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Open energy system models or open-source energy system models are energy system models that also classify as open-source software. Energy system models are used to explore the operational dynamics and/or structural development of energy systems – and are often applied to questions of energy policy. Open-source model development is usually a team effort and typically constituted as either an academic project, a commercial project, or a genuinely inclusive community initiative.

There is also a parallel effort to assemble and collate open energy system data – for use in energy system models – using an open data approach.

Open energy system data and open energy system modeling are relatively new activities. Indeed there are relatively few projects that pre-date 2010. Several drivers favor these initiatives. There is increasing pressure to make public policy energy models more transparent with the aim of improving their acceptance by the public and by policymakers.^[1] There is also a desire to leverage the benefits that open data and open-source software development may bring, including reduced duplication of effort, better sharing of ideas, improved quality, and wider usage.

Energy system models, in general, vary tremendously in terms of their type, design, programming, application, scope, and level of detail. Most models either simulate or optimize energy systems in order to investigate and improve their performance and reduce their impacts. Some models are specifically suited to intermittent renewable technologies, others to municipal systems, and others to long-term national capacity expansion or system transformation. Some attempt to capture the demand-side, while others treat electricity, fuel, and heat demand as exogenous inputs. Models also vary in terms of their positioning on the engineering–economics spectrum and can variously represent financial costs as exogenous, embed agent-based investment decisions and price discovery, or include a partial equilibrium economy.

General considerations

An energy system modeling project typically comprises a codebase, datasets, and software documentation and perhaps scientific publications.^[2] The project repository may be hosted on an institutional server or on a public code-hosting site, such as GitHub.

Projects vary markedly in their attitudes to membership. Academic projects have, historically at least, been limited to trusted individuals. Open projects, like OSeMOSYS, have adopted the open software movement's ethos of inclusion. Projects may offer emailing lists, web forums, and even wikis, as well as distributed source control and issues tracking features. The software, data, and documentation licenses also vary. The GNU GPL license is often used for the source code and a Creative Commons license for the data and documentation.

A number of programming languages have been deployed for software development, including: Python, R, GAMS, MathProg, C++, Java, Matlab, Octave, Mathematica, and Excel/VBA. A number of languages are used for the pre-processing and post-processing of data and for visualization, including: Excel, R, Matlab, Python, and Graphviz. Relational and object-relational databases are also used to manage datasets.

Some software is written as a classic mathematical program, using dedicated high-level languages like GAMS and MathProg. This code is naturally parsimonious. The proprietary language GAMS tends to be used for academic projects, whereas MathProg, its free equivalent, is preferred for community projects. A non-academic GAMS license costs several thousand dollars and thus presents a substantial barrier to non-institutional users.^[3]

Other software classifies as a 'modeling framework', meaning that the description of the system is part of the dataset and not hardcoded into the model itself. Such software normally offers a 'library' of technologies that the user can then connect and particularize to suit their needs. Modeling frameworks typically employ an object-oriented language, such as C++, Java, or Python. Modeling frameworks may easily run to tens of thousands of lines of code.

Some modeling projects release only their codebase, while others ship their datasets as well. To honor the process of peer review, some argue that it is essential to place both the source code and the data under publicly accessible version control so that third-parties can run, verify, and scrutinize specific models.^[4] Projects which make their source code or spreadsheets available for public download, but which omit a free software license and make no effort to otherwise collaborate are not considered open models and are not listed on this page.

The Deep Decarbonization Pathways Project (DDPP) is seeking to improve its modelling methodologies across the 16 nations that participate. A key motivation being "the intertwined goals of transparency, communicability and policy credibility."^[5]^: S27 Some of the models used in the project are already open-source, for instance, EnergyPATHWAYS.

Published surveys on open and closed energy system modeling have focused on decentralized planning,^[6] modeling methods,^[7] renewables integration,^[8] and the use of layered models to support climate protection policy.^[9] DDPP researchers have also analyzed model typologies.^[5]^{: S30–S31}

Open energy system data projects

Various national governments and the European Union are developing meta-data standards and putting key policy statistics and datasets online. This includes energy supply data and energy trading data. One key component is the SDMX Statistical Data and Metadata eXchange standard. Sponsors of SDMX include Eurostat and various UN agencies. The US Department of Energy publishes energy information for the United States. The availability of municipal energy data depends on data policies of the relevant city administrations and utility providers.

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 plants.

Open energy system data projects
Project	Host	License	Access	Data formats	Documentation	Scope/type
Enipedia	Delft University of Technology	ODbL	web	RDF, SPARQL, JSON	wiki	global materials and energy
Open Power System Data	joint project, Germany	CC–BY 4.0	download	JSON, CSV	website	western European electricity system

Enipedia

The semantic wiki-site and database Enipedia lists energy systems data worldwide.^[10] 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.^[11]

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.^[12] A 2012 chapter introduces a system of systems engineering (SoSE) perspective and outlines how agent-based models and crowd-sourced data can contribute to the solving of global issues.^[13]

Open Power System Data

The Open Power System Data project seeks to characterize the German and western European power plant fleets, their associated transmission network, and related information.^[14] 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 based in Germany. The project is funded from August 2015 to July 2017.

Open energy system modeling projects

Of the projects listed, 10 are from Germany, 3 from the Netherlands, 3 from the US, 2 from Australia, 2 from Switzerland, 2 from the UK, and 1 from Denmark. Again, of the projects listed, 16 are written using an object-oriented language, 5 use a high-level mathematical programming language, 2 are implemented as spreadsheets, and 1 uses an imperative programming language.

Open energy system modeling projects
Project	Host	License	Access	Coding	Documentation	Scope/type
Balmorel	Denmark	—	registration	GAMS	manual	energy markets
Calliope	ETH Zurich	Apache 2.0	download	Python	manual, website, list	dispatch and investment
Decarbonization Calculator	Deep Decarbonization Pathways Project	—	download	Excel/VBA	manual	spreadsheet
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 model
EMMA	Neon Neue Energieökonomik	CC BY-SA 3.0	download	GAMS	website	electricity market
Energy Transition Model	Quintel Intelligence	MIT	GitHub	Ruby (on Rails)	website	all sectors, web-based
EnergyNumbers–Balancing	University College London	GPLv3	on application	Fortran, PHP	—	electricity, 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
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
oemof	ZNES Flensburg	GPLv3	GitHub	Python	website	electricity and heat
OSeMOSYS	OSeMOSYS community	GPLv3	download	MathProg	website, forum	national planning
PowerMatcher	Flexiblepower Alliance Network	Apache 2.0	GitHub	Java	website	smart grid
PyPSA	Goethe University Frankfurt	GPLv3	GitHub	Python	website	electric power systems
renpass	ZNES Flensburg	GPLv3	invitation	R, MySQL	manual	renewables pathways
SciGRID	Next Energy	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 model
TEMOA	North Carolina State University	GPLv2	registration	Python	website, forum	system planning
URBS	Technical University of Munich	GPLv3	GitHub	Python	website	distributed energy systems

Balmorel

Balmorel is a market-based energy system model from Denmark. 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.^[15]^[16]^[17] 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.^[18] These ambitions and limitations have long since been superseded and Balmorel is no longer tied to its original geography and issues.^[16] 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).

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.^[19] 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.^[20] 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.^[21]

Calliope

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 a website, another website for documentation, 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.^[22]

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.^[23] 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.^[24]

DDPP Decarbonization Calculator

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.^[25]

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.

DESSTinEE

DESSTinEE stands for Demand for Energy Services, Supply and Transmission in EuropE. DESSTinEE is a model of the European energy system in 2050 with a focus on the electricity system. DESSTinEE is being developed primarily at the Imperial College Business School, Imperial College London (ICL), London, United Kingdom. The project maintains a website, from where the software can be downloaded. DESSTinEE is written in Excel/VBA and comprises a set of standalone spreadsheets. A flier is available.^[26]

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 utilisation falls 15–20%, in part due to the substantial uptake of heat pumps and electric vehicles. These are significant changes.^[27]

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 project runs a website from which the codebase and datasets for Germany can be downloaded. The model is fully described in a DIW working paper.^[28] 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.^[28]

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. The project runs a website and the codebase is hosted on GitHub. A factsheet is available.^[29] And software documentation is available.^[30] 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.^[31] 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 behaviours 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. And 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.^[32]

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, as can the latest manual.^[33] The codebase is not currently on a hosting facility. EMMA is written in GAMS and uses the CPLEX commercial solver.

Energy Transition Model

The Energy Transition Model (ETM) is an interactive web-based model using a holistic description of a country's energy system. It is being developed by Quintel Intelligence, Amsterdam, the Netherlands. The project maintains a 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 and 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, useable-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).

EnergyNumbers–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 and is hosted on GitHub.^{[dead link]} Users can request access to the codebase by email. EnergyNumbers-Balancing is programmed in Fortran, PHP, JavaScript, HTML, and CSS.

The model uses historic demand data, and historic (half) 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 and Germany are supported.

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 project maintains a website and another website. 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.^[34] 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

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, in combination with European Union and national research grants. The project has two websites: website and website. The software can be downloaded from website (but as of July 2016, this looks out of date). A manual is available with the software.^[35] ETEM is written in MathProg (note that GMPL, referred to in the documentation, is an alternative name for MathProg). Presentations describing ETEM are available.^[36]^[37]

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, now 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 a world computable general equilibrium model (CGEM), named GEMINI-E3, to complete the analysis.^[38] 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.^[39]

ficus

ficus is a mixed integer optimization model for local energy systems. It is being developed at the Institute for Energy Economy and Application Technology, Technical University of Munich, Munich, Germany. The project runs a website and another 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.

GENESYS

GENESYS stands for Genetic Optimisation of a European Energy Supply System. The software is being developed jointly by the Institute of Power Systems and Power Economics (IAEW) and the Institute for Power Electronics and Electrical Drives (ISEA), both of the 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.^[40] Detailed descriptions of the software are available.^[41]^[42] 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 optimisation of this energy system in combination with an evolutionary approach. The optimisation 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 comes with a set of input time series and a set of parameters for the year 2050, which can be adjusted by users.

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 is 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.^[41]

A 2016 study looked at the relationship between storage and transmission capacity under high shares of renewable energy sources (RES) in a 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.^[42]

NEMO

NEMO, the National Electricity Market Optimiser, is a chronological dispatch model for testing and optimising different portfolios of conventional and renewable electricity generation technologies. It applies solely to the Australian National Electricity Market (NEM). 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 also runs an email list. NEMO is written in Python. Optimisations 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.

oemof

oemof stands for Open Energy Modelling Framework. The project is managed by the Reiner Lemoine Institute, Berlin, Germany and the Center for Sustainable Energy Systems (ZNES) at the University of Flensburg and the Flensburg University of Applied Sciences, both Flensburg, Germany. The project runs a website, another website, and a GitHub repository. oemof is written in Python and uses Pyomo and COIN-OR components for optimization.

oemof classes as an energy modeling framework. It consists of a linear optimisation (no integer variables) 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 model.

OSeMOSYS

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 endeavour. 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.^[43]

OSeMOSYS is used for the analysis of energy systems over the medium (10–15 years) and long term (50–100 years). OSeMOSYS uses linear optimisation – 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 by 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.^[44] 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.^[45] 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.^[46] In a 2016 study, OSeMOSYS is modified to take into account real consumer behavior.^[47] 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.^[48]

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 ageing) distribution networks.^[49]

PyPSA

PyPSA stands for Python for Power System Analysis. PyPSA is a free software toolbox for simulating and optimising 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

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.^[50] renpass is also described in a PhD thesis.^[51]

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.^[52] A 2014 study uses renpass to model Germany and its neighbors.^[53] 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.^[54] Another 2014 study uses renpass to examine the German Energiewende, the rapid 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.^[55]

SciGRID

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.^[56]^[57]

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

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 GitHub. 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.^[58]

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 modelling assumes a carbon price of AUD $30/tCO₂. Further scenarios examine the goal of 100% renewable generation.^[59]

SWITCH

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 data, 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.^[60]

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.^[4]

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.^[61] TEMOA is "strongly influenced by the well-documented MARKAL/TIMES model generators".^[62]^: 4

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 project runs a website. 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.^[63]^: 11–14

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.^[64] The software has also been used to explore energy systems spanning Europe, the Middle East, and North Africa (EUMENA)^[63] and Indonesia, Malaysia, and Singapore.^[65]

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^[66]

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

the EPOC nodal pricing model^[67]

vSPD nodal pricing model^[68]

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.^[69]

Notes

^ MathProg is a subset of AMPL. It is sometimes possible to convert an AMPL model into MathProg without much effort.

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Further information

The Open Energy Modelling Initiative open models wiki is highly recommended.

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

Category:Energy Category:Energy models Category:Energy policy Category:Mathematical modeling Category:Simulation Category:Systems theory

[67] MathProg is a subset of AMPL. It is sometimes possible to convert an AMPL model into MathProg without much effort.

[acatech-etal-2016-1] tech; 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 28 April 2016.

[bazilian-etal-2012-2] 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: 49–153. doi:10.1016/j.enpol.2012.06.032. Retrieved 17 June 2016.

[gams-2016-3] GAMS — Commercial Price List (PDF). 15 March 2016. Retrieved 11 July 2016.

[decarolis-etal-2012-4] DeCarolis, Joseph F; Hunter, Kevin; Sreepathi, Sarat (2012). "The case for repeatable analysis with energy economy optimization models" (PDF). Energy Economics. 34: 1845–1853. doi:10.1016/j.eneco.2012.07.004. Retrieved 8 July 2016.

[pye-and-bataille-2016-5] 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.

[hiremath-etal-2007-6] Hiremath, RB; Shikha, S; Ravindranath, NH (2007). "Decentralized energy planning: modeling and application — a review". Renewable and Sustainable Energy Reviews. 11 (5): 729–752. doi:10.1016/j.rser.2005.07.005.

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