Open energy system models

• Comment: Note to reviewer (current) / RobbieIanMorrison (talk) 12:59, 30 November 2016 (UTC)
  * Regarding structure (addressing the earlier "large and unwieldy" review comment), the open energy models material is now organized into three areas: electricity sector models, energy system models, and other types. There are now section-level (table-marked-up) infoboxes to aid orientation. In addition, I plan to revise and fork the open energy system data material into a new article, assuming this page is approved.
  * Regarding suitability (addressing the earlier "indiscriminate collection of information" review comment), I do not agree that the material presented is inappropriate for Wikipedia. I believe that the articles covering and reviewing software on Wikipedia have developed their own traditions and should be assessed as such. For just one example, please see list of optimization software. Notwithstanding, pages about software should remain encyclopedic.
  * The merging of material from this page into the energy modeling article (also suggested in the review) does not make much sense because that page needs space to grow in its own right (for instance, I have started work on adding the PRIMES and WEM models and on expanding the coverage of model typologies).
  * I have prototyped a proper infobox at User:RobbieIanMorrison/infobox energy model (it renders something close to the table markup currently used) but do not know if it is worth developing and publishing this template for just one page. Please advise.
  * The embedded URLs (recording project websites and so forth) have largely been removed or shifted to infoboxes and the referencing has been tightened up.

• Comment: I don't mean to discourage you from editing and article creation in the future, rather, I'm hoping this helps clear out some of the issues with this article and why it hasn't (and may not ever) pass.
  Wikipedia is not an indiscriminate collection of information nor a scientific journal - the content below appears large and unwieldy given the overall topic, and while heavily referenced not all of them appear to be directly correlated to Open Energy System Models. I would consider merging the relevant info into Energy modeling, as suggested below, as I just simply (no offense intended) don't see this passing without serious work being done - and even then it's not a given. Garchy (talk) 14:42, 4 November 2016 (UTC)

• Comment: Note to reviewer / RobbieIanMorrison (talk) 10:57, 3 November 2016 (UTC)
  * In response to the non-NPOV comment (below), I removed two potentially aspirational sentences and improved the referencing in the lead.

• Comment: Possible npov issue Ⓩⓟⓟⓘⓧ (talk) 04:45, 3 November 2016 (UTC)

• Comment: Note to reviewer / RobbieIanMorrison (talk) 15:27, 27 September 2016 (UTC)
  * The lead section and the first section General considerations have been rewritten to be much more compact and to make better use of sources. The remaining technical sections are largely factual and are not considered to be essay-like, nonetheless they have been reworked a little to improve their style.
  * I will source some diagrams (showing model structure and timeseries output) to illustrate this article once it has been accepted.

• Comment: Note to reviewer: Offending material in section #EMMA removed and will be re-written shortly to comply. RobbieIanMorrison (talk) 17:30, 30 July 2016 (UTC). Postscript: The offending material was supposed to be issued under a CC BY-SA 3.0 license but the document itself omitted to include this notice. I contacted the author and this oversight will be corrected for the next release. RobbieIanMorrison (talk) 10:28, 2 December 2016 (UTC)

• Comment: possible cv Daniel kenneth (talk) 17:11, 30 July 2016 (UTC)

• Comment: Note to reviewer / RobbieIanMorrison (talk) 10:56, 13 July 2016 (UTC)
  * There are now 24 energy models listed and described, previously there were 5 – a 480% increase in number.
  * I will seek some colorful diagrams from the various projects once the article is live.

• Comment: Note to reviewer / RobbieIanMorrison (talk) 18:26, 2 June 2016 (UTC)
  * In response to the first comment, there is now a page on Energy modeling.
  * In response to the second comment, the note mentioned is now here.

• Comment: I've left a note at WP:ENERGY (Wikipedia_talk:WikiProject_Energy#Wikipedia_talk:Articles_for_creation.2FOpen_source_energy_system_models) ~KvnG 18:49, 28 January 2015 (UTC)

• Comment: I'm not sure that open source energy system models are a notable sub-group of energy system models. In fact, there is currently (as of 02:45, 28 March 2013 (UTC)) no article called energy system models in Wikipedia, although the topic may be covered under a different name or as a section in another article.
  This draft also has writing-style issues. Simply put, it's written more like a magazine article or an "introduction to the topic" lesson one might see in a college or continuing-education course than an encyclopedia article.
  I am going to decline this with the hopes that the author will see if the topic of energy system models is already covered in Wikipedia and if not, determine if the topic meets Wikipedia's notability requirements. If the topic is notable and missing, I hope he writes an article about it. davidwr/(talk)/(contribs)/(e-mail) 02:45, 28 March 2013 (UTC)

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For broader coverage of this topic, see Energy modeling.

For the simulation of energy use in buildings, see Building energy simulation.

Open energy system models are energy system models that are open source. Similarly open energy system data employs open data methods to produce 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 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 most 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 the public and by policymakers. 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 free software license.

General considerations

Open energy system modeling projects

See also: Energy modeling and Open-source software development

An open energy system modeling project typically comprises a codebase, datasets, and software documentation and perhaps scientific publications. The project repository may be hosted on an institutional server or on a public code-hosting site, such as GitHub. Some projects release only their codebase, while others ship some or all of their datasets as well. Projects often furnish social networking services to assist collaboration, including email lists, chat rooms, and web forums. The provision of wikis facilitates collaborative authoring and the use of documentation generators can streamline end-user and software documentation.

Open energy system modeling developed in the 2010s. Just two active projects were cited in a 2012 paper on the topic: OSeMOSYS and TEMOA. As of December 2016, this article lists 25 such undertakings.

Transparency, comprehensibility, and verifiability

See also: Open Energy Modelling Initiative § Context

Open energy system modeling represents one attempt to improve the transparency, comprehensibility, and verifiability of energy system models, particularly those used in support of public policy development.

To honor the process of peer review, researchers argue, in a 2012 paper, that it is essential to place both the source code and the 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."

Open energy system 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). But most published datasets carry proprietary licenses, preventing 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 prevent downstream usage. Recent transparency measures include the 2013 European energy market transparency regulation 543/2013.

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. Wikipedia itself has a growing set of information related to national energy systems, including descriptions of individual power stations.

The following table summarizes projects that specifically collect and collate open energy system data. Some are general repositories while others (for instance, open_eGo) are designed to interact in real-time with open energy system models.

Open energy system database projects

Project	Host	License	Access	Data formats	Scope/type
Enipedia	Delft University of Technology	ODbL	semantic wiki, LOD	JSON	global materials and energy
Open Power System Data	University of Flensburg DIW Berlin Technical University of Berlin Neon Neue Energieökonomik	dataset-specific	download	CSV, JSON, XLSX, SQLite	western European power system
open_eGo	Reiner Lemoine Institute	dataset-specific	website, API	CSV	model-oriented
OpenEI	US Department of Energy	CC0, open licenses	semantic wiki, LOD	CSV	US focus
reegle	REEEP REN21	—	website, LOD	—	clean energy

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.

Enipedia

Project	Enipedia
Host	Delft University of Technology
Data license	ODbL
Wiki	enipedia.tudelft.nl

The semantic wiki-site and database Enipedia lists energy systems data worldwide. Enipedia is maintained by the Energy and Industry Group, 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.

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.

Open Power System Data

Project	Open Power System Data
Host	University of Flensburg DIW Berlin TU Berlin Neon Neue Energieökonomik
Data license	dataset-specific 1
Website	open-power-system-data.org
Repository	github.com/Open-Power-System-Data
1. The project website states that data might be subject to proprietary copyright, in which case the primary data owner should be consulted.

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 project is jointly managed by the University of Flensburg, DIW Berlin, the Technical University of Berlin, and the energy economics consultancy Neon Neue Energieökonomik, all from Germany. The project is funded by the Federal Ministry for Economic Affairs and Energy (BMWi) for €490000 and runs from August 2015 to July 2017. Developers collate and harmonize data from a range of governmental, 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. The website was launched on 28 October 2016. As of September 2016, the project offers, for Germany and other European countries:

• details 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

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 often also provided as XLSX (Excel) and SQLite files. The datasets can be accessed in realtime using stable URLs. The scripts deployed for preprocessing are also available and carry an MIT license. The licensing conditions for the data itself depends on the source and varies in terms of openness. The datasets and scripts are stored at GitHub. Previous versions 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), to encourage them to release their data under open licenses (for instance, Creative Commons and ODbL).

open_eGo

Project	open_eGo
Host	Reiner Lemoine Institute
Data license	dataset-specific
Website	reiner-lemoine-institut.de/en/open_ego-open-electricity-grid-optimization/

One component of the open_eGo project 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 source licenses are specific to each dataset. Initial development is being lead by the Reiner Lemoine Institute, Berlin, Germany and the repository is scheduled to go live at the end of 2016.

OpenEI

Main article: Open Energy Information

Project	OpenEI
Host	National Renewable Energy Laboratory
Data license	CC0 open licenses
Website	en.openei.org

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 license, 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 US. And to publicize municipal utility rates, again within the US.

reegle

Main article: reegle

Project	reegle
Host	REEEP REN21
Data license	—
Website	www.reegle.info

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.

Open electricity sector models

Open electricity sector models are confined to just the electricity sector. The 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 AC power flow within high-voltage transmission networks. Others models depict electricity spot markets. While other models embed autonomous agents to capture, for instance, bidding decisions using techniques from bounded rationality. The ability to handle variable renewable energy and grid storage are becoming important considerations.

Open electricity sector models

Project	Host	License	Access	Coding	Documentation	Scope/type
DESSTinEE	Imperial College London	CC BY-SA 3.0	download	Excel/VBA	website	simulation
DIETER	DIW Berlin	MIT	download	GAMS	publication	dispatch and investment
EMLab-Generation	Delft University of Technology	Apache 2.0	GitHub	Java	manual, website	agent-based
EMMA	Neon Neue Energieökonomik	CC BY-SA 3.0	download	GAMS	website	electricity market
EnergyNumbers–Balancing	University College London	GPLv3	on application	Fortran, PHP	—	web-based
GENESYS	RWTH Aachen University	LGPLv2.1	on application	C++	website	European electricity system
NEMO	University of New South Wales	GPLv3	git repository	Python	website, list	Australian NEM
PowerMatcher	Flexiblepower Alliance Network	Apache 2.0	GitHub	Java	website	smart grid
PyPSA	Goethe University Frankfurt	GPLv3	GitHub	Python	website	electric power systems
renpass	University of Flensburg	GPLv3	invitation	R, MySQL	manual	renewables pathways
SciGRID	University of Oldenburg	Apache 2.0	git repository	Python	website, newsletter	European transmission grid
SIREN	Sustainable Energy Now	AGPLv3	GitHub	Python	website	renewable generation
SWITCH	University of Hawai'i	Apache 2.0	GitHub	Python	website	optimal planning
URBS	Technical University of Munich	GPLv3	GitHub	Python	website	distributed energy systems

DESSTinEE

Project	DESSTinEE
Host	Imperial College London
Scope/type	simulation
Code license	CC BY-SA 3.0
Website	sites.google.com/site/2050desstinee/

DESSTinEE stands for Demand for Energy Services, Supply and Transmission in EuropE. DESSTinEE is a model of the European energy system in 2050 with a focus on the electricity system. DESSTinEE is being developed primarily at the Imperial College Business School, Imperial College London (ICL), London, United Kingdom. The software can be downloaded from the project website. DESSTinEE is written in 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.

DIETER

Project	DIETER
Host	DIW Berlin
Scope/type	dispatch and investment
Code license	MIT
Data license	MIT
Website	www.diw.de/dieter

DIETER stands for Dispatch and Investment Evaluation Tool with Endogenous Renewables. DIETER is a dispatch and investment model 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 model is fully described in a DIW working paper. DIETER is written in GAMS and was developed using the CPLEX commercial solver.

DIETER is framed as a linear cost minimization problem. The decision variables include: the investment in generation and the dispatch of generation, storage, and DSM capacities as well as vehicle-to-grid interactions (as an extension) in the German wholesale and balancing electricity markets.

A 2015 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 on biomass availability.

EMLab-Generation

Project	EMLab-Generation
Host	Delft University of Technology
Scope/type	agent-based
Code license	Apache 2.0
Website	emlab.tudelft.nl/generation.html
Repository	github.com/EMLab/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

Project	EMMA
Host	Neon Neue Energieökonomik
Scope/type	electricity market
Code license	CC BY-SA 3.0
Data license	CC BY-SA 3.0
Website	neon-energie.de/emma/

EMMA is the European Electricity Market Model. It is a techno-economic model covering the integrated Northwestern European power system. EMMA is being developed by the energy economics consultancy Neon Neue Energieökonomik, Berlin, Germany. The source code and 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 terms of economics, 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 (a.k.a. equilibria) and estimates the corresponding capacity mix, hourly prices, generation, 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 times more than at present.

An external 2015 study reviews the EMMA model and comments on the high assumed specific costs for renewable investments.

EnergyNumbers–Balancing

Project	EnergyNumbers–Balancing
Host	University College London
Scope/type	web-based
Code license	GPLv3
Website	energynumbers.info/balancing/

EnergyNumbers–Balancing is an interactive electricity system model. It is being developed by the UCL Energy Institute, University College London, London, United Kingdom. The project maintains an interactive website. Users can request access to the codebase by twitter. EnergyNumbers-Balancing is programmed in Fortran, PHP, JavaScript, HTML, and CSS.

The model uses historic demand data, and historic (half or one) hourly capacity factors for photovoltaics and wind generation, to simulate the extent to which demand could be met by some combination of wind, photovoltaics, and storage. As of 2016, Britain, Germany, and Spain are supported.

GENESYS

Project	GENESYS
Host	RWTH Aachen University
Scope/type	European electricity system
Code license	LGPLv2.1
Data license	TBA
Website	www.genesys.rwth-aachen.de/index.php?id=12&L=3

GENESYS stands for Genetic Optimisation of a European Energy Supply System. The software is being developed jointly by the 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 minimal 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 adjust.

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

Project	NEMO
Host	University of New South Wales
Scope/type	Australian NEM
Code license	GPLv3
Website	nemo.ozlabs.org
Repository	git.ozlabs.org?p=nemo.git
Documentation	nbviewer.jupyter.org/urls/nemo.ozlabs.org/guide.ipynb

NEMO, the National Electricity Market Optimiser, is a chronological dispatch model for testing and optimizing different portfolios of conventional and renewable electricity generation technologies. It applies solely to the Australian National Electricity Market (NEM), which, despite its name, is limited to east and south Australia. NEMO has been in development at the Centre for Energy and Environmental Markets (CEEM), University of New South Wales (UNSW), Sydney, Australia since 2011. The project maintains a small website and runs an email list. NEMO is written in Python. 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.

PowerMatcher

Project	PowerMatcher
Host	Flexiblepower Alliance Network
Scope/type	smart grid
Code license	Apache 2.0
Website	flexiblepower.github.io
Repository	github.com/flexiblepower/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 July 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.

PyPSA

Project	PyPSA
Host	Goethe University Frankfurt
Scope/type	electric power systems
Code license	GPLv3
Website	www.pypsa.org
Repository	github.com/FRESNA/PyPSA

PyPSA stands for Python for Power System Analysis. PyPSA is a free software toolbox for simulating and optimizing electric power systems. It features variable wind and solar generation, electricity storage, and mixed alternating and direct current networks. PyPSA is designed to scale well with large networks and long time series. The project is managed by the Frankfurt Institute of Advanced Studies, Goethe University Frankfurt, Germany. The project maintains a website. The source code is hosted on GitHub. PyPSA is written in Python and uses the Pyomo library.

renpass

Project	renpass
Host	University of Flensburg
Scope/type	renewables pathways
Code license	GPLv3
Website	renpass.eu

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.

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 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 plants 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, 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.

As of 2015, renpass is being extended as renpassG!S, based on oemof.

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 electricity 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.

SciGRID

Project	SciGRID
Host	University of Oldenburg
Scope/type	European transmission grid
Code license	Apache 2.0
Website	www.scigrid.de

SciGRID, short for Scientific Grid, is an open source model of the German and European electricity transmission networks. The research project is managed by Next Energy (officially the EWE Research Centre for Energy Technology) located at the University of Oldenburg, Oldenburg, Germany. The project maintains a website and an email newsletter. SciGRID is written in Python and uses a PostgreSQL database. The first release (v0.1) was made on 15 June 2015.

SciGRID aims to rectify the lack of open research data on the structure of electricity transmission networks within Europe. This lack of data frustrates attempts to build, characterise, and compare high resolution energy system models. SciGRID utilizes transmission network data available from the OpenStreetMap project, available under the Open Database License (ODbL), to automatically author transmission connections. Indeed SciGRID will not use data from closed sources. SciGRID can also mathematically decompose a given network into a simpler representation for use in energy models.

A related project is GridKit, released under an MIT license. GridKit is being developed to investigate the possibility of a 'heuristic' analysis to augment the route-based analysis used in SciGRID. Data is available for network models of the European and North-American high-voltage electricity grids.

SIREN

Project	SIREN
Host	Sustainable Energy Now
Scope/type	renewable generation
Code license	AGPLv3
Website	www.sen.asn.au/modelling_overview
Repository	sourceforge.net/projects/sensiren/

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 should be avoided. The modeling assumes a carbon price of AUD $30/tCO₂. Further scenarios examine the goal of 100% renewable generation.

SWITCH

Project	SWITCH
Host	University of Hawai'i
Scope/type	optimal planning
Code license	Apache 2.0
Website	switch-model.org
Repository	github.com/switch-model

SWITCH is a loose acronym for solar, wind, conventional and hydroelectric generation, and transmission. SWITCH is an optimal planning model for power systems with large shares of renewable energy. SWITCH is being developed at the Department of Electrical Engineering, University of Hawai'i, 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 and Gurobi solvers.

SWITCH is a power system model, focused on renewables integration. It can identify which generator and transmission projects to build in order to satisfy electricity demand at the lowest cost over a several year period while also reducing CO₂ emissions. SWITCH uses 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% more than 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.

URBS

Project	URBS
Host	Technical University of Munich
Scope/type	distributed energy systems
Code license	GPLv3
Repository	github.com/tum-ens/urbs

URBS, Latin for city, is a linear programming model for exploring capacity expansion and unit commitment problems and is particularly suited to distributed 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 modeler-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.

Open energy system models

Open energy system models capture some or all of the energy commodities found in an energy system. All models include the electricity sector. Some models add the heat sector, which can be important for countries with significant district heating. Other models add gas networks. With the advent of emobility, other models still include aspects of the transport sector. Indeed, coupling these various sectors using power-to-X technologies is an emerging area of research.

Open energy system models (bottom-up, with support for heat, gas, and such, as well as electricity)

Project	Host	License	Access	Coding	Documentation	Scope/type
Balmorel	Denmark	TBA	registration	GAMS	manual	energy markets
Calliope	ETH Zurich	Apache 2.0	download	Python	manual, website, list	dispatch and investment
Energy Transition Model	Quintel Intelligence	MIT	GitHub	Ruby (on Rails)	website	web-based
EnergyPATHWAYS	Evolved Energy Research	MIT	GitHub	Python	website	mostly simulation
ETEM	ORDECSYS, Luxembourg	Eclipse 1.0	registration	MathProg	manual	municipal
ficus	Technical University of Munich	GPLv3	GitHub	Python	manual	local electricity and heat
oemof	Reiner Lemoine Institute University of Flensburg Flensburg University of Applied Sciences	GPLv3	GitHub	Python	website	electricity and heat
OSeMOSYS	OSeMOSYS community	GPLv3	download	MathProg	website, forum	national planning
TEMOA	North Carolina State University	GPLv2	GitHub	Python	website, forum	system planning

Balmorel

Project	Balmorel
Host	stand-alone from Denmark
Scope/type	energy markets
Code license	TBA
Website	www.balmorel.com

Balmorel is a market-based energy system model from Denmark. Development was originally financed by the Danish Energy Research Program in 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 issues. Balmorel classes as a dispatch and investment model and uses a one hour time resolution. 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 use 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

Project	Calliope
Host	ETH Zurich
Scope/type	dispatch and investment
Code license	Apache 2.0
Website	www.callio.pe
Repository	github.com/calliope-project/calliope
Documentation	docs.callio.pe/en/stable/

Calliope is a 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 two websites, 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 and Gurobi solvers. PDF documentation 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 linear optimization 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.

Energy Transition Model

Project	Energy Transition Model
Host	Quintel Intelligence
Scope/type	web-based
Code license	MIT
Website	energytransitionmodel.com
Interactive website	pro.energytransitionmodel.com
Repository	github.com/quintel/documentation

The Energy Transition Model (ETM) is an interactive web-based model using a holistic description of a country's energy system. It is being developed by Quintel Intelligence, Amsterdam, the Netherlands. The project maintains a project website, an interactive website, and a GitHub repository. ETM is written in Ruby (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, the model calculates the primary energy use, the total cost, and the resulting CO₂ emissions. ETM 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

Project	EnergyPATHWAYS
Host	Evolved Energy Research
Scope/type	mostly simulation
Code license	MIT
Repository	github.com/energyPATHWAYS/energyPATHWAYS

EnergyPATHWAYS is a bottom-up energy sector model used to explore the near-term implications of long-term deep decarbonization. The lead developer is energy and climate protection consultancy, Evolved Energy Research, San Francisco, USA. The code is hosted on GitHub. EnergyPATHWAYS is written in Python and links to the open source Cbc solver. Alternatively, the GLPK, CPLEX, or Gurobi solvers can be employed. EnergyPATHWAYS utilizes the PostgreSQL object-relational database management system (ORDBMS) to manage its data.

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 UN Deep Decarbonization Pathways Project (DDPP) assessments for the United States. As of 2016, the DDPP plans to employ EnergyPATHWAYS for future analysis.

ETEM

Project	ETEM
Host	ORDECSYS
Scope/type	municipal
Code license	Eclipse 1.0
Website	apps.ordecsys.com/etem www.energyplan.eu/othertools/local/etem/

ETEM stands for Energy Technology Environment Model. The ETEM model offers a similar structure to OSeMOSYS but is aimed at urban planning. The software is being developed by the ORDECSYS company, Chêne-Bougeries, Switzerland, supported with European Union and national research grants. The project has two websites. The software can be downloaded from first of these websites (but as of 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 enabled by the development of smart grids.

The ETEM model has been applied to Luxembourg, the Geneva and Basel-Bern-Zurich cantons, Switzerland, and the Grenoble metropolitan and Midi-Pyrénées region, 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 use to deal with the uncertainty in future electricity prices and the uptake of electric vehicles.

ficus

Project	ficus
Host	Technical University of Munich
Scope/type	local electricity and heat
Code license	GPLv3
Repository	github.com/yabata/ficus
Documentation	ficus.readthedocs.io/en/latest/

ficus is a mixed integer optimization model for local energy systems. It is being developed at the Institute for Energy Economy and Application Technology, Technical University of Munich, 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 and Gurobi solvers.

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 specified cost time series for imported commodities as well as peak demand charges with a configurable timebase for each commodity in use.

oemof

Project	oemof
Host	Reiner Lemoine Institute University of Flensburg Flensburg University of Applied Sciences
Scope/type	electricity and heat
Code license	GPLv3
Website	oemof.wordpress.com
Repository	github.com/oemof/
Documentation	oemof.readthedocs.io

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.

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 power and heat 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.

OSeMOSYS

Project	OSeMOSYS
Host	community project
Scope/type	national planning
Code license	GPLv3
Website	www.osemosys.org

OSeMOSYS stands for Open Source Energy Modelling System. OSeMOSYS is intended for national policy development and uses an intertemperal optimization framework. The model posits a single socially motivated operator/investor with perfect foresight. The OSeMOSYS project is a community endeavor, supported by the Energy Systems Analysis Group, KTH, Stockholm, Sweden. The project maintains a website, from which the source code and an example model (named UTOPIA) can be downloaded. The project also offers an internet forum, but (as of June 2016) a Facebook account is required. OSeMOSYS is written in MathProg, a high-level mathematical programming language. An OSeMOSYS model is first processed using the GNU glpsol utility (part of GLPK) and then solved directly using the GLPK solver or alternatively passed to the commercial CPLEX or Gurobi solvers. A PDF manual is available.

OSeMOSYS is used for the analysis of energy systems over the medium (10–15 years) and long term (50–100 years). OSeMOSYS uses 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, defined by their potentials and costs. Technical constraints, economic restrictions, and/or environmental targets may also be imposed to reflect policy considerations. Supported regions include Africa (all countries), the Baltic States, Bolivia, the EU-28 (under development), Nicaragua, South America, and Sweden. OSeMOSYS is available in extended and compact formulations, either of which should give identical results. In its extended version, OSeMOSYS comprises a little more than 400 lines of code.

A key paper describing OSeMOSYS is available. Some of the studies using OSeMOSYS have been conducted in sub-Saharan Africa. A 2011 study uses OSeMOSYS to investigate the role of household investment decisions. A 2012 study extends OSeMOSYS to capture the salient features of smart grids, the paper explains how to model variability in generation, flexible demand, and grid storage and how these impact on the stability of the grid. 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, Italy. One of the aims of the exercise was to encourage citizen to participate in the energy planning process. Preliminary results indicate that this was successful and open modeling is needed to properly include both the technological dynamics and the non-technological issues.

TEMOA

Project	TEMOA
Host	North Carolina State University
Scope/type	system planning
Code license	GPLv2
Website	temoaproject.org
Repository	github.com/TemoaProject/temoa/

TEMOA stands for Tools for Energy Model Optimization and Analysis. The software is being developed by the Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, North Carolina, USA. The project runs a website and a forum. The source code is hosted on GitHub. The model is programmed in Pyomo, an optimization components library written in Python. 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".

Other open energy models

Other open energy models includes energy accounting models and distribution network models. Accounting models are often implemented using spreadsheets or relational databases.

Other open energy models

Project	Host	License	Access	Coding	Documentation	Scope/type
Decarbonization Calculator	Deep Decarbonization Pathways Project	TBA	download	Excel/VBA	manual	spreadsheet
pandapower	University of Kassel Fraunhofer Institute IWES	BSD-new	on application	Python	website	automated power system analysis

DDPP Decarbonization Calculator

Project	DDPP Decarbonization Calculator
Host	Deep Decarbonization Pathways Project
Scope/type	spreadsheet
Code license	TBA
Website	deepdecarbonization.org/research-methods/ddpp-collective-toolkit/
Documentation	deepdecarbonization.org/wp-content/uploads/2015/09/DDPP-Decarbonization-Calculator-Users-Guide.pdf

The DDPP Decarbonization Calculator is a spreadsheet-based energy system model used to explore different pathways to deep decarbonization. It is being developed by the Deep Decarbonization Pathways Project (DDPP), headquartered in Paris, France. The calculator consists of a single spreadsheet written in Excel/VBA. The project has a small website, from where the software can be downloaded. The user is responsible for gathering the necessary data. A PDF manual is available.

The Decarbonization Calculator is intended to represent a simple energy-economy system that can be characterized using a reasonable small set of readily-found input data.

pandapower

Project	pandapower
Host	University of Kassel Fraunhofer Institute IWES
Scope/type	automated power system analysis
Code license	BSD-new
Website	www.uni-kassel.de/go/pandapower
Repository	github.com/panda-power/pandapower
Documentation	www.uni-kassel.de/go/pp_docs

pandapower is a power system analysis and optimization program being developed by the Energy Management and Power System Operation research group, University of Kassel and the Department for Distribution System Operation, Fraunhofer Institute for Wind Energy and Power Systems Technology (IWES), both of Kassel, Germany. The project maintains a website, an emailing list, and online documentation. PDF documentation is also available. While the code is hosted on GitHub, authorization is required for access. pandapower is written in Python. It uses the pandas library for data manipulation and analysis and the PYPOWER library  to solve for power flow.

pandapower supports the automated analysis and optimization of distribution and sub-transmission networks. This allows a large of number of scenarios to be explored, based on different future grid configurations and technologies. pandapower offers a collection of power system elements, including: lines, 2-winding transformers, 3-winding transformers, and ward-equivalents. It also contains a switch model that allows the modeling of ideal bus-bus switches as well as bus-line/bus-trafo switches. The software supports topological searching. Networks can be plotted, with or without geographical information, using the matplotlib library.

A 2016 article evaluated the usefulness of the software by undertaking several case studies with major distribution system operators (DSO). These studies examined the integration of increasing levels of photovoltaics into existing distribution grids. The authors conclude that being able to test a large number of detailed scenarios is essential for robust grid planning. Notwithstanding, issues of data availability and problem dimensionality will continue to present challenges for modelers.

Project statistics

Statistics for the 25 open energy modeling projects discussed are as follows:

Primary origin Country Count Australia 2 Denmark 1 Germany 11 Netherlands 3 Sweden 1 Switzerland 2 United Kingdom  2 United States 3

Programming paradigms Paradigm Count Imperative programming 1 Mathematical programming 5 Object-oriented programming  17 Spreadsheet 2

Programming components

Component models

A number of technical component models are now also open source. While these component models do not constitute systems models aimed at public policy development (the focus of this page), they nonetheless warrant a mention. Component models can be linked or otherwise adapted into these broader initiatives.

• Sandia photovoltaic array performance model

A number of electricity auction models have been written in GAMS, AMPL, MathProg, and other languages. These include:

• the EPOC nodal pricing model

• vSPD nodal pricing model

• for Australian National Electricity Market examples using MathProg, see wikibooks: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. Proprietary solvers outperform open source solvers by a considerable margin, so choosing an open solver will limit performance in terms of both speed and memory consumption.

See also

General

• Comprehensive Knowledge Archive Network (CKAN) – a web-based open data management system
• Climate change mitigation scenarios
• Collaborative software development model
• Energy modeling – the process of building computer models of energy systems
• Open Energy Modelling Initiative – a European-based energy modeling community
• Open Knowledge International – a global non-profit network that promotes and shares information

Software

• Cbc (COIN-OR Branch and Cut) – an open source optimization solver
• 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 optimization solver

Further information

• The Open Energy Modelling Initiative open models wiki

External links

• Expert system for an Intelligent Supply of Thermal Energy in Industry (EINSTEIN), a project for single-facility analysis
• OpenEnergyMonitor, an open source energy use monitoring project
• Open Power System Data, an open electricity data project for Germany and beyond
• SAM Solar Advisor Model project for evaluating photovoltaic installations
• TRNSYS Transient System Simulation Tool

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