Sanjurjo, Fernando (5 January 2017). Position paper for the endorsement of free software and open standards in Horizon 2020 and all publicly-funded research (PDF). Berlin, Germany: Free Software Foundation Europe (FSFE). Retrieved 2017-02-09.
  27. ^ "OSeMOSYS Newsletter". Retrieved 2016-04-25.
  28. ^ "Open Energy Modelling Workshop". Retrieved 2015-09-25.
  29. ^ "Open Energy Modelling Workshop — KTH, Stockholm 2016". Retrieved 2016-04-28.
  30. ^ "Open Energy Modelling Workshop — Frankfurt 2017". Retrieved 2016-12-01.
  31. ^ "Energiedaten für alle – Projekt "Open Power System Data" an der EUF gestartet" [Energy data for all — project "Open Power System Data" started at the EUF] (in German). Retrieved 2015-09-25.
  32. ^ "Offene Plattform macht Energiedaten zugänglich" [Open platform makes energy data available] (in German). 2015-09-14. Retrieved 2015-09-25.
Deep Decarbonization Pathways Project

The Deep Decarbonization Pathways Project (DDPP) is a global consortium formed in October 2013 which researches methods to limit the rise of global temperature due to global warming to 2 °C or less. The focus of the DDPP is on sustainable energy systems, other sectors of the economy, such as agriculture and land-use, are not directly considered.

Electricity market

In economic terms, electricity (both power and energy) is a commodity capable of being bought, sold, and traded. An electricity market is a system enabling purchases, through bids to buy; sales, through offers to sell; and short-term trades, generally in the form of financial or obligation swaps. Bids and offers use supply and demand principles to set the price. Long-term trades are contracts similar to power purchase agreements and generally considered private bi-lateral transactions between counterparties.

Wholesale transactions (bids and offers) in electricity are typically cleared and settled by the market operator or a special-purpose independent entity charged exclusively with that function. Market operators do not clear trades but often require knowledge of the trade in order to maintain generation and load balance.

The commodities within an electric market generally consist of two types: power and energy. Power is the metered net electrical transfer rate at any given moment and is measured in megawatts (MW). Energy is electricity that flows through a metered point for a given period and is measured in megawatt-hours (MWh).

Markets for energy-related commodities trade net generation output for a number of intervals usually in increments of 5, 15 and 60 minutes. Markets for power-related commodities required and managed by (and paid for by) market operators to ensure reliability, are considered ancillary services and include such names as spinning reserve, non-spinning reserve, operating reserves, responsive reserve, regulation up, regulation down, and installed capacity.

In addition, for most major operators, there are markets for transmission congestion and electricity derivatives such as electricity futures and options, which are actively traded. These markets developed as a result of the restructuring of electric power systems around the world. This process has often gone on in parallel with the restructuring of natural gas markets.

Energy Modeling Forum

The Energy Modeling Forum (EMF) is a structured forum for discussing important issues in energy and the environment. The EMF was established in 1976 at Stanford University. The EMF works through a series of ad hoc working groups, each focussing on a particular corporate or policy decision. The EMF provides a non-partisan platform that ensures objective consideration of opposing views. Participation is by invitation.

Since the late-1990s, the EMF has made contributions to the economics of climate change, as witnessed in the reports of the Intergovernmental Panel on Climate Change (IPCC) and on integrated assessment modeling more generally.

John Weyant is the current director of the EMF.

Energy modeling

Energy modeling or energy system modeling is the process of building computer models of energy systems in order to analyze them. Such models often employ scenario analysis to investigate different assumptions about the technical and economic conditions at play. Outputs may include the system feasibility, greenhouse gas emissions, cumulative financial costs, natural resource use, and energy efficiency of the system under investigation. A wide range of techniques are employed, ranging from broadly economic to broadly engineering. Mathematical optimization is often used to determine the least-cost in some sense. Models can be international, regional, national, municipal, or stand-alone in scope. Governments maintain national energy models for energy policy development.

Energy models are usually intended to contribute variously to system operations, engineering design, or energy policy development. This page concentrates on policy models. Individual building energy simulations are explicitly excluded, although they too are sometimes called energy models. IPCC-style integrated models, which also contain a representation of the world energy system and are used to examine global transformation pathways through to 2050 or 2100 are not considered here in detail.

Energy modeling has increased in importance as the need for climate change mitigation has grown in importance. The energy supply sector is the largest contributor to global greenhouse gas emissions. The IPCC reports that climate change mitigation will require a fundamental transformation of the energy supply system, including the substitution of unabated (not captured by CCS) fossil fuel conversion technologies by low-GHG alternatives.

Grantham Research Institute on Climate Change and the Environment

The Grantham Research Institute on Climate Change and the Environment is a research institute at the London School of Economics and Political Science founded in May 2008. The centre is a partner of the Grantham Institute for Climate Change at Imperial College and acts as an umbrella body for LSE's overall research contributions to the field of climate change and its impact on the environment. Furthermore, the institute oversees the activities of the Centre for Climate Change Economics and Policy (CCCEP), a partnership between LSE and the University of Leeds.

Both Grantham research centres are sponsored through the Grantham Foundation for the Protection of the Environment, established by Hannelore and Jeremy Grantham in 1997. The combined investments totalling approximately £24 million is recognised as one of the largest private contributions to climate change research. CCCEP is funded independently by the ESRC.

The institute is currently chaired by Lord Nicholas Stern of Brentford, former Chief Economist of the World Bank and author of the widely known Stern Review.The purpose of the Institute is to increase knowledge and understanding on climate change and the environment; promote better informed decision-making; and educate and train new generations of researchers through its undergraduate and postgraduate programmes.

The institute's main research activities are divided into five different areas:

1. Global response strategies2. Green growth3. Practical aspects of climate policy4. Adaptation and development5. Resource securityThe research of the institute is characterised by its interdisciplinary nature and brings together international expertise on economics, finance, geography, the environment, international development and political economy, as the centre's academic staff comprise a broad range of disciplines, including physicists, climatologists, economists, statisticians, political scientists and various other social scientists.

In September 2015, the institute hosted the Open Energy Modelling Initiative workshop.

Open-source model

The open-source model is a decentralized software development model that encourages open collaboration.

A main principle of open-source software development is peer production, with products such as source code, blueprints, and documentation freely available to the public. The open-source movement in software began as a response to the limitations of proprietary code. The model is used for projects such as in open-source appropriate technology, and open-source drug discovery.Open source promotes universal access via an open-source or free license to a product's design or blueprint, and universal redistribution of that design or blueprint. Before the phrase open source became widely adopted, developers and producers used a variety of other terms. Open source gained hold with the rise of the Internet. The open-source software movement arose to clarify copyright, licensing, domain, and consumer issues.

Generally, open source refers to a computer program in which the source code is available to the general public for use or modification from its original design. Open-source code is meant to be a collaborative effort, where programmers improve upon the source code and share the changes within the community. Code is released under the terms of a software license. Depending on the license terms, others may then download, modify, and publish their version (fork) back to the community.

Many large formal institutions have sprung up to support the development of the open-source movement, including the Apache Software Foundation, which supports community projects such as the open-source framework Apache Hadoop and the open-source HTTP server Apache HTTP.

Open energy system databases

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.

Open energy system models

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

Energy system models are used to explore future energy systems and are often applied to questions involving energy and climate policy. The models themselves vary widely in terms of their type, design, programming, application, scope, level of detail, sophistication, and shortcomings. 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. For many models, some form of mathematical optimization is used to inform the solution process.

Several drivers favor the development of open models and open data. There is an increasing interest in making public policy energy models more transparent to improve their acceptance by policymakers and the public. 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.

Open science

Open science is the movement to make scientific research (including publications, data, physical samples, and software) and its dissemination accessible to all levels of an inquiring society, amateur or professional. Open science is transparent and accessible knowledge that is shared and developed through collaborative networks. It encompasses practices such as publishing open research, campaigning for open access, encouraging scientists to practice open notebook science, and generally making it easier to publish and communicate scientific knowledge.

Open Science can be seen as a continuation of, rather than a revolution in, practices begun in the 17th century with the advent of the academic journal, when the societal demand for access to scientific knowledge reached a point at which it became necessary for groups of scientists to share resources with each other so that they could collectively do their work. In modern times there is debate about the extent to which scientific information should be shared. The conflict that led to the Open Science movement is between the desire of scientists to have access to shared resources versus the desire of individual entities to profit when other entities partake of their resources. Additionally, the status of open access and resources that are available for its promotion are likely to differ from one field of academic inquiry to another.

Power-flow study

In power engineering, the power-flow study, or load-flow study, is a numerical analysis of the flow of electric power in an interconnected system. A power-flow study usually uses simplified notations such as a one-line diagram and per-unit system, and focuses on various aspects of AC power parameters, such as voltages, voltage angles, real power and reactive power. It analyzes the power systems in normal steady-state operation.

Power-flow or load-flow studies are important for planning future expansion of power systems as well as in determining the best operation of existing systems. The principal information obtained from the power-flow study is the magnitude and phase angle of the voltage at each bus, and the real and reactive power flowing in each line.

Commercial power systems are usually too complex to allow for hand solution of the power flow. Special purpose network analyzers were built between 1929 and the early 1960s to provide laboratory-scale physical models of power systems. Large-scale digital computers replaced the analog methods with numerical solutions.

In addition to a power-flow study, computer programs perform related calculations such as short-circuit fault analysis, stability studies (transient and steady-state), unit commitment and economic dispatch. In particular, some programs use linear programming to find the optimal power flow, the conditions which give the lowest cost per kilowatt hour delivered.

A load flow study is especially valuable for a system with multiple load centers, such as a refinery complex. The power flow study is an analysis of the system’s capability to adequately supply the connected load. The total system losses, as well as individual line losses, also are tabulated. Transformer tap positions are selected to ensure the correct voltage at critical locations such as motor control centers. Performing a load flow study on an existing system provides insight and recommendations as to the system operation and optimization of control settings to obtain maximum capacity while minimizing the operating costs. The results of such an analysis are in terms of active power, reactive power, magnitude and phase angle. Furthermore, power-flow computations are crucial for optimal operations of groups of generating units.

The Open Energy Modelling Initiative promotes open source load-flow models and other types of energy system models.

Open data
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