Open energy-system models capture some or all of the energy commodities found in an energy system. Typically
models of the electricity sector are always included. Some models add the heat sector, which can be important for countries with significant
district heating. Other models add gas networks. With the advent of
emobility, other models still include aspects of the transport sector. Indeed, coupling these various sectors using
power-to-X technologies is an emerging area of research. Methods from
stochastic programming are now being implemented to better address the uncertainties associated with renewable generation. most studies deploying the tool have focused on the German energy system in a European context, for instance investigating the tradeoffs between centralized and decentralized designs, the role of grid planning, and the potential of sufficiency measures. In addition, AnyMOD.jl has been used to support policy reports from the
German Institute for Economic Research (DIW) on the
European Green Deal and the coordination of the German
Energiewende.
Backbone Backbone is an energy system modeling framework that allows for a high level of detail and adaptability. It has been used to study city-level energy systems as well as multi-country energy systems. It was originally developed during 20152018 in an Academy of Finlandfunded project 'VaGe' by the Design and Operation of Energy Systems team at
VTT. It has been further developed in a collaboration which includes
VTT,
UCD, and
RUB. The framework is agnostic about what is modeled, but still has capabilities to represent a large range of energy system characteristics such as generation and transfer, reserves, unit commitment, heat diffusion in buildings, storages, multiple emissions and P2X, etc. It offers linear and mixed integer constraints for capturing things like unit start-ups and investment decisions. It allows the modeler to change the temporal resolution of the model between time steps. and this enables, for example, to use a coarser time resolution further ahead in the time horizon of the model. The model can be solved as an investment model (single or multi-period, myopic, or full foresight) or as a rolling production cost unit commitment model to simulate operations. Backbone's own wiki page has a tutorial for new users, example models, and user created mods. Open datasets include Northern European model for electricity, heat, and hydrogen and district heating and cooling model for the Finnish capital region.
Balmorel Balmorel is a market-based energy system model from Denmark. Development was originally financed by the Danish Energy Research Program in 2001. The Balmorel project maintains an extensive website, from where the
codebase and
datasets can be download as a
zip file. Users are encouraged to register. Documentation is available from the same site. Balmorel is written in
GAMS. The original aim of the Balmorel project was to construct a
partial equilibrium model of the electricity and
CHP sectors in the
Baltic Sea region, for the purposes of policy analysis. These ambitions and limitations have long since been superseded and Balmorel is no longer tied to its original geography and policy questions. 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 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 Calliope is an energy system modeling framework, with a focus on flexibility, high spatial and temporal resolution, and the ability to execute different runs using the same base-case dataset. The project is being developed at the Department of Environmental Systems Science,
ETH Zurich,
Zürich, Switzerland. The project maintains a website, hosts the
codebase at
GitHub, operates an issues tracker, and runs two
email lists. Calliope is written in
Python and uses the
Pyomo library. It can link to the open source
GLPK solver and the commercial
CPLEX solver. PDF documentation is available. And a twopage software review is available. A Calliope model consists of a collection of structured text files, in
YAML and
CSV formats, that define the technologies, locations, and resource potentials. Calliope takes these files, constructs a pure
linear optimization (no integer variables) problem, solves it, and reports the results in the form of
pandas data structures for analysis. The framework contains five
abstract base technologies – supply, demand, conversion, storage, transmission – from which new concrete technologies can be derived. The design of Calliope enforces the clear separation of framework (code) and model (data). A 2015 study uses Calliope to compare the future roles of
nuclear power and
CSP in
South Africa. It finds CSP could be competitive with nuclear by 2030 for baseload and more competitive when producing above baseload. CSP also offers less investment risk, less environmental risk, and other co-benefits. A second 2015 study compares a large number of cost-optimal future power systems for
Great Britain. Three generation technologies are tested: renewables, nuclear power, and fossil fuels with and without
carbon capture and storage (CCS). The scenarios are assessed on financial cost, emissions reductions, and energy security. Up to 60% of
variable renewable capacity is possible with little increase in cost, while higher shares require large-scale
storage, imports, and/or
dispatchable renewables such as
tidal range. Calliope codeveloper Stefan Pfenninger discusses the role that energy system models can play in supporting realworld decisions at a seminar held in mid2021. One study cited investigates the consequences of pursuing energy
selfsufficiency by duly adding increasingly restrictive internal constraints. Another at near optimal solutions for
Italy. A2023 video describes recent developments, many of which are designed to benefit users.
DESSTinEE DESSTinEE stands for Demand for Energy Services, Supply and Transmission in EuropE. DESSTinEE is a model of the European energy system in 2050 with a focus on the electricity system. DESSTinEE is being developed primarily at the
Imperial College Business School,
Imperial College London (ICL),
London, United Kingdom. The software can be downloaded from the project website. DESSTinEE is written in
Excel/
VBA and comprises a set of standalone
spreadsheets. A flier is available. DESSTinEE is designed to investigate assumptions about the technical requirements for energy transport – particularly electricity – and the scale of the economic challenge to develop the necessary infrastructure. Forty countries are considered in and around Europe and ten forms of primary and secondary energy are supported. The model uses a predictive simulation technique, rather than solving for either
partial or
general equilibrium. The model projects annual energy demands for each country to 2050, synthesizes hourly profiles for electricity demand in 2010 and 2050, and simulates the least-cost generation and transmission of electricity around the region. A 2016 study using DESSTinEE (and a second model eLOAD) examines the evolution of electricity load curves in Germany and Britain from the present until 2050. In 2050, peak loads and ramp rates rise 20–60% and system utilization falls 15–20%, in part due to the substantial uptake of
heat pumps and
electric vehicles. These are significant changes.
Energy Transition Model The Energy Transition Model (ETM) is an interactive web-based model using a holistic description of a country's energy system. It is being developed by Quintel Intelligence,
Amsterdam, the Netherlands. The project maintains a project website, an interactive website, and a
GitHub repository. ETM is written in
Ruby (on
Rails) and displays in a
web browser. ETM consists of several software components as described in the documentation. ETM is fully interactive. After selecting a region (France, Germany, the Netherlands, Poland, Spain, United Kingdom, EU-27, or Brazil) and a year (2020, 2030, 2040, or 2050), the user can set 300 sliders (or enter numerical values) to explore the following: • targets: set goals for the scenario and see if they can be achieved, targets comprise: reductions, renewables shares, total cost, and caps on imports • demands: expand or restrict energy demand in the future • costs: project the future costs of energy carriers and energy technologies, these costs do not include taxes or subsidies • supplies: select which technologies can be used to produce heat or electricity ETM is based on an energy graph (
digraph) where nodes (
vertices) can convert from one type of energy to another, possibly with losses. The connections (
directed edges) are the energy flows and are characterized by volume (in
megajoules) and carrier type (such as coal, electricity, usable-heat, and so forth). Given a demand and other choices, ETM calculates the primary energy use, the total cost, and the resulting emissions. The model is demand driven, meaning that the digraph is traversed from
useful demand (such as space heating, hot water usage, and car-kilometers) to
primary demand (the extraction of gas, the import of coal, and so forth).
EnergyPATHWAYS EnergyPATHWAYS is a bottom-up energy sector model used to explore the near-term implications of long-term deep decarbonization. The lead developer is energy and climate protection consultancy, Evolved Energy Research,
San Francisco, USA. The code is hosted on
GitHub. EnergyPATHWAYS is written in
Python and links to the open source
Cbc solver. Alternatively, the
GLPK,
or CPLEX solvers can be employed. EnergyPATHWAYS utilizes the
PostgreSQL object-relational database management system (ORDBMS) to manage its
data. EnergyPATHWAYS is a comprehensive accounting framework used to construct economy-wide energy infrastructure scenarios. While portions of the model do use
linear programming techniques, for instance, for electricity dispatch, the EnergyPATHWAYS model is not fundamentally an optimization model and embeds few decision dynamics. EnergyPATHWAYS offers detailed energy, cost, and emissions accounting for the energy flows from primary supply to final demand. The energy system representation is flexible, allowing for differing levels of detail and the nesting of cities, states, and countries. The model uses hourly least-cost electricity dispatch and supports
power-to-gas, short-duration energy storage, long-duration energy storage, and
demand response. Scenarios typically run to 2050. A predecessor of the EnergyPATHWAYS software, named simply PATHWAYS, has been used to construct policy models. The California PATHWAYS model was used to inform Californian state climate targets for 2030. And the US PATHWAYS model contributed to the
United Nations Deep Decarbonization Pathways Project (DDPP) assessments for the United States. , the DDPP plans to employ EnergyPATHWAYS for future analysis.
ETEM ETEM stands for Energy Technology Environment Model. The ETEM model offers a similar structure to
OSeMOSYS but is aimed at urban planning. The software is being developed by the ORDECSYS company,
Chêne-Bougeries, Switzerland, supported with European Union and national research grants. The project has two websites. The software can be downloaded from first of these websites (but , 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 50years, in two or five year steps, with time slices of four seasons using typically individual days or finer. The spatial resolution can be highly detailed. Electricity and heat are both supported, as are
district heating networks, household energy systems, and grid storage, including the use of
plug-in hybrid electric vehicles (PHEV). ETEM-SG, a development, supports
demand response, an option which would be enabled by the development of
smart grids. The ETEM model has been applied to Luxembourg, the Geneva and Basel-Bern-Zurich cantons in Switzerland, and the Grenoble metropolitan and Midi-Pyrénées region in France. A 2005 study uses ETEM to study climate protection in the Swiss housing sector. The ETEM model was coupled with the GEMINI-E3 world
computable general equilibrium model (CGEM) to complete the analysis. A 2012 study examines the design of
smart grids. As distribution systems become more intelligent, so must the models needed to analysis them. ETEM is used to assess the potential of smart grid technologies using a
case study, roughly calibrated on the
Geneva canton, under three scenarios. These scenarios apply different constraints on emissions and electricity imports. A stochastic approach is used to deal with the uncertainty in future electricity prices and the uptake of electric vehicles.
ficus ficus is a
mixed integer optimization model for local energy systems. It is being developed at the Institute for Energy Economy and Application Technology,
Technical University of Munich,
Munich, Germany. The project maintains a website. The project is hosted on
GitHub. ficus is written in
Python and uses the
Pyomo library. The user can choose between the open source
GLPK solver or the commercial
CPLEX solver. Based on
URBS, ficus was originally developed for optimizing the energy systems of factories and has now been extended to include local energy systems. ficus supports multiple energy commodities – goods that can be imported or exported, generated, stored, or consumed – including electricity and heat. It supports multiple-input and multiple-output energy conversion technologies with load-dependent efficiencies. The objective of the model is to supply the given demand at minimal cost. ficus uses exogenous cost time series for imported commodities as well as peak demand charges with a configurable timebase for each commodity in use.
GENeSYS-MOD The Global Energy System Model (GENeSYSMOD) is a linear cost-minimizing optimization model being developed at
Technische Universität Berlin, Germany. The project was originally based on the
OSeMOSYS framework and the first version was released in 2017 using
GAMS. The codebase was later translated into
Julia. Both versions and a representative dataset are available on GitHub. GENeSYSMOD couples the demand sectors covering electricity, buildings, industry, and transport and finds the cost-optimal investment into conventional and renewable energy generation, storage, and infrastructure. The research focus is on long-term system development and pathway analysis. The model was first used to analyze decarbonization scenarios at the global level, broken down into ten regions. However, the framework is highly flexible, allowing for calculations at various levels of detail, from individual households to global aggregations, depending on the desired research question and availability of input data. A2019 study examined the lowcarbon transition of the European energy system and specifically the problem of
stranded assets under arange of scenarios. It found that up to in fossil-fueled capacities could be stranded by 2035 unless stronger policy signals are able to address shortterm planning biases. Another 2019 study evaluates China's energy system transformation, highlighting the need to reduce coal consumption by 60% by 2050 to meet global climate targets. Renewable energies, and in particular
photovoltaics and
onshore wind, emerge as cost-effective solutions, but overcoming local resistance and increasing
stakeholder engagement remain crucial for success. A2021 study investigates the
European Green Deal goal of achieving 100%
greenhouse gas reductions by 2050, examining the interplay of technological developments, policy imperatives, and societal attitudes. The study presents four future storylines that highlight the critical contribution of high rates of
electrification combined with nearterm technology deployment to achieve the necessarily rapid decarbonization.
GenX and deploys the
JuMP library for building the underlying optimization problem. GenX through
JuMP can utilize various open source (including
CBC/
CLP) and commercial optimization solvers (including
CPLEX). In June2021, the project launched as an active open source project and test suites are available to assist onboarding. In parallel, the
PowerGenome project is designed to provide GenX with a comprehensive current state dataset of the
United States electricity system. That dataset can then be used as a springboard to develop future scenarios. GenX has been used to explore long-term storage options in systems with high renewables shares, to explore the value of '
firm' low-carbon power generation options, and a variety of other applications. While North America remains a key focus, the software has been applied to problems in India, Italy, and Spain. GenX was deployed in a 2021 case study with
Louisville Gas and Electric and
Kentucky Utilities that showed that stakeholder-driven modeling utilizing opensource tools and public data can contribute productively to utilityled analysis and planning. A mid2022 study examined the
natural gas crisis facing Europe, and particularly Germany, and concluded that there are several feasible paths (labeled "cases") to eliminate all imports of Russian natural gas by October2022. Ongoing work seeks to examine the effect of extending the operating lives of Germany's
three remaining nuclear reactors past 2022 and the effect of strong
drought conditions on hydrogeneration and the system more generally.
oemof oemof stands for Open Energy Modelling Framework. The project is managed by the Reiner Lemoine Institute,
Berlin, Germany and the Center for Sustainable Energy Systems (CSES or ZNES) at the
University of Flensburg and the
Flensburg University of Applied Sciences, both
Flensburg, Germany. The project runs two websites and a
GitHub repository. oemof is written in
Python and uses
Pyomo and
COIN-OR components for optimization. Energy systems can be represented using spreadsheets (
CSV) which should simplify data preparation. was released on 1December 2016. oemof classes as an energy modeling framework. It consists of a
linear or
mixed integer optimization problem formulation library (solph), an input data generation library (feedin-data), and other auxiliary libraries. The solph library is used to represent multi-regional and multi-sectoral (electricity, heat, gas, mobility) systems and can optimize for different targets, such as financial cost or emissions. Furthermore, it is possible to switch between dispatch and investment modes. In terms of scope, oemof can capture the European power system or alternatively it can describe a complex local power and heat sector scheme. oemof has been applied in subSaharan Africa. A masters project in 2020 compared oemof and
OSeMOSYS.
OSeMOSYS OSeMOSYS stands for Open Source Energy Modelling System. OSeMOSYS is intended for national and regional policy development and uses an intertemporal optimization framework. The model posits a single socially motivated operator/investor with perfect foresight. The OSeMOSYS project is a community endeavor, supported by the division of Energy Systems,
KTH Royal Institute of Technology,
Stockholm, Sweden. The project maintains a website providing background. The project also offers several active
internet forums on
Google Groups. OSeMOSYS was originally written in
MathProg, a high-level
mathematical programming language. It was subsequently reimplemented in
GAMS and
Python and all three codebases are now maintained. The project also provides a test model called UTOPIA. A manual is available. OSeMOSYS provides a framework for the analysis of energy systems over the medium (10–15 years) and long term (50–100 years). OSeMOSYS uses pure
linear optimization, with the option of
mixed integer programming for the treatment of, for instance, discrete power plant capacity expansions. It covers most energy sectors, including heat, electricity, and transport. OSeMOSYS is driven by exogenously defined
energy services demands. These are then met through a set of technologies which draw on a set of resources, both characterized by their potentials and costs. These resources are not limited to energy commodities and may include, for example, water and
land-use. This enables OSeMOSYS to be applied in domains other than energy, such as water systems. Technical constraints, economic restrictions, and/or environmental targets may also be imposed to reflect policy considerations. OSeMOSYS is available in extended and compact MathProg formulations, either of which should give identical results. In its extended version, OSeMOSYS comprises a little more than 400
lines of code. OSeMOSYS has been used as a base for constructing reduced models of energy systems. . The paper explains how to model variability in generation, flexible demand, and
grid storage and how these impact on the stability of the grid. OSeMOSYS has been applied to village systems. A 2015 paper compares the merits of stand-alone, mini-grid, and grid electrification for rural areas in
Timor-Leste under differing levels of access. In a 2016 study, OSeMOSYS is modified to take into account realistic consumer behavior. A 2017 paper covering Alberta, Canada factors in the risk of overrunning specified emissions targets because of technological uncertainty. Among other results, the paper finds that solar and wind technologies are built out seven and five years earlier respectively when emissions risks are included. Another 2017 paper analyses the electricity system in Cyprus and finds that, after European Union environmental regulations are applied post-2020, a switch from oil-fired to natural gas generation is indicated. OSeMOSYS has been used to construct wide-area electricity models for
Africa, comprising 45countries and South America, comprising 13countries. It has also been used to support United Nations' regional climate, land, energy, and water strategies (CLEWS) for the
Sava river basin, central Europe, the
Syr Darya river basin, eastern Europe, and Mauritius. Models have previously been built for the
Baltic States,
Bolivia,
Nicaragua,
Sweden, and
Tanzania. A2021 paper summarizes recent applications and also details various versions, forks, and local enhancements related to the OSeMOSYS codebase. A2022 study looked at the effects of a changing climate on the
Ethiopian power system. and Ecuador. Another 2022 study examined water usage, split by withdraws and consumption, for several low carbon energy strategies for Africa. Another study that year examined renewable energy in
Egypt. And another the Dominican Republic. The Italian island of
Pantelleria was used as a case study to compare battery and hydrogen storage and found that a hybrid system was least cost. In 2016, work started on a
browser-based interface to OSeMOSYS, known as the Model Management Infrastructure (MoManI). Led by the
UN Department of Economic and Social Affairs (DESA), MoManI is being trialled in selected countries. The interface can be used to construct models, visualize results, and develop better scenarios. Atlantis is the name of a fictional country case-study for training purposes. A simplified
GUI interface named clicSAND and utilizing Excel and Access was released in March2021. A
CLI workflow tool named otoole bundles several dedicated utilities, including one that can convert between
OKI frictionless data and
GNU MathProg data formats. In 2022, the project released starterkits for modeling selected countries in Africa, East Asia, and South America. first. The model, funded as part of
Horizon 2020 and falling under work package WP7 of the REEEM project, will be used to help stakeholders engage with a range of sustainable energy futures for Europe. The REEEM project runs from early-2016 until mid-2020. A 2021 paper reviews the OSeMOSYS community, its composition, and its governance activities. And also describes the use of OSeMOSYS in education and for building analytical capacity within developing countries.
PyPSA PyPSA stands for Python for Power System Analysis. PyPSA is a free software toolbox for simulating and optimizing electric power systems and allied sectors. It supports conventional generation, variable wind and solar generation, electricity storage,
coupling to the natural gas, hydrogen, heat, and transport sectors, and hybrid alternating and direct current networks. Moreover, PyPSA is designed to scale well. The project is managed by the Institute for Automation and Applied Informatics (IAI),
Karlsruhe Institute of Technology (KIT),
Karlsruhe, Germany, although the project itself exists independently under its own name and accounts. The project maintains a website and runs an
email list. PyPSA itself is written in
Python and uses the
linopy library. The
source code is hosted on
GitHub and is also released periodically as a
PyPI package. power system map created by and prepared for energy system model runs with PyPSA-Eur The basic functionality of PyPSA is described in a 2018 paper. PyPSA bridges traditional steady-state power flow analysis software and full multi-period energy system models. It can be invoked using either non-linear power flow equations for system simulation or linearized approximations to enable the joint optimization of operations and investment across multiple periods. Generator ramping and multi-period up and down-times can be specified,
DSM is supported, but demand remains
price inelastic. A 2018 study examines potential synergies between
sector coupling and
transmission reinforcement in a future European energy system constrained to reduce
carbon emissions by 95%. The PyPSA-Eur-Sec-30 model captures the
demand-side management potential of
battery electric vehicles (BEV) as well as the role that
power-to-gas, long-term
thermal energy storage, and related technologies can play. Results indicate that BEVs can smooth the daily variations in solar power while the remaining technologies smooth the
synoptic and seasonal variations in both demand and renewable supply. Substantial
buildout of the electricity grid is required for a least-cost configuration. More generally, such a system is both feasible and affordable. The underlying datasets are available from
Zenodo. , PyPSA is used by more than a dozen research institutes and companies worldwide. In 2020, the PyPSA‑Eur‑Sec model for Europe was used to analyze several Paris Agreement Compatible Scenarios for Energy Infrastructure and determined that early action should pay off. On 9January 2019, the project released an interactive web-interfaced "toy" model, using the
Cbc solver, to allow the public to experiment with different future costs and technologies. The site was relaunched on 5November 2019 with some internal improvements, a new URL, and faster solver now completing in about . A newer version now uses the
HiGHS solver. expansion options for Europe and the United Kingdom and the impact of the kind of tradeoffs that might stem from limited public acceptance of new infrastructure. Subsequent work added
endogenous learning effects and identified steeper technology cost reductions than those anticipated by the
European Commission. Work published in 2024 integrated PyPSAEur with the global energy supply chain model TRACE and highlighted the need to coordinate infrastructure policies and import strategies. ADecember2021 study and ongoing work deployed a PyPSA‑PL model to assess policy options for Poland.
Edinburgh University researchers published an independent power system model for
Britain named PyPSAGB in 2024, together with assessments of official netzero Future Energy Scenarios (FES) from the UK
National Grid. Several PyPSA maintainers announced a new
nonprofit startup in June2023 to provide consulting services using PyPSA.
PyPSA meets Earth initiative The PyPSA meets Earth initiative arose in October2022 as a means of gathering together several historically disjoint PyPSA applications. One key strand is the PyPSAAfrica project (previously PyPSA-meets-Africa), launched some months earlier to provide a single model and dataset spanning the
African continent. AJuly2022 webinar cohosted by CPEEL, Nigeria advanced this agenda. The first research paper, released in 2022, examines various pathways for Africa to be netzero by 2060 with solar power and battery storage expected to be the predominant technologies. Another key strand of the initiative is the PyPSAEarth project which seeks to create a global energy systems model at high spatial and temporal resolution.
REMix REMix stands for "Renewable Energy Mix". It is an open source
framework developed by the
German Aerospace Center for setting up
linear or
mixed integer optimization models written in
GAMS. A framework is understood as a collection of mutually compatible
source codes required for a particular model, which can be combined in a modular manner. In this way, the same modeling concepts, along with the associated
source code, can be reutilized to address various content focuses based on a common set of available model features. REMix is developed for applications in energy system modeling studies. It is typically used to set up energy system optimization models, although potential applications beyond energy research are conceivable. In particular, these energy system optimization models are often characterized as bottom-up models in terms of explicitly modeling different technologies. In addition, these models are resolved on a spatial and a temporal dimension. In practical terms, the framework allows for modeling competition between technologies that can serve the same purpose, such as power generation, while also providing insights into when and where a specific technology is required. Additionally, it can be applied to
transportation problems, where the optimal exchange of a commodity between at least two distinct regions needs to be determined. Furthermore, it addresses storage problems, where the optimal balance between production and consumption at different points in time is calculated. REMix offers several key features that make it a robust tool for energy system modeling. It is designed to handle large-scale models with high
spatial and
technological resolutions, making it suitable for complex analyses. The framework also incorporates path optimization, allowing for multi-year analyses and strategic planning over extended periods. Ongoing work deals with very large instances involving path optimization using the parallel solver PIPS-IPM++. A notable feature is its custom accounting capability, provided through the indicator module, which enables flexible definitions of what contributes to the objective functions. Additionally, REMix supports flexible modeling, offering multiple approaches to integrate and model technologies, allowing users to tailor the framework to their specific needs. Finally, it supports
multi-criteria optimization, where, beyond cost minimization, additional factors such as ecological impacts or resilience indicators can be considered in the objective function, providing a more comprehensive approach to system
optimization. In the past, the model has been used to investigate a wide range of research questions. In addition to detailed analyses of the integration of renewable energies into the electricity system, for example, the role of
hydrogen in the
energy system of the future has also been examined. For the purpose of validating the REMix model,
German Aerospace Center has participated in various model comparisons.
TEMOA TEMOA stands for Tools for Energy Model Optimization and Analysis. The software is being developed by the Department of Civil, Construction, and Environmental Engineering,
North Carolina State University,
Raleigh, North Carolina, USA. The project runs a website and a forum. The
source code is hosted on
GitHub. The model is programmed in
Pyomo, an optimization components library written in
Python. TEMOA can be used with any solver that
Pyomo supports, including the open source
GLPK solver. TEMOA uses
version control to publicly archive
source code and
datasets and thereby enable third-parties to verify all published modeling work. TEMOA classes as a modeling framework and is used to conduct analysis using a bottom-up, technology rich energy system model. The model objective is to minimize the system-wide cost of energy supply by deploying and utilizing energy technologies and commodities over time to meet a set of
exogenously specified end-use demands. TEMOA is "strongly influenced by the well-documented
MARKAL/TIMES model generators". TEMOA forms the basis of the Open Energy Outlook (OEO) research project spanning 2020–2022. The OEO project utilizes open source tools and open data to explore deep decarbonization policy options for the United States. From mid2021, an interactive interface located on the main website allows registered users to manipulate scenario data locally, upload structured
SQLite files, and then run these scenarios using the TEMOA software. The service also provides some limited data visualization and project management functionality. ==Specialist models==