Open energy system models

Open energy system models are

.

Energy system models are used to explore future energy systems and are often applied to questions involving

is used to inform the solution process.

Energy regulators and system operators in Europe and North America began adopting open energy system models for planning purposes in the early‑2020s.[1] Open models and open data are increasingly being used by government agencies to guide the develop of net‑zero public policy as well (with examples indicated throughout this article). Companies and engineering consultancies are likewise adopting open models for analysis (again see below).

General considerations

Organization

The open energy modeling projects listed here fall exclusively within the bottom-up paradigm, in which a model is a relatively literal representation of the underlying system.

Several drivers favor the development of open models and open data. There is an increasing interest in making

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

^: 1 The projects listed here are deemed suitable for inclusion through having pending or published academic literature or by being reported in secondary sources.

A 2017 paper lists the benefits of open data and models and discusses the reasons that many projects nonetheless remain closed.[5]^{: 211–213} The paper makes a number of recommendations for projects wishing to transition to a more open approach.^[5]: 214 The authors also conclude that, in terms of openness, energy research has lagged behind other fields, most notably physics, biotechnology, and medicine.^{[5]: 213–214}

Growth

Open energy system modeling came of age in the 2010s. Just two projects were cited in a 2011 paper on the topic: OSeMOSYS and TEMOA.^[6]: 5861 Balmorel was also active at that time, having been made public in 2001.^[b] As of July 2022^[update], 31 such undertakings are listed here (with an approximately equal number waiting to be added). Chang et al (2021) survey modeling trends and find the open to closed division about even after reviewing 54 frameworks — although that interpretation is based on project count and not on uptake and use.^[7] A 2022 model comparison exercise in Germany reported eight from 40 modeling projects (20%) were open source,^[8] these projects also had active communities behind them.^[9]

Transparency, comprehensibility, and reproducibility

The use of open energy system models and open energy data represents one attempt to improve the transparency, comprehensibility, and reproducibility of energy system models, particularly those used to aid public policy development.^[2]

A 2010 paper concerning energy efficiency modeling argues that "an open peer review process can greatly support model verification and validation, which are essential for model development".

^: 4

A one-page opinion piece from 2017 advances the case for using open energy data and modeling to build public trust in policy analysis. The article also argues that scientific journals have a responsibility to require that data and code be submitted alongside text for peer review.^[14] And an academic commentary from 2020 argues that distributed development would facilitate a more diverse contributor base and thus improve model quality — a process supported by online platforms and enabled by open data and code.^[15]

State projects

State-sponsored open source projects in any domain are a relatively new phenomena.

As of 2017^[update], the European Commission now supports several open source energy system modeling projects to aid the transition to a low-carbon energy system for Europe. The Dispa-SET project (below) is modeling the European electricity system and hosts its codebase on GitHub. The MEDEAS project, which will design and implement a new open source energy-economy model for Europe, held its kick-off meeting in February 2016.^{[16]: 6 [17]} As of February 2017^[update], the project had yet to publish any source code. The established OSeMOSYS project (below) is developing a multi-sector energy model for Europe with Commission funding to support stakeholder outreach.^[18] The flagship JRC-EU-TIMES model however remains closed source.^[19]

The United States NEMS national model is available but nonetheless difficult to use. NEMS does not classify as an open source project in the accepted sense.^[14]

A 2021 research call from the European Union Horizon Europe scientific research funding program expressly sought energy system models that are open source.^[20]

Surveys

A survey completed in 2021 investigated the degree to which open energy system modeling frameworks support flexibility options, broken down by supply, demand,

Electricity sector models

Open electricity sector models are confined to just the electricity sector. These models invariably have a temporal resolution of one hour or less. Some models concentrate on the engineering characteristics of the system, including a good representation of high-voltage transmission networks and AC power flow. Others models depict electricity spot markets and are known as dispatch models. While other models embed autonomous agents to capture, for instance, bidding decisions using techniques from bounded rationality. The ability to handle variable renewable energy, transmission systems, and grid storage are becoming important considerations.

Open electricity sector models

Project	Host	License	Access	Coding	Documentation	Scope/type
AMIRIS	German Aerospace Center	Apache 2.0	GitLab	Java	wiki	agent‑based electricity market modeling
Breakthrough Energy Model	Breakthrough Energy Foundation	MIT	GitHub	Python, Julia	website, GitHub	power sector modeling
DIETER	DIW Berlin	MIT	download	GAMS	publication	dispatch and investment
Dispa-SET	EC Joint Research Centre	EUPL 1.1	GitHub	GAMS, Python	website	European transmission and dispatch
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
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 market
OnSSET	KTH Royal Institute of Technology	MIT	GitHub	Python	website, GitHub	cost-effective electrification
pandapower	Fraunhofer Institute IEE	BSD-new	GitHub	Python	website	automated power system analysis
PowerMatcher	Flexiblepower Alliance Network	Apache 2.0	GitHub	Java	website	smart grid
Power TAC	Erasmus Centre for Future Energy Business Erasmus University	Apache 2.0	GitHub	Java	website, forum	automated retail electricity trading simulation
renpass	University of Flensburg	GPLv3	by invitation	R, MySQL	manual	renewables pathways
SciGRID	DLR Institute of Networked Energy Systems	Apache 2.0	git repository	Python	website, newsletter	European transmission grid
SIREN	Sustainable Energy Now	AGPLv3	GitHub	Python	website	renewable generation
SWITCH	University of Hawaiʻi	Apache 2.0	GitHub	Python	website	optimal planning
URBS	Technical University of Munich	GPLv3	GitHub	Python	website	distributed energy systems
Access refers to the methods offered for accessing the codebase.

AMIRIS

Project	AMIRIS
Host	German Aerospace Center
Status	active
Scope/type	agent‑based electricity markets
Code license	Apache-2.0
Data license	CC‑BY‑4.0
Language	Java
Website	dlr-ve.gitlab.io/esy/amiris/home/
Repository	gitlab.com/dlr-ve/esy/amiris/amiris
Documentation	gitlab.com/dlr-ve/esy/amiris/amiris/-/wikis/home
Discussion	forum.openmod.org/tag/amiris
Datasets	gitlab.com/dlr-ve/esy/amiris/examples
Publications	zenodo.org/communities/amiris

AMIRIS is the open Agent-based Market model for the Investigation of Renewable and Integrated energy Systems.^[22] The AMIRIS simulation framework was first developed by the German Aerospace Center (DLR) in 2008 and later released as an open source project in 2021.^[23][24]

AMIRIS enables researchers to address questions regarding future energy markets, their market design, and energy-related policy instruments.^[25] In particular, AMIRIS is able to capture market effects that may arise from the integration of renewable energy sources and flexibility options by considering the strategies and behaviors of the various energy market actors present. For instance, those behaviors can be influenced by the prevailing political framework and by external uncertainties.^[26] AMIRIS may also uncover complex effects that may emerge from the inter‑dependencies of the energy market participants.^[27]

The embedded market clearing algorithm computes electricity prices based on the bids of prototyped market actors. These bids may not only reflect the marginal cost of electricity production but also the limited information available to the actors and related uncertainties. But also the bidding can be strategic as an attempt to game official support instruments or exploit market power opportunities.

Actors in AMIRIS are represented as agents that can be roughly divided into six classes: power plant operators, traders, market operators, policy providers, demand agents, and storage facility operators. In the model, power plant operators provide generation capacities to traders, but do not participate directly in markets. Instead, they supply traders who conduct the marketing and deploy bidding strategies on the operators behalf. Marketplaces serve as trading platforms and calculate market clearing. Policy providers define the regulatory framework which then may impact on the decisions of the other agents. Demand agents request energy directly at the electricity market. Finally, flexibility providers, such as storage operators, use forecasts to determine bidding patterns to match their particular objectives, for instance, projected profit maximization.

Due to its agent‑based and modular nature, AMIRIS can be easily extended or modified.^[28] AMIRIS is based on the open Framework for distributed Agent-based Modelling of Energy systems or FAME.^[29][30][31]

AMIRIS can simulate large‑scale agent systems in acceptable timeframes. For instance, the simulation of one year at hourly resolution may take as little as one minute on a contemporary desktop computer. The researchers at DLR also have access to high-performance computing facilities.

Breakthrough Energy Model

Project	Breakthrough Energy Model
Host	Breakthrough Energy Foundation
Status	active
Scope/type	power sector modeling
Code license	MIT
Data license	CC‑BY‑4.0
Language	Python, Julia
Website	science.breakthroughenergy.org
Repository	github.com/Breakthrough-Energy
Documentation	breakthrough-energy.github.io/docs/index.html

The Breakthrough Energy Model is a production cost model with capacity expansion algorithms and heuristics, originally designed to explore the generation and transmission expansion needs to meet U.S. states’ clean energy goals. The data management occurs within Python and the DCOPF optimization problem is created via Julia. The Breakthrough Energy Model is being developed by the Breakthrough Energy Sciences team.

The open data underlying the model builds upon the synthetic test cases developed by researchers at Texas A&M University.^[32][33][34]

The Breakthrough Energy Model initially explored the generation and transmission expansion necessary to meet clean energy goals in 2030 via the building of a Macro Grid.^[35] Ongoing work adds and expands modules to the model (e.g. electrification of buildings and transportation) to provide a framework for testing numerous scenario combinations. Development of and integration with other open-source data sets is in progress for modeling countries and regions beyond the United States.

The model was applied subsequently the 2021 Texas power crisis, in which winter power outages resulted in hundreds of deaths and billions of dollars in economic losses.^[36]: 1

DIETER

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

DIETER stands for Dispatch and Investment Evaluation Tool with Endogenous Renewables. DIETER is a dispatch and investment model. It was first used to study the role of

commercial solver.

DIETER is framed as a pure linear (no integer variables) cost minimization problem. In the initial formulation, the decision variables include the investment in and dispatch of generation, storage, and DSM capacities in the German wholesale and balancing electricity markets. Later model extensions include vehicle-to-grid interactions and prosumage of solar electricity.^[39][40]

The first study using DIETER examines the power storage requirements for renewables uptake ranging from 60% to 100%. Under the baseline scenario of 80% (the lower bound German government target for 2050), grid storage requirements remain moderate and other options on both the supply side and demand side offer flexibility at low cost. Nonetheless, storage plays an important role in the provision of reserves. Storage becomes more pronounced under higher shares of renewables, but strongly depends on the costs and availability of other flexibility options, particularly biomass availability.^[41]

Dispa-SET

Project	Dispa-SET
Host	EC Joint Research Centre
Status	active
Scope/type	European transmission and dispatch
Code license	EUPL 1.2
Data license	CC‑BY‑4.0
Website	www.dispaset.eu
Repository	github.com/energy-modelling-toolkit/Dispa-SET
Documentation	www.dispaset.eu

Under development at the

The SET in the project name refers to the European Strategic Energy Technology Plan (SET-Plan), which seeks to make Europe a leader in energy technologies that can fulfill future (2020 and 2050) energy and climate targets. Energy system modeling, in various forms, is central to this European Commission initiative.^[45]

The model power system is managed by a single operator with full knowledge of the economic and technical characteristics of the generation units, the loads at each node, and the heavily simplified transmission network. Demand is deemed fully

^: 14–15

Two related publications describe the role and representation of flexibility measures within power systems facing ever greater shares of variable renewable energy (VRE).^[46][47] These flexibility measures comprise: dispatchable generation (with constraints on efficiency, ramp rate, part load, and up and down times), conventional storage (predominantly pumped-storage hydro), cross-border interconnectors, demand side management, renewables curtailment, last resort load shedding, and nascent power-to-X solutions (with X being gas, heat, or mobility). The modeler can set a target for renewables and place caps on CO₂ and other pollutants.^[43] Planned extensions to the software include support for simplified AC power flow ^[c] (transmission is currently treated as a transportation problem), new constraints (like cooling water supply), stochastic scenarios, and the inclusion of markets for ancillary services.^[44]

Dispa-SET has been or is being applied to case studies in Belgium, Bolivia, Greece, Ireland, and the Netherlands. A 2014 Belgium study investigates what if scenarios for different mixes of nuclear generation, combined cycle gas turbine (CCGT) plant, and VRE and finds that the CCGT plants are subject to more aggressive cycling as renewable generation penetrates.^[49]

A 2020 study investigated the collective impact of future climatic conditions on 34 European power systems, including potential variations in solar, wind, and hydro‑power output and electricity demand under various projected meteorological scenarios for the European continent.^[50]

Dispa-SET has been applied in Africa with soft linking to the LISFLOOD model to examine water‑energy nexus problems in the context of a changing climate.^[51]

EMLab-Generation

Project	EMLab-Generation
Host	Delft University of Technology
Status	active
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.^[52] And software documentation is available.^[53] 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.^[54] 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

EMMA

Project	EMMA
Host	Neon Neue Energieökonomik
Status	active
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,

commercial solver.

EMMA models electricity dispatch and investment, minimizing the total cost with respect to investment, generation, and trades between market areas. In economic terms, EMMA classifies as a partial equilibrium model of the wholesale electricity market with a focus on the supply-side. EMMA identifies short-term or long-term optima (or equilibria) and estimates the corresponding capacity mix, hourly prices, dispatch, and cross-border trading. Technically, EMMA is a pure linear program (no integer variables) with about two million non-zero variables. As of 2016^[update], the model covers Belgium, France, Germany, the Netherlands, and Poland and supports conventional generation, renewable generation, and cogeneration.^[56][57]

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.^[58] A 2015 study estimates the welfare-optimal market share for wind and solar power. For wind, this is 20%, three-fold more than at present.^[59]

An independent 2015 study reviews the EMMA model and comments on the high assumed specific costs for renewable investments.^[37]: 6

GENESYS

Project	GENESYS
Host	RWTH Aachen University
Status	active
Scope/type	European electricity system
Code license	LGPLv2.1
Data license	LGPLv2.1
Language	C++
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

solver.

The GENESYS simulation tool is designed to optimize a future EUMENA (Europe, Middle East, and North Africa) power system and assumes a high share of renewable generation. It is able to find an economically optimal distribution of generator, storage, and transmission capacities within a 21 region EUMENA. It allows for the optimization of this energy system in combination with an evolutionary method. The optimization is based on a covariance matrix adaptation evolution strategy (CMA-ES), while the operation is simulated as a hierarchical set-up of system elements which balance the load between the various regions at minimum cost using the network simplex algorithm. GENESYS ships with a set of input time series and a set of parameters for the year 2050, which the user can modify.

A future EUMENA energy supply system with a high share of renewable energy sources (RES) will need a strongly interconnected energy transport grid and significant energy storage capacities. GENESYS was used to dimension the storage and transmission between the 21 different regions. Under the assumption of 100% self-supply, about 2500 GW of RES in total and a storage capacity of about 240000 GWh are needed, corresponding to 6% of the annual energy demand, and a HVDC transmission grid of 375000 GW·km. The combined cost estimate for generation, storage, and transmission, excluding distribution, is 6.87 ¢/kWh.^[61]

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.^[62]

NEMO

Project	NEMO
Host	University of New South Wales
Status	active
Scope/type	Australian NEM market
Code license	GPLv3
Language	Python
Website	nemo.ozlabs.org
Repository	github.com/bje-/NEMO
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

(covariance matrix adaptation evolution strategy) algorithm. The timestep is arbitrary but one hour is normally employed.

NEMO has been used to explore generation options for the year 2030 under a variety of renewable energy (RE) and abated fossil fuel technology scenarios. A 2012 study investigates the feasibility of a fully renewable system using concentrated solar power (CSP) with thermal storage, windfarms, photovoltaics, existing hydroelectricity, and biofuelled gas turbines. A number of potential systems, which also meet NEM reliability criteria, are identified. The principal challenge is servicing peak demand on winter evenings following overcast days and periods of low wind.^[64] A 2014 study investigates three scenarios using coal-fired thermal generation with carbon capture and storage (CCS) and gas-fired gas turbines with and without capture. These scenarios are compared to the 2012 analysis using fully renewable generation. The study finds that "only under a few, and seemingly unlikely, combinations of costs can any of the fossil fuel scenarios compete economically with 100% renewable electricity in a carbon constrained world".^[66]: 196 A 2016 study evaluates the incremental costs of increasing renewable energy shares under a range of greenhouse gas caps and carbon prices. The study finds that incremental costs increase linearly from zero to 80% RE and then escalate moderately. The study concludes that this cost escalation is not a sufficient reason to avoid renewables targets of 100%.^[65]

OnSSET

Project	OnSSET
Host	KTH Royal Institute of Technology
Status	active
Scope/type	cost-effective electrification
Code license	MIT
Website	www.onsset.org
Mailing list	groups.google.com/g/onsset
Repository	github.com/OnSSET/onsset
Documentation	onsset-manual.readthedocs.io
Datasets	energydata.info

OnSSET is the OpeN Source Spatial Electrification Toolkit. OnSSET is being developed by the division of Energy Systems,

OnSSET can estimate, analyze, and visualize the most cost-effective electrification access options, be they

, are being added.

OnSSET utilizes energy and geographic information, the latter may include settlement size and location, existing and planned transmission and generation infrastructure, economic activity, renewable energy resources, roading networks, and nighttime lighting needs. The

OnSSET has been used for case studies in Afghanistan,^[73] Bolivia,^[72][74] Cameroon,^[75] Ethiopia,^[70][76] Malawi,^[77] Nigeria,^[70][78][79] and Tanzania.^[71] OnSSET has also been applied in India, Kenya, and Zimbabwe. In addition, continental studies have been carried out for Sub-Saharan Africa and Latin America.^[80] A 4‑way GIS‑based study set in Nigeria reported that OnSSET offered the best set of capabilities.^[81]

OnSSET results have contributed to the IEA World Energy Outlook reports for 2014 ^[82] and 2015,^[83] the World Bank Global Tracking Framework report in 2015,^[84] and the IEA Africa Energy Outlook report in 2019.^[85] OnSSET also forms part of the nascent GEP platform.^[86]

pandapower

Project	pandapower
Host	University of Kassel Fraunhofer Institute IEE
Status	active
Scope/type	automated power system analysis
Code license	BSD-new
Website	www.pandapower.org
Repository	github.com/e2nIEE/pandapower
Python package	pypi.org/project/pandapower/
Documentation	pandapower.readthedocs.io
Discussion	forum.openmod.org/tag/pandapower

pandapower is a power system analysis and optimization program being jointly developed by the Energy Management and Power System Operation research group,

.

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

A 2016 publication evaluates the usefulness of the software by undertaking several case studies with major distribution system operators (DSO). These studies examine the integration of increasing levels of photovoltaics into existing distribution grids. The study concludes that being able to test a large number of detailed scenarios is essential for robust grid planning. Notwithstanding, issues of data availability and problem dimensionality will continue to present challenges.^[88]

A 2018 paper describes the package and its design and provides an example case study. The article explains how users work with an element-based model (EBM) which is converted internally to a bus-branch model (BBM) for computation. The package supports power system simulation, optimal power flow calculations (cost information is required), state estimation (should the system characterization lacks fidelity), and graph-based network analysis. The case study shows how a few tens of lines of scripting can interface with pandapower to advance the design of a system subject to diverse operating requirements. The associated code is hosted on GitHub as jupyter notebooks.^[89]

As of 2018^[update], BNetzA, the German network regulator, is using pandapower for automated grid analysis.^[90] Energy research institutes in Germany are also following the development of pandapower.^[91]: 90

PowerMatcher

Project	PowerMatcher
Host	Flexiblepower Alliance Network
Status	active
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 June 2016^[update], 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.^[92]

Power TAC

Project	Power TAC
Host	Erasmus Centre for Future Energy Business at the Rotterdam School of Management, Erasmus University
Status	active
Scope/type	automated retail electricity trading simulation
Code license	Apache 2.0
Website	powertac.org

Power TAC stands for Power Trading Agent Competition. Power TAC is an

competitive gaming platform to simulate electricity retail market performance in smart grid scenarios. The inaugural annual tournament was held in Valencia, Spain in 2012.

Autonomous

, or 'brokers', compete directly with each other as profit-maximizing aggregators between wholesale markets and retail customers. Customer models represent households, small and large businesses, multi-residential buildings, wind parks, solar panel owners, electric vehicle owners, cold-storage warehouses, etc. Brokers aim at making profit through offering electricity tariffs to customers and trading electricity in the wholesale market, while carefully balancing supply and demand.

The competition is founded and orchestrated by Professors Wolfgang Ketter and John Collins and the platform software is developed collaboratively by researchers at the Rotterdam School of Management, Erasmus University Centre for Future Energy Business, the Institute for Energy Economics at the University of Cologne, and the Computer Science department at the University of Minnesota. The platform uses a variety of real-world data about weather, market prices and aggregate demand, and customer behavior. Broker agents are developed by research teams around the world and entered in annual tournaments. Data from those tournaments are publicly available and can be used to assess agent performance and interactions. The platform exploits competitive benchmarking to facilitate research into, among other topics, tariff design in retail electricity markets, bidding strategies in wholesale electricity markets, performance of markets as penetration of sustainable energy resources or electric vehicles is ramped up or down, effectiveness of machine learning approaches, and alternative policy approaches to market regulation. The software has contributed to research topics ranging from the use of electric vehicle fleets as virtual power plants to how an electricity customer decision support system (DSS) can be used to design effective demand response programs using methods such as dynamic pricing.

renpass

Project	renpass
Host	University of Flensburg
Status	inactive
Scope/type	renewables pathways
Code license	GPLv3
Website	github.com/fraukewiese/renpass
Repository	github.com/znes/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.^[93] renpass is also described in a PhD thesis.^[94] As of 2015^[update], renpass is being extended as renpassG!S, based on oemof.

renpass is an electricity dispatch model which minimizes system costs for each time step (optimization) within the limits of a given infrastructure (simulation). Time steps are optionally 15 minutes or one hour. The method assumes perfect foresight. renpass supports the electricity systems found in Austria, Belgium, the Czech Republic, Denmark, Estonia, France, Finland, Germany, Latvia, Lithuania, Luxembourg, the Netherlands, Norway, Poland, Sweden, and Switzerland.

The optimization problem for each time step is to minimize the electricity supply cost using the existing power plant fleet for all regions. After this regional dispatch, the exchange between the regions is carried out and is restricted by the grid capacity. This latter problem is solved with a heuristic procedure rather than calculated deterministically. The input is the merit order, the marginal power plant, the excess energy (renewable energy that could be curtailed), and the excess demand (the demand that cannot be supplied) for each region. The exchange algorithm seeks the least cost for all regions, thus the target function is to minimize the total costs of all regions, given the existing grid infrastructure, storage, and generating capacities. The total cost is defined as the residual load multiplied by the price in each region, summed over all regions.

A 2012 study uses renpass to examine the feasibility of a 100% renewable electricity system for the Baltic Sea region (Denmark, Estonia, Finland, Germany, Latvia, Lithuania, Poland, and Sweden) in the year 2050. The base scenario presumes conservative renewable potentials and grid enhancements, a 20% drop in demand, a moderate uptake of storage options, and the deployment of biomass for flexible generation. The study finds that a 100% renewable electricity system is possible, albeit with occasional imports from abutting countries, and that biomass plays a key role in system stability. The costs for this transition are estimated at 50 €/MWh.^[95] A 2014 study uses renpass to model Germany and its neighbors.^[96] A 2014 thesis uses renpass to examine the benefits of both a new cable between Germany and Norway and new pumped storage capacity in Norway, given 100% renewable electricity systems in both countries.^[97] 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.^[98]

SciGRID

Project	SciGRID
Host	Deutsches Zentrum für Luft- und Raumfahrt
Status	active
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

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. 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.^[99][100]

SIREN

Project	SIREN
Host	Sustainable Energy Now
Status	active
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

A 2016 study using SIREN to analyze Western Australia's South-West Interconnected System (SWIS) finds that it can transition to 85% renewable energy (RE) for the same cost as new coal and gas. In addition, 11.1 million tonnes of CO₂eq emissions would be avoided. The modeling assumes a carbon price of AUD $30/tCO₂. Further scenarios examine the goal of 100% renewable generation.^[102]

SWITCH

Project	SWITCH
Host	University of Hawaiʻi
Status	active
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 by the Department of Electrical Engineering,

solver.

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

facility, or transfer along each transmission interconnector. The system must also ensure enough generation and transmission capacity to provide a planning reserve margin of 15% above the load forecasts. For each sampled hour, SWITCH uses electricity demand and renewable power production based on actual measurements, so that the weather-driven correlations between these elements remain intact.

Following the optimization phase, SWITCH is used in a second phase to test the proposed investment plan against a more complete set of weather conditions and to add backstop generation capacity so that the planning reserve margin is always met. Finally, in a third phase, the costs are calculated by freezing the investment plan and operating the proposed power system over a full set of weather conditions.

A 2012 paper uses California from 2012 to 2027 as a case study for SWITCH. The study finds that there is no ceiling on the amount of wind and solar power that could be used and that these resources could potentially reduce emissions by 90% or more (relative to 1990 levels) without reducing reliability or severely raising costs. Furthermore, policies that encourage electricity customers to shift demand to times when renewable power is most abundant (for example, though the well-timed charging of electric vehicles) could achieve radical emission reductions at moderate cost.^[103]

SWITCH was used more recently to underpin consensus-based power system planning in Hawaii.^[104] The model is also being applied in Chile, Mexico, and elsewhere.^[105]

Major version 2.0 was released in late‑2018.^[105] An investigation that year favorably compared SWITCH with the proprietary General Electric MAPS model using Hawaii as a case study.^[106]

URBS

Project	URBS
Host	Technical University of Munich
Status	active
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 model-defined. The decision variables are the capacities for the production, storage, and transport of electricity and the time scheduling for their operation.^{[107]: 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.^[108] The software has also been used to explore energy systems spanning Europe, the Middle East, and North Africa (EUMENA)^[107] and Indonesia, Malaysia, and Singapore.^[109]

Energy system models

Open energy system models capture some or all of the energy commodities found in an energy system. Typically models of the electricity sector are always included. Some models add the heat sector, which can be important for countries with significant district heating. Other models add gas networks. With the advent of emobility, other models still include aspects of the transport sector. Indeed, coupling these various sectors using power-to-X technologies is an emerging area of research.^[61]

Open energy system models (bottom-up, with support for heat, gas, and such, in addition to electricity)

Project	Host	License	Access	Coding	Documentation	Scope/type
AnyMOD.jl	TU Berlin	MIT	GitHub	Julia	website	system planning framework
Backbone	VTT	LGPLv3	GitLab	GAMS	website	framework - dispatch, investment, all sectors, LP/MILP
Balmorel	Denmark	ISC	registration	GAMS	manual	energy markets
Calliope	ETH Zurich	Apache 2.0	download	Python	manual, website, list	dispatch and investment
DESSTinEE	Imperial College London	CC BY-SA 3.0	download	Excel/VBA	website	simulation
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, Switzerland	Eclipse 1.0	registration	MathProg	manual	municipal
ficus	Technical University of Munich	GPLv3	GitHub	Python	manual	local electricity and heat
GenX	MIT and Princeton University	GPLv2	GitHub	Julia	website	multi‑commodity sector investment planning
oemof	oemof community supported by Reiner Lemoine Institute University of Flensburg Flensburg University of Applied Sciences	MIT	GitHub	Python	website	framework - dispatch, investment, all sectors, LP/MILP
OSeMOSYS	OSeMOSYS community	Apache 2.0	GitHub	GAMS MathProg Python	website, forum	planning at all scales
PyPSA	Goethe University Frankfurt	MIT	GitHub	Python	website	electric power systems with sector coupling
TEMOA	North Carolina State University	GPLv2+	GitHub	Python	website, forum	system planning
Access refers to the methods offered for accessing the codebase.

AnyMOD.jl

Project	AnyMOD.jl
Host	TU Berlin
Status	active
Scope/type	energy system planning
Code license	MIT
Language	Julia
Website	github.com/leonardgoeke/AnyMOD.jl
Documentation	leonardgoeke.github.io/AnyMOD.jl/stable/
Publications	www.researchgate.net/project/AnyMODjl-Methods-and-applications

AnyMOD.jl is a framework for planning macro‑energy systems at a high level of spatio-temporal detail. The framework covers the expansion and operation of short-term and seasonal storage, fossil and renewable generation, transmission infrastructure, and sector coupling technologies. It can be used to plan long‑term pathways under perfect foresight.

AnyMOD.jl is implemented in Julia and relies on the JuMP library for optimization and DataFrames.jl for data management. Models are formulated as linear optimization problems and can be solved with open-source libraries like HiGHS or commercial solvers like CPLEX. To increase accessibility and enable version-controlled development, specific models are fully defined using CSV files.

Compared to similar tools, AnyMOD.jl puts an emphasis on innovative methods to achieve high detail and capture intermittent renewables, while maintaining a comprehensive scope in terms of regions and sectors. These methods include varying the spatio-temporal resolution by energy carrier within the same model and a scaling algorithm to improve the properties of the underlying optimization problem.^[112][111] Methods from stochastic programming are now being implemented to better address the uncertainties associated with renewable generation.^[113]

As of 2022,^[update] most studies deploying the tool have focused on the German energy system in a European context, for instance investigating the trade‑offs between centralized and decentralized designs, the role of grid planning, and the potential of sufficiency measures.^{[114][115][116]} 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.^[110][117]

Backbone

Project	Backbone
Host	VTT
Status	active
Scope/type	framework - dispatch, investment, all sectors, LP/MILP
Code license	LGPLv3
Language	GAMS
Website	gitlab.vtt.fi/backbone/backbone/-/wikis/home
Repository	gitlab.vtt.fi/backbone/backbone
Documentation	gitlab.vtt.fi/backbone/backbone/-/wikis/home

Backbone is an energy system modeling framework that allows for a high level of detail and adaptability. It has been used to study city-level energy systems as well as multi-country energy systems. It was originally developed during 2015–2018 in an Academy of Finland‑funded project 'VaGe' by the Design and Operation of Energy Systems team at VTT. It has been further developed in a collaboration which includes VTT, UCD, and RUB.

The framework is agnostic about what is modeled, but still has capabilities to represent a large range of energy system characteristics — such as generation and transfer, reserves, unit commitment, heat diffusion in buildings, storages, multiple emissions and P2X, etc. It offers linear and mixed integer constraints for capturing things like unit start-ups and investment decisions. It allows the modeler to change the temporal resolution of the model between time steps. — and this enables, for example, to use a coarser time resolution further ahead in the time horizon of the model. The model can be solved as an investment model (single or multi-period, myopic, or full foresight) or as a rolling production cost unit commitment model to simulate operations.^[118]

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 ^[119] and District heating and cooling model for the Finnish capital region. ^[120]

Balmorel

Project	Balmorel
Host	stand-alone from Denmark
Status	active
Scope/type	energy markets
Code license	ISC
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 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.^[125] These ambitions and limitations have long since been superseded and Balmorel is no longer tied to its original geography and policy questions.^[123] Balmorel classes as a dispatch and investment model and uses a time resolution of one hour. It models electricity and heat supply and demand, and supports the intertemporal storage of both. Balmorel is structured as a pure linear program (no integer variables).

As of 2016^[update], Balmorel has been the subject of some 22 publications. A 2008 study uses Balmorel to explore the Nordic energy system in 2050. The focus is on renewable energy supply and the deployment of hydrogen as the main transport fuel. Given certain assumptions about the future price of oil and carbon and the uptake of hydrogen, the model shows that it is economically optimal to cover, using renewable energy, more than 95% of the primary energy consumption for electricity and district heat and 65% of the transport.^[126] 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.^[127] 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.^[128]

Calliope

Project	Calliope
Host	TU Delft
Status	active
Scope/type	dispatch and investment
Code license	Apache 2.0
Language	Python
Website	www.callio.pe
Repository	github.com/calliope-project/calliope
Documentation	calliope.readthedocs.io

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,

And a two‑page software review is available.[130]

A Calliope model consists of a collection of structured text files, in

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.^[131] 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.^[132]

Calliope co‑developer Stefan Pfenninger discusses the role that energy system models can play in supporting real‑world decisions at a seminar held in mid‑2021.^[133] One study cited investigates the consequences of pursuing energy self‑sufficiency by duly adding increasingly restrictive internal constraints.^[134] Another at near optimal solutions for Italy.^[135] A 2023 video describes recent developments, many of which are designed to benefit users.^[136]

DESSTinEE

Project	DESSTinEE
Host	Imperial College London
Status	active
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.^[137]

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.^[138]

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.^[139]

Energy Transition Model

Project	Energy Transition Model
Host	Quintel Intelligence
Status	active
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 (

) and carrier type (such as coal, electricity, usable-heat, and so forth). Given a demand and other choices, ETM calculates the primary energy use, the total cost, and the resulting CO₂ emissions. The model is demand driven, meaning that the digraph is traversed from useful demand (such as space heating, hot water usage, and car-kilometers) to primary demand (the extraction of gas, the import of coal, and so forth).

EnergyPATHWAYS

Project	EnergyPATHWAYS
Host	Evolved Energy Research
Status	active
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,

.

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.^[140] And the US PATHWAYS model contributed to the United Nations Deep Decarbonization Pathways Project (DDPP) assessments for the United States.^[141] As of 2016^[update], the DDPP plans to employ EnergyPATHWAYS for future analysis.

ETEM

Project	ETEM
Host	ORDECSYS Archived 9 November 2016 at the Wayback Machine
Status	active
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

ETEM is a bottom-up model that identifies the optimal energy and technology options for a regional or city. The model finds an energy policy with minimal cost, while investing in new equipment (new technologies), developing production capacity (installed technologies), and/or proposing the feasible import/export of primary energy. ETEM typically casts forward 50 years, in two or five year steps, with time slices of four seasons using typically individual days or finer. The spatial resolution can be highly detailed. Electricity and heat are both supported, as are district heating networks, household energy systems, and grid storage, including the use of plug-in hybrid electric vehicles (PHEV). ETEM-SG, a development, supports demand response, an option which would be enabled by the development of smart grids.

The ETEM model has been applied to Luxembourg, the Geneva and Basel-Bern-Zurich cantons in Switzerland, and the Grenoble metropolitan and Midi-Pyrénées region in France. A 2005 study uses ETEM to study climate protection in the Swiss housing sector. The ETEM model was coupled with the GEMINI-E3 world computable general equilibrium model (CGEM) to complete the analysis.^[145] A 2012 study examines the design of smart grids. As distribution systems become more intelligent, so must the models needed to analysis them. ETEM is used to assess the potential of smart grid technologies using a case study, roughly calibrated on the Geneva canton, under three scenarios. These scenarios apply different constraints on CO₂ emissions and electricity imports. A stochastic approach is used to deal with the uncertainty in future electricity prices and the uptake of electric vehicles.^[146]

ficus

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

ficus is a

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.

GenX

Project	GenX
Host	MIT and Princeton University
Status	active
Scope/type	multi‑commodity sector investment planning
Code license	GPLv2
Website	genx.mit.edu
Repository	github.com/GenXProject/GenX
Documentation	genxproject.github.io/GenX/dev/

GenX is multi‑commodity sector capacity expansion model originally developed by researchers in the United States.^[148][149] The framework is written in Julia and deploys the JuMP library for building the underlying optimization problem.^[150][151] GenX through JuMP can utilize various open source (including CBC/CLP) and commercial optimization solvers (including CPLEX). In June 2021, the project launched as an active open source project and test suites are available to assist onboarding.^[152]

In parallel, the PowerGenome project is designed to provide GenX with a comprehensive current state dataset of the United States electricity system.^[153] 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,^[154][155] to explore the value of 'firm' low-carbon power generation options,^[156] and a variety of other applications. While North America remains a key focus, the software has been applied to problems in India,^[157] Italy,^[158] and Spain.^[159]

GenX was deployed in a 2021 case study with

A mid‑2022 study examined the

]

oemof

Project	oemof
Host	oemof e.V. nonprofit with community support from Reiner Lemoine Institute University of Flensburg Flensburg University of Applied Sciences
Status	active
Scope/type	electricity, heat, mobility, gas
Code license	MIT
Language	Python
Website	oemof.org
Repository	github.com/oemof/
Documentation	oemof.readthedocs.io
Discussion	forum.openmod.org/tag/oemof

oemof stands for Open Energy Modelling Framework. The project is managed by the Reiner Lemoine Institute,

) which should simplify data preparation. Version 0.1.0 was released on 1 December 2016.

oemof classes as an energy modeling framework. It consists of a

mixed integer

optimization problem formulation library (solph), an input data generation library (feedin-data), and other auxiliary libraries. The solph library is used to represent multi-regional and multi-sectoral (electricity, heat, gas, mobility) systems and can optimize for different targets, such as financial cost or CO₂ emissions. Furthermore, it is possible to switch between dispatch and investment modes. In terms of scope, oemof can capture the European power system or alternatively it can describe a complex local power and heat sector scheme.

oemof has been applied in sub‑Saharan Africa.

OSeMOSYS

Project	OSeMOSYS
Host	community project
Status	active
Scope/type	planning at all scales
Code license	Apache 2.0
Language	various
Website	www.osemosys.org
Forum	groups.google.com/g/osemosys
Repository	github.com/OSeMOSYS/OSeMOSYS
Discussion	forum.openmod.org/tag/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,

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

A key paper describing OSeMOSYS is available.^[6] A 2011 study uses OSeMOSYS to investigate the role of household investment decisions.^[168] A 2012 study extends OSeMOSYS to capture the salient features of a smart grid. The paper explains how to model variability in generation, flexible demand, and grid storage and how these impact on the stability of the grid.^[169] 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.^[170] In a 2016 study, OSeMOSYS is modified to take into account realistic consumer behavior.^[171] Another 2016 study uses OSeMOSYS to build a local multi-regional energy system model of the Lombardy region in Italy. One of the aims of the exercise was to encourage citizens to participate in the energy planning process. Preliminary results indicate that this was successful and that open modeling is needed to properly include both the technological dynamics and the non-technological issues.^[172] 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.^[173] 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.^[174]

OSeMOSYS has been used to construct wide-area electricity models for

In 2016, work started on a

The OSeMBE reference model covering western and central Europe was announced on 27 April 2018.

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.[184]

OSeMOSYS Global project

The OSeMOSYS community launched the OSeMOSYS Global project in 2022 to create a global model and associated workflows. As of late‑2022, OSeMOSYS Global is limited in scope to the electricity sector and the world system provided comprises 164 countries separated by 265 nodes.^[199]

PyPSA

Project	PyPSA
Host	Technical University of Berlin
Status	active
Scope/type	electric power systems with sector coupling
Code license	MIT
Language	Python
Code website	pypsa.org
Project website	pypsa-meets-earth.github.io
Repository	github.com/PyPSA/PyPSA
Documentation	pypsa.readthedocs.io
Python package	pypi.org/project/pypsa
Mailing list	groups.google.com/g/pypsa
Chat	discord.gg/AnuJBk23FU
Discussion	forum.openmod.org/tag/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.

package.

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,

A 2018 study examines potential synergies between

As of January 2018[update], PyPSA is used by more than a dozen research institutes and companies worldwide.^[205]: 2 Some research groups have independently extended the software, for instance to model integer transmission expansion.^[207]

In 2020, the PyPSA‑Eur‑Sec model for Europe was used to analyze several Paris Agreement Compatible Scenarios for Energy Infrastructure ^[208] and determined that early action should pay off.^[209]

On 9 January 2019, the project released an interactive web-interfaced "toy" model, using the Cbc solver, to allow the public to experiment with different future costs and technologies.^[210][211] The site was relaunched on 5 November 2019 with some internal improvements, a new URL, and faster solver now completing in about 12 s.^[212] A newer version now uses the HiGHS solver.^[213]

During September 2021, PyPSA developers announced the PyPSA‑Server project to provide a web interface to a simplified version of their PyPSA‑Eur‑Sec sector‑coupled European model.^[215][216] Users need not install software and can define fresh scenarios "by difference" using a forms‑based webpage. Previously run scenarios are stored for future reference. The implementation as of October 2021^[update] is essentially proof‑of‑concept.

In late‑2021, PyPSA developers reported their investigation into integrated high-voltage electricity and hydrogen grid expansion options for Europe and the United Kingdom and the impact of the kind of trade‑offs that might stem from limited public acceptance of new infrastructure.^[217][218] Subsequent work added endogenous learning effects and identified steeper technology cost reductions than those anticipated by the European Commission.^[219]

A December 2021 study and ongoing work deployed a PyPSA‑PL model to assess policy options for Poland.^{[220][221][222][223]}

Several PyPSA maintainers announced a new non‑profit startup in June 2023 to provide consulting services using PyPSA.^[224]

PyPSA meets Earth initiative

The PyPSA meets Earth initiative arose in October 2022 as a means of gathering together several historically disjoint PyPSA applications.^[225] One key strand is the PyPSA‑Africa project (previously PyPSA-meets-Africa), launched some months earlier to provide a single model and dataset spanning the African continent.^[226][227] A July 2022 webinar co‑hosted by CPEEL, Nigeria advanced this agenda.^[228][229] The first research paper, released in 2022, examines various pathways for Africa to be net zero by 2060 — with solar power and battery storage expected to be the predominant technologies.^[214]

Another key strand of the initiative is the PyPSA‑Earth project which seeks to create a global energy systems model at high spatial and temporal resolution.^[225] The project hopes to encourage large‑scale collaboration by providing software and processes that can capture the global energy system and thus also any subset of it. The codebase currently supports system integration studies that draw together electricity generation, storage, and transmission expansion. And a sector-coupled version of the framework is in development that will also offer a choice between myopic decision‑taking or perfect foresight.^[214]

TEMOA

Project	TEMOA
Host	North Carolina State University
Key people	Joseph DeCarolis
Status	active
Scope/type	system planning
Code license	GPLv2+
Language	Python
Website	temoacloud.com
Repository	github.com/TemoaProject/temoa/
Mailing list	groups.google.com/g/temoa-project

TEMOA stands for Tools for Energy Model Optimization and Analysis. The software is being developed by the Department of Civil, Construction, and Environmental Engineering,

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.^[230] TEMOA is "strongly influenced by the well-documented MARKAL/TIMES model generators".^[231]: 4

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.^[15][232]

From mid‑2021, an interactive interface located on the main website allows registered users to manipulate scenario data locally, upload structured SQLite files, and then run these scenarios using the TEMOA software.^[233][234] The service also provides some limited data visualization and project management functionality.

Specialist models

This section lists specialist modeling frameworks that cover particular aspects of an energy system in more detail than would normally be convenient or feasible with more general frameworks.

VencoPy

Project	VencoPy
Host	German Aerospace Center
Status	active
Scope/type	electric vehicle / system interactions
Code license	BSD-new
Language	Python
Website	—
Mailing list	groups.google.com/g/openmod-trans
Repository	gitlab.com/dlr-ve/vencopy
Documentation	vencopy.readthedocs.io
Discussion	forum.openmod.org/tag/vencopy

The VencoPy model framework can be used to investigate interactions between the uptake of battery electric vehicles (BEV) and the electricity system at large. More specifically, BEVs can usefully contribute to short‑haul storage in power systems facing high shares of fluctuating renewable energy. But unlike dedicated grid storage, BEV contributions are highly dependent on the connection and charging choices that individual vehicle owners might make.^[235]

VencoPy has been applied to various scenarios in Germany in 2030 using a projected 9 million BEVs in service and an annual fleet power consumption of 27 TWh. Simulations show that owner decisions are indeed significant and that some system design variables have more influence than others. For instance, aggregate fleet capacity and the availability of fast charging facilities appear to strongly impact the likely system contribution. Further work is needed to assess the influence of more resolved weather and demand patterns.^[235] The mathematical formulation is available.^[236] VencoPy builds on an earlier spreadsheet prototype.^[237]

Project statistics

Statistics for the 29 open energy modeling projects listed (given sufficient information is available) are as follows:

Core programming language Paradigm Language Count Imperative programming R 1 Object-oriented programming  C++ 1 Java 2 Python 14 Ruby 1 Multiple dispatch Julia 2 Mathematical programming GAMS 6 MathProg 2 Spreadsheet Excel/VBA 1   indicates a compiled language.   indicates a commercial software license is required.

Primary origin Country Count  Australia 2  Denmark 1  European Union 1  Finland 1  Germany 13  Netherlands 4  Sweden [f] 2   Switzerland 2  United Kingdom  1  United States 3

Project host Type Count Academic institution 20 Commercial entity 5 Community-based 1 Non-profit entity 2 State-sponsored 1

The

Programming components

Programming components, in this context, are coherent blocks of code or compiled libraries that can be relatively easily imported or linked to by higher‑level modeling frameworks in order to obtain some well‑defined functionality.

Technology modules

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

• Sandia photovoltaic array performance model^[239]
• pvlib photovoltaics facility library^[240]
• hplib heat pump facility library^[241]
• windpowerlib wind turbine library ^[242]
• hydropowerlib hydroelectricity library ^[243]

Auction models

A number of electricity auction models have been written in

These include:

• the EPOC
  nodal pricing model^[244]

• Australian
  MathProg can be found at wikibooks:GLPK/Electricity markets

Open solvers

Many projects rely on a

Proprietary solvers outperform open source solvers by a considerable margin (perhaps ten-fold), so choosing an open solver will limit performance in terms of speed, memory consumption, and perhaps even tractability.[248]

The flexible SMS++ optimization toolbox, written in C++17, is being developed specifically to meet the needs of energy system modeling.^[249]

See also

General

• Building energy simulation
  – the modeling of energy flows in buildings
• Climate change mitigation scenarios
• Energy modeling – the process of building computer models of energy systems
• Energy system – the interpretation of the energy sector in system terms
• Open Energy Modelling Initiative – a European-based energy modeling community
• Open energy system databases – database projects which collect, clean, and republish energy-related datasets
• Unit commitment problem in electrical power production

Software

• List of free and open-source optimization solvers
• Cbc (COIN-OR Branch and Cut) – an open source optimization solver
• Clp (COIN-OR LP) – an open source linear optimization solver
• Community Climate System Model – a mostly open source coupled global climate model
• ESMF (Earth System Modeling Framework) – open source software for building climate, numerical weather prediction, and data assimilation applications
• GHGProof – an open source land-use model
• GLPK
  (GNU Linear Programming Kit) – an open source linear and mixed integer optimization solver
• GridLAB-D – an open source simulation and analysis tool for smart grid energy technologies
• GridSpice – an open source cloud-based simulation package for modelling smart grids
• HiGHS – an open source optimization solver

People

• Joe DeCarolis – energy system modeler and current head of the United States Energy Information Administration

Notes

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Further information

The following lists and databases cover energy system models to varying degrees of completeness and usually with a focus on open source:

• Open energy models wiki maintained by the Open Energy Modelling Initiative
• Open Energy Platform factsheets — structured summaries covering a range of open and closed energy system models
• Global Power System Transformation Consortium database — filterable database of open models and related projects

External links

Modeling efforts by region

• Africa: reports and publications — broken down by region and country
• Latin America: reports and publications — broken down by region and country
• Oceania: reports and publications — broken down by region and country