Open energy system models

From Wikipedia, the free encyclopedia
Jump to navigation Jump to search

Open energy system models are energy system models that are open source.[a] However, some of them may use third party proprietary software as part of their workflows to input, process, or output data. Preferably, these models use open data, which facilitates open science.

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

General considerations[edit]

Organization[edit]

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 public policy energy models more transparent to improve their acceptance by policymakers and the public.[1] There is also a desire to leverage the benefits that open data and open software development can bring, including reduced duplication of effort, better sharing of ideas and information, improved quality, and wider engagement and adoption.[2] Model development is therefore usually a team effort and constituted as either an academic project, a commercial venture, or a genuinely inclusive community initiative.

This article does not cover projects which simply make their source code or spreadsheets available for public download, but which omit a recognized free and open-source software license. The absence of a license agreement creates a state of legal uncertainty whereby potential users cannot know which limitations the owner may want to enforce in the future.[3]: 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.[4]: 211–213  The paper makes a number of recommendations for projects wishing to transition to a more open approach.[4]: 214  The authors also conclude that, in terms of openness, energy research has lagged behind other fields, most notably physics, biotechnology, and medicine.[4]: 213–214 

Growth[edit]

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.[5]: 5861  Balmorel was also active at that time, having been made public in 2001.[b] As of July 2022, 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.[6] A 2022 model comparison exercise in Germany reported eight from 40 modeling projects (20%) were open source,[7] these projects also had active communities behind them.[8]

Transparency, comprehensibility, and reproducibility[edit]

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.[1]

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".[9]: 17 [10] To further honor the process of peer review, researchers argue, in a 2012 paper, that it is essential to place both the source code and datasets under publicly accessible version control so that third-parties can run, verify, and scrutinize specific models.[11] A 2016 paper contends that model-based energy scenario studies, seeking to influence decision-makers in government and industry, must become more comprehensible and more transparent. To these ends, the paper provides a checklist of transparency criteria that should be completed by modelers. The authors however state that they "consider open source approaches to be an extreme case of transparency that does not automatically facilitate the comprehensibility of studies for policy advice."[12]: 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.[13] 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.[14]

State projects[edit]

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

As of 2017, 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.[15]: 6 [16] As of February 2017, 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.[17] The flagship JRC-EU-TIMES model however remains closed source.[18]

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.[13]

A 2021 research call from the European Union Horizon Europe scientific research funding program expressly sought energy system models that are open source.[19]

Surveys[edit]

A survey completed in 2021 investigated the degree to which open energy system modeling frameworks support flexibility options, broken down by supply, demand, storage, sector coupled, and network response. Of the frameworks surveyed, none supported all types, which suggests that the soft coupling of complementary frameworks could provide more holistic assessments of flexibility. Even so, most candidates opt for perfect foresight and do not natively admit probabilistic actions or explicit behavioral responses.[20]

Electricity sector models[edit]

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 BSD-new GitHub Python website automated power system analysis
PowerMatcher Flexiblepower Alliance Network Apache 2.0 GitHub Java website smart grid
Power TAC
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 Hawai'i 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[edit]

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. 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.[21][22]

AMIRIS enables researchers to address questions regarding future energy markets, their market design, and energy-related policy instruments.[23] 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.[24] AMIRIS may also uncover complex effects that may emerge from the inter‑dependencies of the energy market participants.[25]

The figure provides an overview of the agents modeled in AMIRIS and illustrates the associated information, energy, and financial flows.
AMIRIS architecture

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.[26] AMIRIS is based on the open Framework for distributed Agent-based Modelling of Energy systems or FAME.[27] 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[edit]

Project Breakthrough Energy Model
Host Breakthrough Energy Foundation
Status active
Scope/type power sector modeling
Code license MIT
Data license MIT
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-source data underlying the model builds upon the synthetic test cases developed by researchers at Texas A&M University.[28][29][30]

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.[31] 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.

DIETER[edit]

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 power storage and other flexibility options in a future greenfield setting with high shares of renewable generation. DIETER is being developed at the German Institute for Economic Research (DIW), Berlin, Germany. The codebase and datasets for Germany can be downloaded from the project website. The basic model is fully described in a DIW working paper and a journal article.[32][33] DIETER is written in GAMS and was developed using the CPLEX 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.[34][35]

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.[36]

Dispa-SET[edit]

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 European Commission's Joint Research Centre (JRC), Petten, the Netherlands, Dispa-SET is a unit commitment and dispatch model intended primarily for Europe. It is written in Python (with Pyomo) and GAMS and uses Python for data processing. A valid GAMS license is required. The model is formulated as a mixed integer problem and JRC uses the proprietary CPLEX sover although open source libraries may also be deployed. Technical descriptions are available for versions 2.0 [37] and 2.1.[38] Dispa-SET is hosted on GitHub, together with a trial dataset, and third-party contributions are encouraged. The codebase has been tested on Windows, macOS, and Linux. Online documentation is available.[39]

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.[40]

48 hour rolling horizon optimization for any given 24 hour day

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 inelastic. The system is subject to intra-period and inter-period unit commitment constraints (the latter covering nuclear and thermal generation for the most part) and operated under economic dispatch.[38]: 4  Hourly data is used and the simulation horizon is normally one year. But to ensure the model remains tractable, two day rolling horizon optimization is employed. The model advances in steps of one day, optimizing the next 48 hours ahead but retaining results for just the first 24 hours.[38]: 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).[41][42] 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.[38] 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.[39]

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.[44]

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.[45]

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.[46]

EMLab-Generation[edit]

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.[47] And software documentation is available.[48] 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.[49] The agent-based methodology enables different sets of assumptions to be tested, such as the heterogeneity of actors, the consequences of imperfect expectations, and the behavior of investors outside of ideal conditions.

EMLab-Generation offers a new way of modeling the effects of public policy on electricity markets. It can provide insights into actor and system behaviors over time – including such things as investment cycles, abatement cycles, delayed responses, and the effects of uncertainty and risk on investment decisions.

A 2014 study using EMLab-Generation investigates the effects of introducing floor and ceiling prices for CO2 under the EU ETS. And in particular, their influence on the dynamic investment pathway of two interlinked electricity markets (loosely Great Britain and Central Western Europe). The study finds a common, moderate CO2 auction reserve price results in a more continuous decarbonisation pathway and reduces CO2 price volatility. Adding a ceiling price can shield consumers from extreme price shocks. Such price restrictions should not lead to an overshoot of emissions targets in the long-run.[50]

EMMA[edit]

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, Berlin, Germany. The source code and datasets can be downloaded from the project website. A manual is available.[51] EMMA is written in GAMS and uses the CPLEX commercial solver.

EMMA models electricity dispatch and investment, minimizing the total cost with respect to investment, generation, and trades between market areas. In 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, the model covers Belgium, France, Germany, the Netherlands, and Poland and supports conventional generation, renewable generation, and cogeneration.[51][52]

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.[53] 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.[54]

An independent 2015 study reviews the EMMA model and comments on the high assumed specific costs for renewable investments.[32]: 6 

GENESYS[edit]

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

GENESYS stands for Genetic Optimisation of a European Energy Supply System. The software is being developed jointly by the Institute of Power Systems and Power Economics (IAEW) and the Institute for Power Electronics and Electrical Drives (ISEA), both of RWTH Aachen University, Aachen, Germany. The project maintains a website where potential users can request access to the codebase and the dataset for the 2050 base scenario only.[55] Detailed descriptions of the software are available.[56][57] GENESYS is written in C++ and uses Boost libraries, the MySQL relational database, the Qt 4 application framework, and optionally the CPLEX solver.

The GENESYS simulation tool is designed to optimize a future EUMENA (Europe, Middle East, and North Africa) power system and assumes a high share of renewable generation. It is able to find an economically optimal distribution of generator, storage, and transmission capacities within a 21 region EUMENA. It allows for the optimization of this energy system in combination with an evolutionary method. The optimization is based on a covariance matrix adaptation evolution strategy (CMA-ES), while the operation is simulated as a hierarchical set-up of system elements which balance the load between the various regions at 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.[56]

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.[57]

NEMO[edit]

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

NEMO, the National Electricity Market Optimiser, is a chronological dispatch model for testing and optimizing different portfolios of conventional and renewable electricity generation technologies. It applies solely to the Australian National Electricity Market (NEM), which, despite its name, is limited to east and south Australia. NEMO has been in development at the Centre for Energy and Environmental Markets (CEEM), University of New South Wales (UNSW), Sydney, Australia since 2011.[58] The project maintains a small website and runs an email list. NEMO is written in Python. NEMO itself is described in two publications.[59]: sec 2 [60]: sec 2  The data sources are also noted.[59]: sec 3  Optimizations are carried out using a single-objective evaluation function, with penalties. The solution space of generator capacities is searched using the CMA-ES (covariance matrix adaptation evolution strategy) algorithm. 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.[59] 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".[61]: 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%.[60]

OnSSET[edit]

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, KTH Royal Institute of Technology, Stockholm, Sweden. The software is used to examine areas not served by grid-based electricity and identify the technology options and investment requirements that will provide least-cost access to electricity services. OnSSET is designed to support the United Nations' SDG 7: the provision of affordable, reliable, sustainable, and modern energy for all. The toolkit is known as OnSSET and was released on 26 November 2016. OnSSET does not ship with data, but suitable datasets are available from energydata.info. The project maintains a website and runs a mailing list.[62][63][64]

A least-cost electrification mapping for Tanzania

OnSSET can estimate, analyze, and visualize the most cost-effective electrification access options, be they conventional grid, mini-grid, or stand-alone.[65] The toolkit supports a range of conventional and renewable energy technologies, including photovoltaics, wind turbines, and small hydro generation. As of 2017, bioenergy and hybrid technologies, such as wind-diesel, 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 GIS information can be supported using the proprietary ArcGIS package or an open source equivalent such as GRASS or QGIS.[66] OnSSET has been applied to microgrids using a three‑tier analysis starting with settlement archetypes.[67]

OnSSET has been used for case studies in Afghanistan,[68] Bolivia,[67][69] Ethiopia,[65][70] Malawi,[71] Nigeria,[65][72] and Tanzania.[66] 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.[73] A 4‑way GIS‑based study set in Nigeria reported that OnSSET offered the best set of capabilities.[74]

OnSSET results have contributed to the IEA World Energy Outlook reports for 2014 [75] and 2015,[76] the World Bank Global Tracking Framework report in 2015,[77] and the IEA Africa Energy Outlook report in 2019.[78] OnSSET also forms part of the nascent GEP platform.[79]

pandapower[edit]

Project pandapower
Host
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, University of Kassel and the Department for Distribution System Operation, Fraunhofer Institute for Energy Economics and Energy System Technology (IEE), both of Kassel, Germany. The codebase is hosted on GitHub and is also available as a package. The project maintains a website, an emailing list, and online documentation. pandapower is written in Python. It uses the pandas library for data manipulation and analysis and the PYPOWER library [80] to solve for power flow. Unlike some open source power system tools, pandapower does not depend on proprietary platforms like MATLAB.

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.[81]

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.[82]

As of 2018, BNetzA, the German network regulator, is using pandapower for automated grid analysis.[83] Energy research institutes in Germany are also following the development of pandapower.[84]: 90 

PowerMatcher[edit]

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, 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.[85]

Power TAC[edit]

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 agent-based model simulating the performance of retail markets in an increasingly prosumer- and renewable-energy-influenced electricity landscape. The first version of the Power TAC project started in 2009, when the open source platform was released as an open-source multi-agent 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 machine-learning trading agents, 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[edit]

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.[86] renpass is also described in a PhD thesis.[87] As of 2015, 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.[88] A 2014 study uses renpass to model Germany and its neighbors.[89] 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.[90] 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.[91]

SciGRID[edit]

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 electricity transmission networks. The research project is managed by DLR Institute of Networked Energy Systems located in Oldenburg, Germany. The project maintains a website and an email newsletter. SciGRID is written in Python and uses a PostgreSQL database. The first release (v0.1) was made on 15 June 2015.

SciGRID aims to rectify the lack of open research data on the structure of electricity transmission networks within Europe. This lack of data frustrates attempts to build, characterise, and compare high resolution energy system models. SciGRID utilizes transmission network data available from the OpenStreetMap project, available under the Open Database License (ODbL), to automatically author transmission connections. 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.[92][93]

SIREN[edit]

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 NGO based in Perth, Australia. The project maintains a website. SIREN runs on Windows and the source code is hosted on SourceForge. The software is written in Python and uses the SAM model (System Advisor Model) from the US National Renewable Energy Laboratory to perform energy calculations. SIREN uses hourly datasets to model a given geographic region. Users can use the software to explore the location and scale of renewable energy sources to meet a specified electricity demand. SIREN utilizes a number of open or publicly available data sources: maps can be created from OpenStreetMap tiles and weather datasets can be created using NASA MERRA-2 satellite data.[d][94]

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.[95]

SWITCH[edit]

Project SWITCH
Host University of Hawai'i
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, University of Hawai'i, Mānoa, Hawaii, USA. The project runs a small website and hosts its codebase and datasets on GitHub. SWITCH is written in Pyomo, an optimization components library programmed in Python. It can use either the open source GLPK solver or the commercial CPLEX 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 stochastic linear optimization with the objective of minimizing the present value of the cost of power plants, transmission capacity, fuel usage, and an arbitrary per-tonne CO2 charge (to represent either a carbon tax or a certificate price), over the course of a multi-year investment period. It has two major sets of decision variables. First, at the start of each investment period, SWITCH selects how much generation capacity to build in each of several geographic load zones, how much power transfer capability to add between these zones, and whether to operate existing generation capacity during the investment period or to temporarily mothball it to avoid fixed operation and maintenance costs. Second, for a set of sample days within each investment period, SWITCH makes hourly decisions about how much power to generate from each dispatchable power plant, store at each pumped hydro facility, or transfer along each transmission interconnector. The system must also ensure enough generation and transmission capacity to provide a planning reserve margin of 15% 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.[96]

SWITCH was used more recently to underpin consensus-based power system planning in Hawaii.[97] The model is also being applied in Chile, Mexico, and elsewhere.[98]

Major version 2.0 was released in late‑2018.[98] An investigation that year favorably compared SWITCH with the proprietary General Electric MAPS model using Hawaii as a case study.[99]

URBS[edit]

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.[100]: 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.[101] The software has also been used to explore energy systems spanning Europe, the Middle East, and North Africa (EUMENA)[100] and Indonesia, Malaysia, and Singapore.[102]

Energy system models[edit]

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.[56]

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, UCD 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 MIT GitHub Python website framework - dispatch, investment, all sectors, LP/MILP
OSeMOSYS OSeMOSYS community Apache 2.0 GitHub 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[edit]

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
Exemplary Sankey diagram visualizing energy flows for France in 2040 computed by AnyMOD.jl as part of a study on the European Green Deal.[103][104]

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.[105][104] Methods from stochastic programming are now being implemented to better address the uncertainties associated with renewable generation.[106]

As of 2022, 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.[107][108][109] 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.[103][110]

Backbone[edit]

Project Backbone
Host VTT, UCD
Status active
Scope/type framework - dispatch, investment, all sectors, LP/MILP
Code license LGPLv3
Language GAMS
Website gitlab.vtt.fi/backbone/backbone
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 and UCD, Dublin. The framework is agnostic about what is modeled, but still has capabilities to represent certain peculiarities found in some energy sectors — such as power flow, reserves, unit commitment, and heat diffusion in buildings. 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.[111]

Balmorel[edit]

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.[87]: 23  The codebase was made public in March 2001.[112] 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.[113][114][115] 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.[116] These ambitions and limitations have long since been superseded and Balmorel is no longer tied to its original geography and policy questions.[114] 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, 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.[117] 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.[118] 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.[119]

Calliope[edit]

Project Calliope
Host ETH Zurich, 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, 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.[120] And a two‑page software review is available.[121]

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.[122] 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.[123]

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.[124] One study cited investigates the consequences of pursuing energy self‑sufficiency by duly adding increasingly restrictive internal constraints.[125] Another at near optimal solutions for Italy.[126]

DESSTinEE[edit]

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.[127]

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.[128]

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.[129]

Energy Transition Model[edit]

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 (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 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[edit]

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, 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.[130] And the US PATHWAYS model contributed to the UN Deep Decarbonization Pathways Project (DDPP) assessments for the United States.[131] As of 2016, the DDPP plans to employ EnergyPATHWAYS for future analysis.

ETEM[edit]

Project ETEM
Host ORDECSYS
Status active
Scope/type municipal
Code license Eclipse 1.0
Website

ETEM stands for Energy Technology Environment Model. The ETEM model offers a similar structure to OSeMOSYS but is aimed at urban planning. The software is being developed by the ORDECSYS company, Chêne-Bougeries, Switzerland, supported with European Union and national research grants. The project has two websites. The software can be downloaded from first of these websites (but as of July 2016, this looks out of date). A manual is available with the software.[132] ETEM is written in MathProg.[e] Presentations describing ETEM are available.[133][134]

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.[135] 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.[136]

ficus[edit]

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 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.

GenX[edit]

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/
Carbon impacts for three core scenarios from the 2022 numerical study that examined the rapid elimination of Russian natural gas from Europe. All core scenarios resulted in reduced emissions.[137]: 7 

GenX is multi‑commodity sector capacity expansion model originally developed by researchers in the United States.[138][139] The framework is written in Julia and deploys the JuMP library for building the underlying optimization problem.[140][141] 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.[142]

In parallel, the PowerGenome project is designed to provide GenX with a comprehensive current state dataset of the United States electricity system.[143] 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,[144][145] to explore the value of 'firm' low-carbon power generation options,[146] and a variety of other applications. While North America remains a key focus, the software has been applied to problems in India,[147] Italy,[148] and Spain.[149]

A mid‑2022 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 October 2022.[137][150] 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 hydro generation and the system more generally.[citation needed]

oemof[edit]

Project oemof
Host oemof community supported by
Status active
Scope/type electricity, heat, mobility, gas
Code license MIT
Language Python
Website
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, 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. Version 0.1.0 was released on 1 December 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 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.[151] A masters project in 2020 compared oemof and OSeMOSYS.[152]

OSeMOSYS[edit]

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, 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.[153] A manual is available.[154]

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.[155]

Simplified results for a fictitious country called Atlantis used for training purposes

A key paper describing OSeMOSYS is available.[5] A 2011 study uses OSeMOSYS to investigate the role of household investment decisions.[156] 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.[157] 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.[158] In a 2016 study, OSeMOSYS is modified to take into account realistic consumer behavior.[159] 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.[160] 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.[161] 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.[162]

Cumulative electricity trade (2015–2065) among African countries for the reference scenario (TWh) [163]: 8 

OSeMOSYS has been used to construct wide-area electricity models for Africa, comprising 45 countries[164][165] and South America, comprising 13 countries.[166][167] It has also been used to support United Nations' regional climate, land, energy, and water strategies (CLEWS)[168] for the Sava river basin, central Europe,[169] the Syr Darya river basin, eastern Europe,[170]: 29  and Mauritius.[171] Models have previously been built for the Baltic States, Bolivia, Nicaragua, Sweden, and Tanzania.[172] A 2021 paper summarizes recent applications and also details various versions, forks, and local enhancements related to the OSeMOSYS codebase.[173] An electricity sector analysis for Bangladesh completed in 2021 concluded that solar power is economically competitive under every investigated scenario.[174] A 2022 study looked at the effects of a changing climate on the Ethiopian power system.[175] OSeMOSYS has also been applied variously in Zimbabwe[176] and Ecuador.[177] Another 2022 study examined water usage, split by withdraws and consumption, for several low carbon energy strategies for Africa.[163] Another study that year examined renewable energy in Egypt.[178] And another the Dominican Republic.[179]

In 2016, work started on a browser-based interface to OSeMOSYS, known as the Model Management Infrastructure (MoManI). Lead 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.[180][181][182] A simplified GUI interface named clicSAND and utilizing Excel and Access was released in March 2021.[183][184] A CLI workflow tool named otoole bundles several dedicated utilities, including one that can convert between OKI frictionless data and GNU MathProg data formats.[185][173]: 3  In 2022, the project released starter kits for modeling selected countries in Africa, East Asia, and South America.[186]

The OSeMBE reference model covering western and central Europe was announced on 27 April 2018.[187][188] The model uses the MathProg implementation of OSeMOSYS but requires a small patch 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.[189] The REEEM project runs from early-2016 till 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.[173]

PyPSA[edit]

Project PyPSA
Host Technical University of Berlin
Status active
Scope/type electric power systems with sector coupling
Code license MIT
Language Python
Website pypsa.org
Repository github.com/PyPSA/PyPSA
Documentation pypsa.readthedocs.io
Python package pypi.org/project/pypsa
Mailing list groups.google.com/group/pypsa
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. 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 Pyomo library. The source code is hosted on GitHub and is also released periodically as a PyPI package.

Simulated locational marginal prices across Germany under conditions of high wind and low load. Bottlenecks in north/south power transmission elicit the large differences.[190]: 11 

The basic functionality of PyPSA is described in a 2018 paper. PyPSA sits between 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.[190]

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.[191]

As of January 2018, PyPSA is used by more than a dozen research institutes and companies worldwide.[190]: 2  Some research groups have independently extended the software, for instance to model integer transmission expansion.[192]

In 2020, the PyPSA‑Eur‑Sec model for Europe was used to analyze several Paris Agreement Compatible Scenarios for Energy Infrastructure [193] and determined that early action should pay off.[194]

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.[195][196] The site was relaunched on 5 November 2019 with some internal improvements, a new URL, and faster solver now completing in about 12 s.[197] A newer version now uses the HiGHS solver.[198]

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.[199][200] 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 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.[201] A December 2021 study deployed a PyPSA‑PL model to assess policy options for Poland.[202][203][204]

PyPSA-Africa project[edit]

The PyPSA‑Africa project (previously PyPSA-meets-Africa) was launched in mid‑2021 to provide a single model and dataset spanning the African continent.[205][206] A July 2022 webinar co‑hosted by CPEEL, Nigeria advanced the Africa agenda.[207][208] The first research paper, released in 2022, examines pathways to net zero by 2060 — with solar power and battery storage expected to be the predominate technologies.[209]

TEMOA[edit]

Project TEMOA
Host North Carolina State University
Key people Joseph DeCarolis
Status active
Scope/type system planning
Code license GPLv2+
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, 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.[11]

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.[210] TEMOA is "strongly influenced by the well-documented MARKAL/TIMES model generators".[211]: 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.[14][212]

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.[213][214] The service also provides some limited data visualization and project management functionality.

Specialist models[edit]

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[edit]

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.[215]

Overall structure of VencoPy.

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.[215] The mathematical formulation is available.[216] VencoPy builds on an earlier spreadsheet prototype.[217]

Project statistics[edit]

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 GAMS language requires a proprietary environment and its significant cost effectively limits participation to those who can access an institutional copy.[218]

Programming components[edit]

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 [edit]

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.

Auction models[edit]

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

Open solvers[edit]

Many projects rely on a pure linear or mixed integer solver to perform classical optimization, constraint satisfaction, or some mix of the two. While there are several open source solver projects, the most commonly deployed solver is GLPK. GLPK has been adopted by Calliope, ETEM, ficus, OSeMOSYS, SWITCH, and TEMOA. Another alternative is the Clp solver.[225][226] From mid‑2022, the HiGHS open source solver offers another option. HiGHS is used by the web‑based version of the PyPSA European multi‑sector model [227]

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.[228]

The flexible SMS++ optimization toolbox, written in C++17, is being developed specifically to meet the needs of energy system modeling.[229]

See also[edit]

General

Software

People

Notes[edit]

  1. ^ The terminology is not settled. These models can also be known as open energy models or open source energy system models or some combination thereof.
  2. ^ NEMO was also under development in 2011 but it is unclear whether its codebase was public at that point.
  3. ^ The simplified AC power-flow method is also referred to as the DC load-flow method because the active power flow equation for fixed-frequency AC is analogous to Ohm's law applied to a resistor carrying DC current.[43]: 59  For the purposes of optimization, the quadratic loss function is also piecewise linearized.
  4. ^ MERRA-2 stands for Modern-Era Retrospective analysis for Research and Applications, Version 2. The remote-sensed data is provided unencumbered by the NASA Goddard Space Flight Center research laboratory.
  5. ^ Note that GMPL, referred to in the documentation, is an alternative name for MathProg.
  6. ^ OSeMOSYS is deemed to reside in Sweden due to the influence of the KTH Royal Institute of Technology on the project.
  7. ^ MathProg is a subset of AMPL. It is sometimes possible to convert an AMPL model into MathProg without much effort.

References[edit]

  1. ^ a b acatech; Lepoldina; Akademienunion, eds. (2016). Consulting with energy scenarios: requirements for scientific policy advice (PDF). Berlin, Germany: acatech — National Academy of Science and Engineering. ISBN 978-3-8047-3550-7. Archived from the original (PDF) on 21 December 2016. Retrieved 19 December 2016.
  2. ^ Bazilian, Morgan; Rice, Andrew; Rotich, Juliana; Howells, Mark; DeCarolis, Joseph; Macmillan, Stuart; Brooks, Cameron; Bauer, Florian; Liebreich, Michael (2012). "Open source software and crowdsourcing for energy analysis" (PDF). Energy Policy. 49: 149–153. doi:10.1016/j.enpol.2012.06.032. Retrieved 17 June 2016.
  3. ^ Morin, Andrew; Urban, Jennifer; Sliz, Piotr (26 July 2012). "A quick guide to software licensing for the scientist-programmer". PLOS Computational Biology. 8 (7): e1002598. Bibcode:2012PLSCB...8E2598M. doi:10.1371/journal.pcbi.1002598. ISSN 1553-7358. PMC 3406002. PMID 22844236.
  4. ^ a b c Pfenninger, Stefan; DeCarolis, Joseph; Hirth, Lion; Quoilin, Sylvain; Staffell, Iain (February 2017). "The importance of open data and software: is energy research lagging behind?". Energy Policy. 101: 211–215. doi:10.1016/j.enpol.2016.11.046. ISSN 0301-4215.
  5. ^ a b Howells, Mark; Rogner, Holger; Strachan, Neil; Heaps, Charles; Huntington, Hillard; Kypreos, Socrates; Hughes, Alison; Silveira, Semida; DeCarolis, Joe; Bazilian, Morgan; Roehrl, Alexander (2011). "OSeMOSYS: the open source energy modeling system: an introduction to its ethos, structure and development". Energy Policy. 39 (10): 5850–5870. doi:10.1016/j.enpol.2011.06.033. The name Morgan Bazillian has been corrected.
  6. ^ Chang, Miguel; Thellufsen, Jakob Zink; Zakeri, Behnam; Pickering, Bryn; Pfenninger, Stefan; Lund, Henrik; Østergaard, Poul Alberg (15 May 2021). "Trends in tools and approaches for modelling the energy transition". Applied Energy. 290: 116731. doi:10.1016/j.apenergy.2021.116731. ISSN 0306-2619. S2CID 233585332. See figure 4 in particular.
  7. ^ Syranidou, Chloi; Koch, Matthias; Matthes, Björn; Winger, Christian; Linßen, Jochen; Rehtanz, Christian; Stolten, Detlef (1 May 2022). "Development of an open framework for a qualitative and quantitative comparison of power system and electricity grid models for Europe". Renewable and Sustainable Energy Reviews. 159: 112055. doi:10.1016/j.rser.2021.112055. ISSN 1364-0321. S2CID 246588297. Retrieved 3 June 2022. open access
  8. ^ Morrison, Robbie (3 June 2022). "MODEX framework comparison study for Germany — Blog". Open Energy Modelling Initiative. Retrieved 24 July 2022. open access
  9. ^ Mundaca, Luis; Neij, Lena; Worrell, Ernst; McNeil, Michael A (1 August 2010). "Evaluating energy efficiency policies with energy-economy models — Report number LBNL-3862E" (PDF). Annual Review of Environment and Resources. Berkeley, CA, US: Ernest Orlando Lawrence Berkeley National Laboratory. doi:10.1146/annurev-environ-052810-164840. OSTI 1001644. Archived from the original (PDF) on 21 December 2016. Retrieved 15 November 2016.
  10. ^ Mundaca, Luis; Neij, Lena; Worrell, Ernst; McNeil, Michael A (22 October 2010). "Evaluating energy efficiency policies with energy-economy models". Annual Review of Environment and Resources. 35 (1): 305–344. doi:10.1146/annurev-environ-052810-164840. ISSN 1543-5938.
  11. ^ a b DeCarolis, Joseph F; Hunter, Kevin; Sreepathi, Sarat (2012). "The case for repeatable analysis with energy economy optimization models" (PDF). Energy Economics. 34 (6): 1845–1853. arXiv:2001.10858. doi:10.1016/j.eneco.2012.07.004. S2CID 59143900. Retrieved 8 July 2016.
  12. ^ Cao, Karl-Kiên; Cebulla, Felix; Gómez Vilchez, Jonatan J; Mousavi, Babak; Prehofer, Sigrid (28 September 2016). "Raising awareness in model-based energy scenario studies — a transparency checklist". Energy, Sustainability and Society. 6 (1): 28–47. doi:10.1186/s13705-016-0090-z. ISSN 2192-0567. S2CID 52243291.
  13. ^ a b Pfenninger, Stefan (23 February 2017). "Energy scientists must show their workings" (PDF). Nature News. 542 (7642): 393. Bibcode:2017Natur.542..393P. doi:10.1038/542393a. PMID 28230147. S2CID 4449502. Retrieved 26 February 2017.
  14. ^ a b DeCarolis, Joseph F; Jaramillo, Paulina; Johnson, Jeremiah X; McCollum, David L; Trutnevyte, Evelina; Daniels, David C; Akın-Olçum, Gökçe; Bergerson, Joule; Cho, Soolyeon; Choi, Joon-Ho; Craig, Michael T; de Queiroz, Anderson R; Eshraghi, Hadi; Galik, Christopher S; Gutowski, Timothy G; Haapala, Karl R; Hodge, Bri-Mathias; Hoque, Simi; Jenkins, Jesse D; Jenn, Alan; Johansson, Daniel JA; Kaufman, Noah; Kiviluoma, Juha; Lin, Zhenhong; MacLean, Heather L; Masanet, Eric; Masnadi, Mohammad S; McMillan, Colin A; Nock, Destenie S; Patankar, Neha; Patino-Echeverri, Dalia; Schivley, Greg; Siddiqui, Sauleh; Smith, Amanda D; Venkatesh, Aranya; Wagner, Gernot; Yeh, Sonia; Zhou, Yuyu (16 December 2020). "Leveraging open-source tools for collaborative macro-energy system modeling efforts". Joule. 4 (12): 2523–2526. doi:10.1016/j.joule.2020.11.002. ISSN 2542-4785. S2CID 229492155.
  15. ^ "SET-Plan update" (PDF). SETIS Magazine (13): 5–7. November 2016. ISSN 2467-382X. Retrieved 1 March 2017.
  16. ^ "Medeas: modeling the renewable energy transition in Europe". Spanish National Research Council (CSIC). Barcelona, Spain. Retrieved 1 March 2017.
  17. ^ Howells, Mark (November 2016). "OSeMOSYS: open source software for energy modelling" (PDF). SETIS Magazine (13): 37–38. ISSN 2467-382X. Retrieved 1 March 2017.
  18. ^ Simoes, Sofia; Nijs, Wouter; Ruiz, Pablo; Sgobbi, Alessandra; Radu, Daniela; Bolat, Pelin; Thiel, Christian; Peteves, Stathis (2013). The JRC-EU-TIMES model: assessing the long-term role of the SET Plan energy technologies — LD-NA-26292-EN-N (PDF). Luxembourg: Publications Office of the European Union. doi:10.2790/97596. ISBN 978-92-79-34506-7. ISSN 1831-9424. Retrieved 3 March 2017. The DOI, ISBN, and ISSN refer to the online version.
  19. ^ European Commission (14 October 2021). "Funding and tenders — Energy system modelling, optimisation and planning tools — TOPIC ID: HORIZON-CL5-2022-D3-01-13". European Commission. Retrieved 10 November 2021. Deadline 26 April 2022.
  20. ^ Heider, Anya; Reibsch, Ricardo; Blechinger, Philipp; Linke, Avia; Hug, Gabriela (1 November 2021). "Flexibility options and their representation in open energy modelling tools". Energy Strategy Reviews. 38: 100737. doi:10.1016/j.esr.2021.100737. ISSN 2211-467X. S2CID 244151317.
  21. ^ Nitsch, Felix; Schimeczek, Christoph (18 February 2022). Backtesting the open source electricity market model AMIRIS by simulating the Austrian day-ahead market — Presentation (PDF). German Aerospace Center (DLR). Stuttgart, Germany. Retrieved 29 March 2022. Presentation at 17th Symposium Energieinnovation EnInnov 2022, Graz, Austria. open access
  22. ^ Klein, Martin; Frey, Ulrich J; Reeg, Matthias (2019). "Models within models: agent‑based modelling and simulation in energy systems analysis". Journal of Artificial Societies and Social Simulation. 22 (4): 6. doi:10.18564/jasss.4129. ISSN 1460-7425. S2CID 208170977. open access
  23. ^ Deissenroth, Marc; Klein, Martin; Nienhaus, Kristina; Reeg, Matthias (10 December 2017). "Assessing the plurality of actors and policy interactions: agent‑based modelling of renewable energy market integration" (PDF). Complexity. 2017: –7494313. doi:10.1155/2017/7494313. ISSN 1076-2787. Retrieved 21 May 2021.
  24. ^ Torralba-Díaz, Laura; Schimeczek, Christoph; Reeg, Matthias; Savvidis, Georgios; Deissenroth-Uhrig, Marc; Guthoff, Felix; Fleischer, Benjamin; Hufendiek, Kai (January 2020). "Identification of the Efficiency Gap by Coupling a Fundamental Electricity Market Model and an Agent-Based Simulation Model". Energies. 13 (15): 3920. doi:10.3390/en13153920.
  25. ^ Frey, Ulrich; Klein, Martin; Nienhaus, Kristina; Schimeczek, Christoph (14 October 2020). "Self-Reinforcing Electricity Price Dynamics under the Variable Market Premium Scheme". Energies. 13 (20): 5350. doi:10.3390/en13205350. ISSN 1996-1073. Retrieved 4 April 2022.
  26. ^ Nitsch, Felix; Deissenroth-Uhrig, Marc; Schimeczek, Christoph; Bertsch, Valentin (15 September 2021). "Economic evaluation of battery storage systems bidding on day-ahead and automatic frequency restoration reserves markets". Applied Energy. 298: 117267. doi:10.1016/j.apenergy.2021.117267. Retrieved 4 April 2022.
  27. ^ "Framework for distributed Agent-based Modelling of Energy systems (FAME)". 30 March 2022. Retrieved 22 February 2022. Source code repository.
  28. ^ Xu, Yixing; Myhrvold, Nathan; Sivam, Dhileep; Mueller, Kaspar; Olsen, Daniel J.; Xia, Bainan; Livengood, Daniel; Hunt, Victoria; Rouillé d’Orfeuil, Benjamin; Muldrew, Daniel; Ondreicka, Merrielle; Bettilyon, Megan (August 2020). U.S. Test System with High Spatial and Temporal Resolution for Renewable Integration Studies. 2020 IEEE Power & Energy Society General Meeting (PESGM). IEEE Power & Energy Society General Meeting: [Proceedings]. pp. 1–5. doi:10.1109/PESGM41954.2020.9281850. ISBN 978-1-7281-5508-1. ISSN 1944-9933.
  29. ^ Yixing Xu; Myhrvold, Nathan; Dhileep Sivam; Mueller, Kaspar; Olsen, Daniel J.; Bainan Xia; Livengood, Daniel; Hunt, Victoria; D'Orfeuil, Ben Rouille; Muldrew, Daniel; Merrielle Ondreicka; Bettilyon, Megan (2020). "U.S. Test System with High Spatial and Temporal Resolution for Renewable Integration Studies". Breakthrough Energy Model Dataset. arXiv:2002.06155. doi:10.5281/zenodo.3530899. Retrieved 1 July 2021.
  30. ^ "Electric Grid Test Case Repository". Texas A&M University Electric Grid Datasets. Retrieved 1 July 2021.
  31. ^ Xu, Yixing; Olsen, Daniel; Xia, Bainan; Livengood, Dan; Hunt, Victoria; Li, Yifan; Smith, Lane (January 2021). A 2030 United States Macro Grid: Unlocking Geographical Diversity to Accomplish Clean Energy Goals (PDF). Seattle, Washington, USA: Breakthrough Energy Sciences. Retrieved 1 July 2021.
  32. ^ a b Zerrahn, Alexander; Schill, Wolf-Peter (2015). A greenfield model to evaluate long-run power storage requirements for high shares of renewables — DIW discussion paper 1457 (PDF). Berlin, Germany: German Institute for Economic Research (DIW). ISSN 1619-4535. Retrieved 7 July 2016.
  33. ^ Zerrahn, Alexander; Schill, Wolf-Peter (2017). "Long-run power storage requirements for high shares of renewables: review and a new model". Renewable and Sustainable Energy Reviews. 79: 1518–1534. doi:10.1016/j.rser.2016.11.098.
  34. ^ Schill, Wolf-Peter; Niemeyer, Moritz; Zerrahn, Alexander; Diekmann, Jochen (1 June 2016). "Bereitstellung von Regelleistung durch Elektrofahrzeuge: Modellrechnungen für Deutschland im Jahr 2035". Zeitschrift für Energiewirtschaft (in German). 40 (2): 73–87. doi:10.1007/s12398-016-0174-7. hdl:10419/165995. ISSN 0343-5377. S2CID 163807710.
  35. ^ Schill, Wolf-Peter; Zerrahn, Alexander; Kunz, Friedrich (1 June 2017). "Prosumage of solar electricity: pros, cons, and the system perspective" (PDF). Economics of Energy & Environmental Policy. 6 (1). doi:10.5547/2160-5890.6.1.wsch. ISSN 2160-5882.
  36. ^ Schill, Wolf-Peter; Zerrahn, Alexander (2018). "Long-run power storage requirements for high shares of renewables: Results and sensitivities". Renewable and Sustainable Energy Reviews. 83: 156–171. doi:10.1016/j.rser.2017.05.205.
  37. ^ Hidalgo González, Ignacio; Quoilin, Sylvain; Zucker, Andreas (2014). Dispa-SET 2.0: unit commitment and power dispatch model: description, formulation, and implementation — EUR 27015 EN (PDF). Luxembourg: Publications Office of the European Union. doi:10.2790/399921. ISBN 978-92-79-44690-0. Retrieved 1 March 2017. The DOI and ISBN refer to the online version.
  38. ^ a b c d Quoilin, Sylvain; Hidalgo González, Ignacio; Zucker, Andreas (2017). Modelling future EU power systems under high shares of renewables: the Dispa-SET 2.1 open-source model — EUR 28427 EN (PDF). Luxembourg: Publications Office of the European Union. doi:10.2760/25400. ISBN 978-92-79-65265-3. Retrieved 1 March 2017.
  39. ^ a b "Dispa-SET documentation". Retrieved 2 March 2017. Automatically the latest version.
  40. ^ "SET-Plan Update" (PDF). SETIS Magazine (13): 5–7. November 2016. ISSN 2467-382X. Retrieved 1 March 2017.
  41. ^ Hidalgo González, Ignacio; Ruiz Castello, Pablo; Sgobbi, Alessandra; Nijs, Wouter; Quoilin, Sylvain; Zucker, Andreas; Thiel, Christian (2015). Addressing flexibility in energy system models — EUR 27183 EN (PDF) (Report). Luxembourg: Publications Office of the European Union. doi:10.2790/925. ISBN 978-92-79-47235-0. Retrieved 2 March 2017. The DOI and ISBN refer to the online version.
  42. ^ Quoilin, Sylvain; Nijs, Wouter; Hidalgo González, Ignacio; Zucker, Andreas; Thiel, Christian (19 May 2015). Evaluation of simplified flexibility evaluation tools using a unit commitment model. 2015 12th International Conference on the European Energy Market (EEM). Energy Market, Eem, International Conference on the European. pp. 1–5. doi:10.1109/EEM.2015.7216757. ISBN 978-1-4673-6692-2. ISSN 2165-4077.
  43. ^ Andersson, Göran (2008). Modelling and analysis of electric power systems: power flow analysis fault analysis power systems dynamics and stability (PDF). Zürich, Switzerland: ETH Zurich. Archived from the original (PDF) on 3 March 2016. Retrieved 2 February 2017.
  44. ^ Quoilin, Sylvain; Hidalgo González, Ignacio; Zucker, Andreas; Thiel, Christian (September 2014). "Available technical flexibility for balancing variable renewable energy sources: case study in Belgium" (PDF). Proceedings of the 9th Conference on Sustainable Development of Energy, Water and Environment Systems. Retrieved 2 March 2017.
  45. ^ De Felice, Matteo; Busch, Sebastian; Kanellopoulos, Konstantinos; Kavvadias, Konstantinos; Hidalgo Gonzalez, Ignacio (April 2020). Power system flexibility in a variable climate — EUR 30184 EN, JRC120338. Luxembourg: Publications Office of the European Union. doi:10.2760/75312. ISBN 978-92-76-18183-5.
  46. ^ Pavičević, Matija; De Felice, Matteo; Busch, Sebastian; Hidalgo González, Ignacio; Quoilin, Sylvain (1 August 2021). "Water-energy nexus in African power pools – the Dispa-SET Africa model". Energy. 228: 120623. doi:10.1016/j.energy.2021.120623. hdl:2268/288350. ISSN 0360-5442. S2CID 234814778. Retrieved 30 April 2021.
  47. ^ EMLab — Generation Factsheet (PDF). Delft, The Netherlands: Energy Modelling Lab, Delft University of Technology. Retrieved 9 July 2016.
  48. ^ de Vries, Laurens J; Chappin, Émile JL; Richstein, Jörn C (August 2015). EMLab-Generation: an experimentation environment for electricity policy analysis — Project report — Version 1.2 (PDF). Delft, The Netherlands: Energy Modelling Lab, Delft University of Technology. Retrieved 9 July 2016.
  49. ^ World energy outlook 2011 (PDF). Paris, France: International Energy Agency (IEA). 2011. ISBN 978-92-64-12413-4. Retrieved 9 July 2016.
  50. ^ Richstein, Jörn C; Chappin, Emile JL; de Vries, Laurens J (2014). "Cross-border electricity market effects due to price caps in an emission trading system: an agent-based approach". Energy Policy. 71: 139–158. doi:10.1016/j.enpol.2014.03.037.
  51. ^ a b Hirth, Lion (12 April 2016). The European Electricity Market Model EMMA — Model documentation — Version 2016-04-12 (PDF). Berlin, Germany: Neon Neue Energieökonomik. Retrieved 9 July 2016.
  52. ^ Hirth, Leon (2015). The economics of wind and solar variability: how the variability of wind and solar power affects their marginal value, optimal deployment, and integration costs — PhD thesis (PDF). Berlin, Germany: Technical University of Berlin. doi:10.14279/depositonce-4291. Retrieved 7 July 2016.
  53. ^ Hirth, Lion (2013). "The market value of variable renewables: the effect of solar wind power variability on their relative price" (PDF). Energy Economics. 38: 218–236. doi:10.1016/j.eneco.2013.02.004. hdl:1814/27135. Retrieved 9 July 2016.
  54. ^ Hirth, Leon (2015). "The optimal share of variable renewables: how the variability of wind and solar power affects their welfare-optimal deployment" (PDF). The Energy Journal. 36 (1): 127–162. doi:10.5547/01956574.36.1.6. Retrieved 7 July 2016.
  55. ^ "The Project". GENESYS project. Retrieved 9 July 2016.
  56. ^ a b c Bussar, Christian; Moos, Melchior; Alvarez, Ricardo; Wolf, Philipp; Thien, Tjark; Chen, Hengsi; Cai, Zhuang; Leuthold, Matthias; Sauer, Dirk Uwe; Moser, Albert (2014). "Optimal allocation and capacity of energy storage systems in a future European power system with 100% renewable energy generation". Energy Procedia. 46: 40–47. doi:10.1016/j.egypro.2014.01.156.
  57. ^ a b Bussar, Christian; Stöcker, Philipp; Cai, Zhuang; Moraes Jr, Luiz; Magnor, Dirk; Wiernes, Pablo; van Bracht, Niklas; Moser, Albert; Sauer, Dirk Uwe (2016). "Large-scale integration of renewable energies and impact on storage demand in a European renewable power system of 2050 – Sensitivity study". Journal of Energy Storage. 6: 1–10. doi:10.1016/j.est.2016.02.004.
  58. ^ "NEMO". OzLabs. Australia. Retrieved 3 December 2016.
  59. ^ a b c Elliston, Ben; Diesendorf, Mark; MacGill, Iain (June 2012). "Simulations of scenarios with 100% renewable electricity in the Australian National Electricity Market". Energy Policy. 45: 606–613. doi:10.1016/j.enpol.2012.03.011. ISSN 0301-4215. Retrieved 19 December 2016. Preprint URL given. This paper does not mention NEMO explicitly.
  60. ^ a b Elliston, Ben; Riesz, Jenny; MacGill, Iain (September 2016). "What cost for more renewables? The incremental cost of renewable generation — An Australian National Electricity Market case study" (PDF). Renewable Energy. 95: 127–139. doi:10.1016/j.renene.2016.03.080. ISSN 0960-1481. Retrieved 3 December 2016. Preprint URL given.
  61. ^ Elliston, Ben; MacGill, Iain; Diesendorf, Mark (June 2014). "Comparing least cost scenarios for 100% renewable electricity with low emission fossil fuel scenarios in the Australian National Electricity Market" (PDF). Renewable Energy. 66: 196–204. doi:10.1016/j.renene.2013.12.010. ISSN 0960-1481. Draft URL given.
  62. ^ "OnSSET: open source spatial electrification tool". OnSSET. Stockholm, Sweden. Retrieved 8 March 2017.
  63. ^ "OpeN Source Spatial Electrification Toolkit (OnSSET)". Department of Energy Technology, KTH Royal Institute of Technology. Stockholm, Sweden. Retrieved 5 December 2016.
  64. ^ Mentis, Dimitrios; Korkovelos, Alexandros; Shahid Siyal, Shahid; Paritosh, Deshpante; Broad, Oliver; Howells, Mark; Rogner, Holger (13 November 2015). Lighting up the world: the first global application of the open source, spatial electrification tool (OnSSET) — Presentation. 2015 International Workshop on Environment and Alternative Energy. Retrieved 7 March 2017.
  65. ^ a b c Nerini, Francesco Fuso; Broad, Oliver; Mentis, Dimitris; Welsch, Manuel; Bazilian, Morgan; Howells, Mark (15 January 2016). "A cost comparison of technology approaches for improving access to electricity services". Energy. 95: 255–265. doi:10.1016/j.energy.2015.11.068. ISSN 0360-5442.
  66. ^ a b Berndtsson, Carl (2016). Open geospatial data for energy planning (MSc). Stockholm, Sweden: KTH School of Industrial Engineering and Management. Retrieved 7 March 2017.
  67. ^ a b Peña Balderrama, JG; Balderrama Subieta, S; Lombardi, Francesco; Stevanato, N; Sahlberg, A; Howells, Mark; Colombo, E; Quoilin, Sylvain (1 June 2020). "Incorporating high-resolution demand and techno-economic optimization to evaluate micro-grids into the Open Source Spatial Electrification Tool (OnSSET)". Energy for Sustainable Development. 56: 98–118. doi:10.1016/j.esd.2020.02.009. ISSN 0973-0826.
  68. ^ Korkovelos, Alexandros; Bazilian, Morgan; Mentis, Dimitrios; Howells, Mark (2017). A GIS approach to planning electrification in Afghanistan. Washington DC, USA: The World Bank.
  69. ^ Arderne, Christopher (June 2016). A climate, land-use, energy and water nexus assessment of Bolivia (PDF) (MSc). Stockholm, Sweden: KTH School of Industrial Engineering and Management. Retrieved 7 March 2017.
  70. ^ Mentis, Dimitrios; Andersson, Magnus; Howells, Mark; Rogner, Holger; Siyal, Shahid; Broad, Oliver; Korkovelos, Alexandros; Bazilian, Morgan (July 2016). "The benefits of geospatial planning in energy access: a case study on Ethiopia" (PDF). Applied Geography. 72: 1–13. doi:10.1016/j.apgeog.2016.04.009. ISSN 0143-6228.
  71. ^ Korkovelos, Alexandros; Khavari, Babak; Sahlberg, Andreas; Howells, Mark; Arderne, Christopher (January 2019). "The role of open access data in geospatial electrification planning and the achievement of SDG7: an OnSSET-based case study for Malawi". Energies. 12 (7): 1395. doi:10.3390/en12071395. ISSN 1996-1073. Dimitrios Mentis added as sixth author following original publication.
  72. ^ Mentis, Dimitrios; Welsch, Manuel; Fuso Nerini, Francesco; Broad, Oliver; Howells, Mark; Bazilian, Morgan; Rogner, Holger (December 2015). "A GIS-based approach for electrification planning: a case study on Nigeria". Energy for Sustainable Development. 29: 142–150. doi:10.1016/j.esd.2015.09.007. ISSN 0973-0826.
  73. ^ "Universal electrification access". United Nations Department of Economic and Social Affairs (UN DESA). New York, USA. Retrieved 9 March 2017.
  74. ^ Isihak, Salisu; Akpan, Uduak; Bhattacharyya, Subhes (19 February 2022). "Evolution of GIS-based rural electrification planning models and an application of OnSSET in Nigeria — Official pre‑proof". Renewable and Sustainable Energy Transition: 100019. doi:10.1016/j.rset.2022.100019. ISSN 2667-095X. S2CID 247004954. Retrieved 24 February 2022. open access
  75. ^ International Energy Agency (2014). World Energy Outlook 2014 (PDF). Paris, France: OECD/IEA. ISBN 978-92-64-20805-6. Retrieved 9 March 2017.
  76. ^ International Energy Agency (2015). World Energy Outlook 2015. Paris, France: OECD/IEA. ISBN 978-92-64-24366-8.
  77. ^ International Energy Agency (IEA) and the World Bank (June 2015). Sustainable energy for all 2015: progress toward sustainable energy (PDF). Washington DC, USA: World Bank. doi:10.1596/978-1-4648-0690-2. hdl:11343/119617. ISBN 978-1-4648-0690-2. Retrieved 9 March 2017. Licensed under Creative Commons CC BY 3.0 IGO.
  78. ^ International Energy Agency (IEA) (8 November 2019). Africa energy outlook. Paris, France: IEA Publications. No cost but registration required.
  79. ^ GEP. "Global Electrification Platform Explorer". Retrieved 19 November 2020.
  80. ^ "PYPOWER". Python Software Foundation. Beaverton, OR, USA. Retrieved 2 December 2016.
  81. ^ Scheidler, Alexander; Thurner, Leon; Kraiczy, Markus; Braun, Martin (14–15 November 2016). Automated grid planning for distribution grids with increasing PV penetration (PDF). 6th Solar Integration Workshop: International Workshop on Integration of Solar Power into Power Systems. Vienna, Austria. Retrieved 2 December 2016.
  82. ^ Thurner, Leon; Scheidler, Alexander; Schäfer, Florian; Menke, Jan-Hendrik; Dollichon, Julian; Meier, Friederike; Meinecke, Steffen; Braun, Martin (2018). "Pandapower: an open source python tool for convenient modeling, analysis and optimization of electric power systems". IEEE Transactions on Power Systems. 33 (6): 6510–6521. arXiv:1709.06743. Bibcode:2018ITPSy..33.6510T. doi:10.1109/TPWRS.2018.2829021. ISSN 0885-8950. S2CID 4917834. The arXiv link given is for version 3.
  83. ^ Thurner, Leon (4 May 2018). "pandapower news: reference paper published / unbalanced calculations / BNetzA adopts pandapower". openmod-initiative (Mailing list). Retrieved 4 May 2018. We are especially proud to say that the German Federal Network Agency (Bundesnetzagentur) is also adopting pandapower for automated grid analysis.
  84. ^ Degner, Thomas; Rohrig, Kurt; Strauß, Philipp; Braun, Martin; Wurdinger, Kerstin; Korte, Klaas (22 March 2017). "Anforderungen an ein zukunftsfähiges Stromnetz" [Requirements for a sustainable power grid]. Forschung für die Energiewende – Die Gestaltung des Energiesystems Beiträge zur FVEE-Jahrestagung 2016 [Research for the energiewende — the design of the energy system contributions to the FVEE Annual Conference 2016] (PDF) (in German). Berlin, Germany: Forschungsverbund Erneuerbare Energien (FVEE). pp. 88–95. Retrieved 4 May 2018.
  85. ^ Kok, Koen (13 May 2013). The PowerMatcher: smart coordination for the smart electricity grid (PDF) (PhD). Amsterdam, The Netherlands: Vrije Universiteit Amsterdam. Retrieved 8 July 2016.
  86. ^ Wiese, Frauke (16 November 2014). renpass: Renewable Energy Pathways Simulation System — Manual (PDF). Retrieved 13 March 2017.
  87. ^ a b Wiese, Frauke (2015). renpass: Renewable Energy Pathways Simulation System: Open source as an approach to meet challenges in energy modeling (PDF) (PhD). Aachen, Germany: Shaker Verlag. ISBN 978-3-8440-3705-0. Retrieved 12 July 2016. University of Flensburg, Flensburg, Germany.
  88. ^ Bernhardi, Nicolas; Bökenkamp, Gesine; Bons, Marian; Borrmann, Rasmus; Christ, Marion; Grüterich, Lauren; Heidtmann, Emilie; Jahn, Martin; Janssen, Tomke; Lesch, Jonas; Müller, Ulf Philipp; Pelda, Johannes; Stein, Isabelle; Veddeler, Eike; Voß, David; Wienholt, Lukas; Wiese, Frauke; Wingenbach, Clemens (November 2012). Modeling sustainable electricity systems for the Baltic Sea region — Discussion paper 3 (PDF). Flensburg, Germany: Centre for Sustainable Energy Systems (CSES), University of Flensburg. ISSN 2192-4597. Retrieved 17 June 2016.
  89. ^ Wiechers, Eva; Böhm, Hendrik; Bunke, Wolf Dieter; Kaldemeyer, Cord; Kummerfeld, Tim; Söthe, Martin; Thiesen, Henning (2014). Modelling sustainable electricity systems for Germany and neighbours in 2050. Flensburg, Germany: Centre for Sustainable Energy Systems (CSES), University of Flensburg.
  90. ^ Bökenkamp, Gesine (October 2014). The role of Norwegian hydro storage in future renewable electricity supply systems in Germany: analysis with a simulation model (PDF) (PhD). Flensburg, German: University of Flensburg. Retrieved 12 July 2016.
  91. ^ Wiese, Frauke; Bökenkamp, Gesine; Wingenbach, Clemens; Hohmeyer, Olav (2014). "An open source energy system simulation model as an instrument for public participation in the development of strategies for a sustainable future". Wiley Interdisciplinary Reviews: Energy and Environment. 3 (5): 490–504. doi:10.1002/wene.109. ISSN 2041-840X. S2CID 108676376.
  92. ^ Matke, Carsten; Medjroubi, Wided; Kleinhans, David (2015). SciGRID: an open source model of the European power transmission network — Poster (PDF). Mathematics and Physics of Multilayer Complex Networks. Dresden, Germany. Retrieved 8 July 2016.
  93. ^ Wiegmans, Bart (2015). Improving the topology of an electric network model based on Open Data (PDF) (MSc). Groningen, The Netherlands: Energy and Sustainability Research Institute, University of Groningen. Retrieved 8 July 2016.
  94. ^ Bosilovich, Michael G; Lucches, Rob; Suarez, M (12 March 2016). MERRA-2: File specification — GMAO Office Note No. 9 (Version 1.1) (PDF). Greenbelt, Maryland, USA: Global Modeling and Assimilation Office (GMAO), Earth Sciences Division, NASA Goddard Space Flight Center. Retrieved 8 July 2016.
  95. ^ Rose, Ben (April 2016). Clean electricity Western Australia 2030: modelling renewable energy scenarios for the South West Integrated System (PDF). West Perth, WA, Australia: Sustainable Energy Now. Retrieved 5 December 2017.
  96. ^ Fripp, Matthius (2012). "Switch: a planning tool for power systems with large shares of intermittent renewable energy" (PDF). Environmental Science and Technology. 46 (11): 6371–6378. Bibcode:2012EnST...46.6371F. CiteSeerX 10.1.1.469.9527. doi:10.1021/es204645c. ISSN 0013-936X. PMID 22506835. Retrieved 11 July 2016.
  97. ^ Fripp, Matthias (29 June 2016). Consensus-based power system planning using open assumptions and models — Presentation (PDF). Manoa, Hawaii, USA: University of Hawaii. Retrieved 31 January 2019.
  98. ^ a b Johnston, Josiah; Henríquez, Rodrigo; Maluenda, Benjamín; Fripp, Matthias (2019). "Switch 2.0: a modern platform for planning high-renewable power systems". SoftwareX. 10: 100251. arXiv:1804.05481. Bibcode:2019SoftX..1000251J. doi:10.1016/j.softx.2019.100251. S2CID 51783016. arXiv preprint v3. The release date for 2.0.0 was 1 August 2018 under GitHub commit fc19cfe.
  99. ^ Fripp, Matthias (27 December 2018). "Intercomparison between Switch 2.0 and GE MAPS models for simulation of high-renewable power systems in Hawaii". Energy, Sustainability and Society. 8 (1): 41. doi:10.1186/s13705-018-0184-x. ISSN 2192-0567. S2CID 53070135.
  100. ^ a b Huber, Matthias; Dorfner, Johannes; Hamacher, Thomas (18 January 2012). Electricity system optimization in the EUMENA region — Technical report (PDF). Munich, Germany: Institute for Energy Economy and Application Technology, Technical University of Munich. doi:10.14459/2013md1171502. Retrieved 7 July 2016.
  101. ^ Schaber, Katrin; Steinke, Florian; Hamacher, Thomas (April 2012). "Transmission grid extensions for the integration of variable renewable energies in Europe: who benefits where?". Energy Policy. 43: 123–135. doi:10.1016/j.enpol.2011.12.040. hdl:11858/00-001M-0000-0026-E54A-9.
  102. ^ Stich, Juergen; Mannhart, Melanie; Zipperle, Thomas; Massier, Tobias; Huber, Matthias; Hamacher, Thomas (2014). Modelling a low-carbon power system for Indonesia, Malaysia and Singapore (PDF). 33rd IEW International Energy Workshop, Peking, China. Retrieved 7 July 2016.
  103. ^ a b Hainsch, Karlo; Göke, Leonard; Kemfert, Claudia; Oei, Pao-Yu; von Hirschhausen, Christian (2020). European Green Deal: Using ambitious climate targets and renewable energy to climb out of the economic crisis — DIW Weekly Report 9 (PDF). Berlin, Germany: German Institute for Economic Research (DIW). ISSN 2667-095X. Retrieved 16 August 2022.
  104. ^ a b Göke, Leonard (December 2021). "AnyMOD.jl: A Julia package for creating energy system models". SoftwareX. 16: 100871. arXiv:2011.00895. Bibcode:2021SoftX..1600871G. doi:10.1016/j.softx.2021.100871. ISSN 2352-7110. S2CID 226283383. Retrieved 16 August 2022. open access
  105. ^ Göke, Leonard (1 November 2021). "A graph-based formulation for modeling macro-energy systems". Applied Energy. 301: 117377. arXiv:2004.10184. doi:10.1016/j.apenergy.2021.117377. ISSN 0306-2619. S2CID 236976140. Retrieved 16 August 2022.
  106. ^ Göke, Leonard; Kendziorski, Mario; Schmidt, Felix (2022). "Planning macro-energy systems with multiple climatic years — A quadratic trust-region approach for Benders decomposition". arXiv:2208.07078 [math.OC].
  107. ^ Kendziorski, Mario; Göke, Leonard; von Hirschhausen, Christian; Kemfert, Claudia; Zozmann, Elmar (August 2022). "Centralized and decentral approaches to succeed the 100% energiewende in Germany in the European context — A model-based analysis of generation, network, and storage investments". Energy Policy. 167: 113039. arXiv:2205.09066. doi:10.1016/j.enpol.2022.113039. ISSN 0301-4215. S2CID 248863407. Retrieved 16 August 2022.
  108. ^ Göke, Leonard; Kendziorski, Mario; Kemfert, Claudia; von Hirschhausen, Christian (September 2022). "Accounting for spatiality of renewables and storage in transmission planning". Energy Economics. 113: 106190. doi:10.1016/j.eneco.2022.106190. ISSN 0140-9883. S2CID 236976304. Retrieved 16 August 2022.
  109. ^ Eerma, MH; Manning, D; Økland, GL; Rodriguez del Angel, C; Seifert, PE; Winkler, J; Zamora Blaumann, A; Zozmann, E; Hosseinioun, SS; Göke, L; Kendziorski, M; von Hirschhausen, C (August 2022). "The potential of behavioral changes to achieve a fully renewable energy system — A case study for Germany". Renewable and Sustainable Energy Transition. 2: 100028. doi:10.1016/j.rset.2022.100028. ISSN 2667-095X. S2CID 250637854. Retrieved 16 August 2022. open access
  110. ^ Göke, Leonard; Kemfert, Claudia; Kendziorski, Mario; von Hirschhausen, Christian (2021). 100% renewable energy for Germany: Coordinated expansion planning needed — DIW Weekly Report 11 (PDF). Berlin, Germany: German Institute for Economic Research (DIW). ISSN 2568-7697. Retrieved 16 August 2022.
  111. ^ Helistö, Niina; Kiviluoma, Juha; Ikäheimo, Jussi; Rasku, Topi; Rinne, Erkka; O'Dwyer, Ciara; Li, Ran; Flynn, Damian (2019). "Backbone—An Adaptable Energy Systems Modelling Framework". Energies. 12 (17): 3388. doi:10.3390/en12173388.
  112. ^ Personal email from Hans Ravn dated 11 December 2016. This makes Balmorel the first open energy modeling project to go public by quite a margin.
  113. ^ Ravn, Hans F (March 2001). The Balmorel model: theoretical background (PDF). Balmorel Project. Retrieved 12 July 2016.
  114. ^ a b Ravn, Hans F (2 July 2012). The Balmorel model structure — Version 3.02 (September 2011) (PDF). Balmorel Project. Retrieved 12 July 2016.
  115. ^ Grohnheit, Poul Erik; Larsen, Helge V (March 2001). Balmorel: data and calibration — Version 2.05 (PDF). Balmorel Project. Retrieved 12 July 2016.
  116. ^ Ravn, Hans F; et al. (2001). Balmorel: a model for analyses of the electricity and CHP markets in the Baltic Sea region (PDF). Denmark: Balmorel Project. ISBN 87-986969-3-9. Retrieved 12 July 2016.
  117. ^ Karlsson, Kenneth Bernard; Meibom, Peter (2008). "Optimal investment paths for future renewable based energy systems: using the optimisation model Balmorel". International Journal of Hydrogen Energy. 33 (7): 1777–1787. doi:10.1016/j.ijhydene.2008.01.031. S2CID 93243823.
  118. ^ Göransson, Lisa; Karlsson, Sten; Johnsson, Filip (October 2010). "Integration of plug-in hybrid electric vehicles in a regional wind-thermal power system". Energy Policy. 38 (10): 5482–5492. doi:10.1016/j.enpol.2010.04.001.
  119. ^ Göransson, Lisa; Johnsson, Filip (May 2013). "Cost-optimized allocation of wind power investments: a Nordic-German perspective". Wind Energy. 16 (4): 587–604. Bibcode:2013WiEn...16..587G. doi:10.1002/we.1517.
  120. ^ Pfenninger, Stefan (10 March 2016). Calliope documentation — Release 0.3.7 (PDF). Retrieved 11 July 2016. The release version may be updated.
  121. ^ Pfenninger, Stefan; Pickering, Bryn (12 September 2018). "Calliope: a multi-scale energy systems modelling framework". Journal of Open Source Software. 3 (29): 825. Bibcode:2018JOSS....3..825P. doi:10.21105/joss.00825. ISSN 2475-9066.
  122. ^ Pfenninger, Stefan; Keirstead, James (2015). "Comparing concentrating solar and nuclear power as baseload providers using the example of South Africa". Energy. 87: 303–314. doi:10.1016/j.energy.2015.04.077.
  123. ^ Pfenninger, Stefan; Keirstead, James (2015). "Renewables, nuclear, or fossil fuels? Scenarios for Great Britain's power system considering costs, emissions and energy security". Applied Energy. 152: 83–93. doi:10.1016/j.apenergy.2015.04.102.
  124. ^ Pfenninger, Stefan (22 April 2021). Model-based decision-support for the energy transition with Calliope. Delft, The Netherlands: TU Delft. Retrieved 15 April 2022. YouTube. 00:29:23. Summer 2021 Webinar Series of Newcastle University Whole Energy Systems Interest Group (NUWIG).
  125. ^ Tröndle, Tim; Lilliestam, Johan; Marelli, Stefano; Pfenninger, Stefan (16 September 2020). "Trade-offs between geographic scale, cost, and infrastructure requirements for fully renewable electricity in Europe". Joule. 4 (9): 1929–1948. doi:10.1016/j.joule.2020.07.018. ISSN 2542-4785. PMC 7498190. PMID 32999994. Retrieved 25 April 2021. open access
  126. ^ Lombardi, Francesco; Pickering, Bryn; Colombo, Emanuela; Pfenninger, Stefan (14 October 2020). "Policy decision support for renewables deployment through spatially explicit practically optimal alternatives". Joule. 4 (10): 2185–2207. doi:10.1016/j.joule.2020.08.002. ISSN 2542-4785. S2CID 221739436. closed access
  127. ^ DESSTinEE: an energy transfer reference case (PDF). 2015. Retrieved 11 July 2016.
  128. ^ "DESSTinEE". Open Energy Modelling Initiative. Retrieved 3 December 2016. CC-BY icon.svg Material was copied from this source, which is available under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
  129. ^ Boßmann, Tobias; Staffell, Iain (2016). "The shape of future electricity demand: exploring load curves in 2050s Germany and Britain". Energy. 90 (20): 1317–1333. doi:10.1016/j.energy.2015.06.082. hdl:10044/1/25173. S2CID 153704373.
  130. ^ Williams, James H; DeBenedictis, Andrew; Ghanadan, Rebecca; Mahone, Amber; Moore, Jack; Morrow, William R; Price, Snuller; Torn, Margaret S (2012). "The technology path to deep greenhouse gas emissions cuts by 2050: the pivotal role of electricity". Science. 335 (6064): 53–59. Bibcode:2012Sci...335...53W. doi:10.1126/science.1208365. OSTI 1209365. PMID 22116030. S2CID 2999525. See also published correction.
  131. ^ "The US Deep Decarbonization Pathways Project (USDDPP)". New York, NY, USA: Deep Decarbonization Pathways Project (DDPP). Retrieved 6 December 2016.
  132. ^ Drouet, Laurent; Thénié, Julie (2009). ETEM: an energy–technology–environment model to assess urban sustainable development policies — Reference manual version 2.1. Chêne-Bougeries, Switzerland: ORDECSYS (Operations Research Decisions and Systems). This PDF is part of the software bundle.
  133. ^ Drouet, Laurent; Zachary, D (21 May 2010). Economic aspects of the ETEM model — Presentation (PDF). Esch-sur-Alzette, Luxembourg: Resource Centre for Environmental Technologies, Public Research Centre Henri Tudor. Retrieved 12 July 2016.
  134. ^ Spatial simulation and optimization with ETEM-SG: Energy–Technology–Environment-Model for smart cities — Presentation (PDF). Chêne-Bougeries, Switzerland: ORDECSYS. 2015. Retrieved 12 July 2016.
  135. ^ Drouet, Laurent; Haurie, Alain; Labriet, Maryse; Thalmann, Philippe; Vielle, Marc; Viguier, Laurent (2005). "A coupled bottom-up/top-down model for GHG abatement scenarios in the Swiss housing sector". Energy and Environment. pp. 27–61. CiteSeerX 10.1.1.111.8420. doi:10.1007/0-387-25352-1_2. ISBN 0-387-25351-3.
  136. ^ Babonneau, Frédéric; Haurie, Alain; Tarel, Guillaume Jean; Thénié, Julien (June 2012). "Assessing the future of renewable and smart grid technologies in regional energy systems" (PDF). Swiss Journal of Economics and Statistics (SJES). 148 (2): 229–273. doi:10.1007/bf03399367. S2CID 166497864. Retrieved 12 July 2016.
  137. ^ a b Lau, Michael; Ricks, Wilson; Patankar, Neha; Jenkins, Jesse (8 July 2022). Pathways to European independence from Russian natural gas. Princeton, New Jersey, USA: Zero Lab, Princeton University. doi:10.5281/zenodo.6811675. Retrieved 9 July 2022. DOI resolves to latest version. License information from Zenodo landing page. open access
  138. ^ Jenkins, Jesse D; Sepulveda, Nestor A (27 November 2017). Enhanced decision support for a changing electricity landscape: the GenX configurable electricity resource capacity expansion model — An MIT Energy Initiative Working Paper — Revision 1.0 (PDF). Cambridge, Massachusetts, USA: Massachusetts Institute of Technology. Retrieved 6 April 2021. MITEI-WP-2017-10.
  139. ^ "GenX documentation". GenX project. Retrieved 9 June 2021.
  140. ^ Dunning, Iain; Huchette, Joey; Lubin, Miles (2017). "JuMP: a modeling language for mathematical optimization" (PDF). SIAM Review. 59 (2): 295–320. arXiv:1508.01982. doi:10.1137/15M1020575. ISSN 0036-1445. S2CID 1407251. Retrieved 21 May 2018.
  141. ^ "JuMP". Retrieved 9 June 2021.
  142. ^ Jenkins, Jesse (9 June 2021). "GenX open source release". Open Energy Modelling Initiative. Retrieved 9 June 2021. Public mailing list posting.
  143. ^ Schivley, Greg (26 March 2020). Create capacity expansion model inputs with PowerGenome (MP4) (webcast). Open Energy Modelling Initiative (openmod). Retrieved 16 September 2020. MP4 webcast 00:10:55.
  144. ^ Sepulveda, Nestor A; Jenkins, Jesse D; Edington, Aurora; Mallapragada, Dharik S; Lester, Richard K (May 2021). "The design space for long-duration energy storage in decarbonized power systems". Nature Energy. 6 (5): 506–516. Bibcode:2021NatEn...6..506S. doi:10.1038/s41560-021-00796-8. hdl:1721.1/138145.2. ISSN 2058-7546. S2CID 233647828. closed access
  145. ^ Mallapragada, Dharik S; Sepulveda, Nestor A; Jenkins, Jesse D (1 October 2020). "Long-run system value of battery energy storage in future grids with increasing wind and solar generation". Applied Energy. 275: 115390. doi:10.1016/j.apenergy.2020.115390. ISSN 0306-2619. S2CID 224962497. closed access
  146. ^ Sepulveda, Nestor A; Jenkins, Jesse D; de Sisternes, Fernando J; Lester, Richard K (21 November 2018). "The role of firm low-carbon resources in deep decarbonization of power generation". Joule. 2 (11): 2403–2420. doi:10.1016/j.joule.2018.08.006. S2CID 169890352.
  147. ^ Rudnick García, Iván (September 2019). Decarbonizing the Indian power sector by 2037: evaluating different pathways that meet long-term emissions targets (PDF). Cambridge, Massachusetts, USA: Massachusetts institute Of Technology (MIT). Retrieved 9 June 2021.
  148. ^ Bompard, E; Botterud, A; Corgnati, S; Huang, T; Jafari, M; Leone, P; Mauro, S; Montesano, G; Papa, C; Profumo, F (1 November 2020). "An electricity triangle for energy transition: application to Italy". Applied Energy. 277: 115525. doi:10.1016/j.apenergy.2020.115525. ISSN 0306-2619. S2CID 225025268. closed access
  149. ^ Oyler, Anthony Fratto; Parsons, John E (May 2020). The value of pumped hydro storage for deep decarbonization of the Spanish grid — Working paper CEEPR WP 2020-007 (PDF). Cambridge, Massachusetts, USA: MIT Center for Energy and Environmental Policy Research (CEEPR). Retrieved 9 June 2021.
  150. ^ Anon (8 July 2022). FAQ: European independence from Russian natural gas. Princeton, New Jersey, USA: Zero Lab, Princeton University. doi:10.5281/zenodo.6811675. Retrieved 9 July 2022. DOI resolves to latest version. License information from Zenodo landing page. open access
  151. ^ Juanpera, M; Blechinger, P; Ferrer-Martí, L; Hoffmann, MM; Pastor, R (1 November 2020). "Multicriteria-based methodology for the design of rural electrification systems: a case study in Nigeria" (PDF). Renewable and Sustainable Energy Reviews. 133: 110243. doi:10.1016/j.rser.2020.110243. hdl:2117/328448. ISSN 1364-0321. S2CID 221817141. Retrieved 24 May 2022. Linked URL is a preprint.
  152. ^ Muschner, Christoph (2020). An Open Source Energy Modelling Framework Comparison of OSeMOSYS and oemof (PDF) (MSc). Stockholm: KTH. Retrieved 3 November 2020.
  153. ^ Lavigne, Denis (2017). "OSeMOSYS energy modeling using an extended UTOPIA model" (PDF). Universal Journal of Educational Research. 5 (1): 162–169. doi:10.13189/ujer.2017.050120. Retrieved 12 January 2017.
  154. ^ Moksnes, Nandi; Welsch, Manuel; Gardumi, Francesco; Shivakumar, Abhishek; Broad, Oliver; Howells, Mark; Taliotis, Constantinos; Sridharan, Vignesh (November 2015). 2015 OSeMOSYS user manual — Working Paper Series DESA/15/11 (PDF). Stockholm, Sweden: Division of Energy Systems Analysis, KTH Royal Institute of Technology. Retrieved 12 July 2016. The version referred to in the manual is OSeMOSYS_2013_05_10.
  155. ^ Volodina, Victoria; Sonenberg, Nikki; Smith, Jim Q; Challenor, Peter G; Dent, Chris J; Wynn, Henry P (2022). "Propagating uncertainty in a network of energy models". arXiv:2201.09624 [stat.AP]. Gaussian process emulators can be used to approximate the behaviour of complex, computationally intensive models and used to generate predictions together with a measure of uncertainty about the predicted model output.
  156. ^ Warren, Peter (23 September 2011). Incorporating behavioural complexity into the Open Source Energy Modelling System using intangible costs and benefits. People and Buildings. London, UK. Retrieved 17 June 2016.
  157. ^ Welsch, Manuel; Howells, Mark; Bazilian, Morgan; DeCarolis, Joseph F; Hermann, Sebastian; Rogner, Holger H (2012). "Modelling elements of smart grids: enhancing the OSeMOSYS (Open Source Energy Modelling System) code". Energy. 46 (1): 337–350. doi:10.1016/j.energy.2012.08.017.
  158. ^ Fuso Nerini, Francesco; Dargaville, Roger; Howells, Mark; Bazilian, Morgan (1 January 2015). "Estimating the cost of energy access: the case of the village of Suro Craic in Timor Leste". Energy. 79: 385–397. doi:10.1016/j.energy.2014.11.025. ISSN 0360-5442.
  159. ^ Fragnière, Emmanuel; Kanala, Roman; Moresino, Francesco; Reveiu, Adriana; Smeureanu, Ion (2016). "Coupling techno-economic energy models with behavioral approaches" (PDF). Operational Research (2): 1–15. doi:10.1007/s12351-016-0246-9. S2CID 44593439.
  160. ^ Fattori, Fabrizio; Albini, Davide; Anglani, Norma (2016). "Proposing an open-source model for unconventional participation to energy planning". Energy Research and Social Science. 15: 12–33. doi:10.1016/j.erss.2016.02.005.
  161. ^ Niet, T; Lyseng, B; English, J; Keller, V; Palmer-Wilson, K; Moazzen, I; Robertson, B; Wild, P; Rowe, A (June 2017). "Hedging the risk of increased emissions in long term energy planning". Energy Strategy Reviews. 16: 1–12. doi:10.1016/j.esr.2017.02.001. ISSN 2211-467X.
  162. ^ Taliotis, Constantinos; Rogner, Holger; Ressl, Stephan; Howells, Mark; Gardumi, Francesco (August 2017). "Natural gas in Cyprus: the need for consolidated planning". Energy Policy. 107: 197–209. doi:10.1016/j.enpol.2017.04.047. ISSN 0301-4215.
  163. ^ a b Pappis, Ioannis; Sridharan, Vignesh; Howells, Mark; Medarac, Hrvoje; Kougias, Ioannis; Sánchez, Rocío González; Shivakumar, Abhishek; Usher, Will (March 2022). "The effects of climate change mitigation strategies on the energy system of Africa and its associated water footprint". Environmental Research Letters. 17 (4): 044048. Bibcode:2022ERL....17d4048P. doi:10.1088/1748-9326/ac5ede. ISSN 1748-9326. S2CID 247542994. Retrieved 1 April 2022. open access
  164. ^ Taliotis, Constantinos; Shivakumar, Abhishek; Ramos, Eunice; Howells, Mark; Mentis, Dimitris; Sridharan, Vignesh; Broad, Oliver; Mofor, Linus (April 2016). "An indicative analysis of investment opportunities in the African electricity supply sector — Using TEMBA (The Electricity Model Base for Africa)". Energy for Sustainable Development. 31: 50–66. doi:10.1016/j.esd.2015.12.001. ISSN 0973-0826.
  165. ^ "The Electricity Model Base for Africa (TEMBA)". OSeMOSYS. Retrieved 13 January 2017.
  166. ^ Moura, Gustavo; Howells, Mark (August 2015). SAMBA: the open source South American model base: a Brazilian perspective on long term power systems investment and integration — Working paper dESA /5/8/11. Sockholm, Sweden: Royal Institute of Technology (KTH). doi:10.13140/RG.2.1.3038.7042. Available for download from ResearchGate.
  167. ^ "South American Model Base (SAMBA)". OSeMOSYS. Retrieved 13 January 2017.
  168. ^ "Global CLEWS (Climate, Land, Energy, and Water Strategies)". New York, USA: Division for Sustainable Development, Department of Economic and Social Affairs (DESA), United Nations. Retrieved 13 January 2017.
  169. ^ de Strasser, Lucia; Mentis, Dimitris; Ramos, Eunice; Sridharan, Vignesh; Welsch, Manuel; Howells, Mark; Destouni, Gia; Levi, Lea; Stec, Stephen; Roo, Ad de (2016). Reconciling resource uses in transboundary basins: assessment of the water-food-energy-ecosystems nexus in the Sava River Basin (PDF). Geneva, Switzerland: United Nations Economic Commission for Europe (UNECE). Retrieved 17 March 2017.
  170. ^ Reconciling resource uses in transboundary basins: assessment of the water-food-energy-ecosystems nexus in the Syr Darya River basin (PDF). United Nations Economic Commission for Europe (UNECE). 2016. Retrieved 13 January 2017.
  171. ^ "Mauritius CLEWS (Climate, Land, Energy, and Water Strategies)". New York, USA: Division for Sustainable Development, Department of Economic and Social Affairs (DESA), United Nations. Retrieved 13 January 2017.
  172. ^ Rocco, Matteo V; Fumagalli, Elena; Vigone, Chiara; Miserocchi, Ambrogio; Colombo, Emanuela (1 March 2021). "Enhancing energy models with geo-spatial data for the analysis of future electrification pathways: the case of Tanzania". Energy Strategy Reviews. 34: 100614. doi:10.1016/j.esr.2020.100614. ISSN 2211-467X. S2CID 233779581.
  173. ^ a b c Niet, Taco; Shivakumar, Abhishek; Gardumi, Francesco; Usher, Will; Williams, Eric; Howells, Mark (1 May 2021). "Developing a community of practice around an open source energy modelling tool". Energy Strategy Reviews. 35: 100650. doi:10.1016/j.esr.2021.100650. ISSN 2211-467X. S2CID 233562043.
  174. ^ Olsson, John Mogren; Gardumi, Francesco (1 November 2021). "Modelling least cost electricity system scenarios for Bangladesh using OSeMOSYS". Energy Strategy Reviews. 38: 100705. doi:10.1016/j.esr.2021.100705. ISSN 2211-467X. S2CID 239658794.
  175. ^ Mekonnen, Tewodros Walle; Teferi, Solomon Tesfamariam; Kebede, Fitsum Salehu; Anandarajah, Gabrial (January 2022). "Assessment of impacts of climate change on hydropower-dominated power system — the case of Ethiopia". Applied Sciences. 12 (4): 1954. doi:10.3390/app12041954. ISSN 2076-3417. Retrieved 13 February 2022. open access
  176. ^ Howells, Mark; Boehlert, Brent; Benitez, Pablo César (January 2021). "Potential climate change risks to meeting Zimbabwe's NDC goals and how to become resilient". Energies. 14 (18): 5827. doi:10.3390/en14185827. ISSN 1996-1073. open access
  177. ^ Fonseca, Roberto Heredia; Gardumi, Francesco (14 February 2022). "Assessing the impact of applying individual discount rates in power system expansion of Ecuador using OSeMOSYS". International Journal of Sustainable Energy Planning and Management. 33: 35–52. doi:10.5278/ijsepm.6820. ISSN 2246-2929. Retrieved 15 February 2022. open access
  178. ^ Dallmann, Christoph; Schmidt, Matthew; Möst, Dominik (1 May 2022). "Between path dependencies and renewable energy potentials: a case study of the Egyptian power system". Energy Strategy Reviews. 41: 100848. doi:10.1016/j.esr.2022.100848. ISSN 2211-467X. S2CID 248757578. Retrieved 24 May 2022.
  179. ^ Quevedo, Jarrizon; Moya, Idalberto Herrera (16 June 2022). "Modeling of the Dominican Republic energy systems with OSeMOSYS to assess alternative scenarios for the expansion of renewable energy sources". Energy Nexus. 6: 100075. doi:10.1016/j.nexus.2022.100075. ISSN 2772-4271. S2CID 248483022. open access
  180. ^ Howells, Mark; Shivakumar, Abhishek; Pelakaukas, Martynas; Allmulla, Yousef; Gritsevskyi, Andrii (17 February 2016). Model Management Interface (MoManI) for OSeMOSYS: supporting development investments and INDCs — Presentation (PDF). Stockholm, Sweden and New York, USA: KTH Royal Institute of Technology and UN Department of Economic and Social Affairs (DESA). Retrieved 17 January 2017.
  181. ^ "Atlantis — Integrated Systems Analysis of Energy". United Nations. New York, USA. Retrieved 16 January 2017.
  182. ^ United Nations Department of Economic and Social Affairs (DESA). "Atlantis". GitHub. Retrieved 16 January 2017.
  183. ^ Climate Compatible Growth. "clicSAND". GitHub. Retrieved 17 May 2021.
  184. ^ Cannone, Carla; Allington, Lucy; Wet, Nicki de; Shivakumar, Abhishek; Goynes, Philip; Valderrama, Cesar; Kapor, Vedran; Wright, Jarrad; Yeganyan, Rudolf; Tan, Naomi; To, Long Seng; Harrison, John; Howells, Mark (12 February 2022). clicSAND for OSeMOSYS: a user-friendly interface using open-source optimisation software for energy system modelling analysis (PDF). doi:10.21203/rs.3.rs-1338761/v1. S2CID 246731625. Retrieved 12 February 2022. Pre‑print. open access
  185. ^ OSeMOSYS (nd). "otoole: OSeMOSYS tools for energy work: a Python toolkit to support use of OSeMOSYS". Retrieved 26 May 2021.
  186. ^ Allington, Lucy; Cannone, Carla; Pappis, Ioannis; Barron, Karla Cervantes; Usher, Will; Pye, Steve; Brown, Edward; Howells, Mark; Walker, Miriam Zachau; Ahsan, Aniq; Charbonnier, Flora; Halloran, Claire; Hirmer, Stephanie; Cronin, Jennifer; Taliotis, Constantinos; Sundin, Caroline; Sridharan, Vignesh; Ramos, Eunice; Brinkerink, Maarten; Deane, Paul; Gritsevskyi, Andrii; Moura, Gustavo; Rouget, Arnaud; Wogan, David; Barcelona, Edito; Niet, Taco; Rogner, Holger; Bock, Franziska; Quirós-Tortós, Jairo; Angulo-Paniagua, Jam; Krishnamurthy, Satheesh; Harrison, John; To, Long Seng (12 February 2022). "Selected 'starter kit' energy system modelling data for selected countries in Africa, East Asia, and South America (#CCG, 2021)" (PDF). Data in Brief. 42: 108021. doi:10.21203/rs.3.rs-1178306/v1. PMC 8943422. PMID 35341031. S2CID 246709496. Retrieved 12 February 2022. Pre‑print. open access
  187. ^ OSeMOSYS (2018). "The open source energy model base for the European Union (OSeMBE)". OSeMOSYS. Stockholm, Sweden. Retrieved 30 April 2018.
  188. ^ Beltramo, Agnese (27 April 2018). "first OSeMBE EU-28 model released". openmod-initiative@googlegroups.com (Mailing list). Retrieved 30 April 2018.
  189. ^ "REEEM – Energy Systems Modelling Project". Modelling the transformation of the European Energy System. Retrieved 16 February 2017.
  190. ^ a b c Brown, Tom; Hörsch, Jonas; Schlachtberger, David (16 January 2018). "PyPSA: Python for Power System Analysis". Journal of Open Research Software. 6 (1): 4. arXiv:1707.09913. doi:10.5334/jors.188. ISSN 2049-9647. S2CID 67101943.
  191. ^ Brown, Tom; Schlachtberger, David; Kies, Alexander; Schramm, Stefan; Greiner, Martin (1 October 2018). "Synergies of sector coupling and transmission reinforcement in a cost-optimised, highly renewable European energy system". Energy. 160: 720–739. arXiv:1801.05290. Bibcode:2018arXiv180105290B. doi:10.1016/j.energy.2018.06.222. ISSN 0360-5442. S2CID 55251011. closed access Content identical arXiv postprint.
  192. ^ Gorenstein Dedecca, João; Hakvoort, Rudi A; Herder, Paulien M (15 April 2017). "Transmission expansion simulation for the European Northern Seas offshore grid". Energy. 125: 805–824. doi:10.1016/j.energy.2017.02.111. ISSN 0360-5442.
  193. ^ Zeyen, Elisabeth; Victoria, Marta; Brown, Tom (15 October 2020). PAC scenarios with PyPSA‑Eur‑Sec — Presentation (PDF). Retrieved 25 February 2021. Presentation to 4th PAC scenario workshop. open access
  194. ^ Victoria, Marta; Zhu, Kun; Brown, Tom; Andresen, Gorm B; Greiner, Martin (4 December 2020). "Early decarbonisation of the European energy system pays off". Nature Communications. 11 (1): 6223. arXiv:2004.11009. Bibcode:2020NatCo..11.6223V. doi:10.1038/s41467-020-20015-4. ISSN 2041-1723. PMC 7718908. PMID 33277493.
  195. ^ PyPSA contributors. "Meet constant demand from wind+solar+storage with zero-direct-emissions using your own assumptions". PyPSA project. Retrieved 7 January 2019.{{cite web}}: CS1 maint: uses authors parameter (link) Caveats apply.
  196. ^ PyPSA constributors. "Online optimisation tool for wind+solar+storage systems: PyPSA/whobs-server". PyPSA Project. Retrieved 7 January 2019. GitHub repository.
  197. ^ Brown, Tom (5 November 2019). "model.energy: live optimisation of electricity systems". Open Energy Modelling Initiative mailing list. Retrieved 23 February 2021. New URL: model.energy
  198. ^ Brown, Tom. "Build your own zero-emission electricity supply". Retrieved 22 July 2022. Web interface to highly simplified PyPSA model.
  199. ^ Brown, Tom (14 September 2021). "PyPSA-Server: customisable energy scenarios, solved while you wait". Open Energy Modelling Initiative mailing list. Retrieved 23 February 2021.
  200. ^ PyPSA project (September 2021). "PyPSA-Eur-Sec optimisation server". Technical University of Berlin. Berlin, Germany. Retrieved 28 September 2021.
  201. ^ Brown, Tom; Victoria, Marta; Neumann, Fabian; Zeyen, Lisa; Frysztacki, Martha (27 October 2021). Can a hydrogen network replace electricity transmission network expansion in a climate-neutral scenario for Europe? (PDF). Berlin, Germany: Technical University of Berlin. Retrieved 27 October 2021. Presentation to the EMP‑E 2021 conference. open access
  202. ^ Berus, Patryk (8 December 2021). Failing to switch from coal to wind and solar could cost every Polish family 500 PLN per year by 2030, report — Press release. Warsaw, Poland: Instrat Foundation.
  203. ^ Czyżak, Paweł; Wrona, Adrianna; Borkowski, Michał (December 2021). The missing element: energy security considerations — Instrat Policy Paper 09/2021 (PDF). Warsaw, Poland: Instrat Foundation. ISBN 978-83-962333-3-2. Retrieved 8 December 2021. open access
  204. ^ Czyżak, Paweł; Sikorski, Maciej; Wrona, Adrianna (June 2021). What's next after coal? RES potential in Poland — Instrat Policy Paper 06/2021 (PDF). Warsaw, Poland: Instrat Foundation. ISBN 978-83-959296-9-4. Retrieved 20 December 2021. open access
  205. ^ PyPSA meets Africa (July 2021). PyPSA meets Africa — Policy brief 07/2021. PyPSA meets Africa.
  206. ^ PyPSA meets Africa. "PyPSA meets Africa: an open source energy system model initiative for Africa". PyPSA meets Africa. Retrieved 7 August 2021.
  207. ^ PyPSA-meets-Africa; CPEEL (7 July 2022). Open data, open source sector‑coupled models, and open solvers to fast‑track the Earth energy transition together — Webinar. PyPSA-meets-Africa and Centre for Petroleum Energy Economics and Law (CPEEL), University of Ibadan, Nigeria. Retrieved 12 July 2022. YouTube video. Duration 00:35:15. open access
  208. ^ Fioriti, Davide; Schumm, Leon (7 July 2022). Open data, open source sector‑coupled models, and open solvers to fast‑track the Earth energy transition together — Presentation. PyPSA-meets-Africa. Retrieved 18 July 2022. open access
  209. ^ Parzen, Maximilian; Abdel-Khalek, Hazem; Fedorova, Ekaterina; Mahmood, Matin; Frysztacki, Martha Maria; Hampp, Johannes; Franken, Lukas; Schumm, Leon; Neumann, Fabian; Poli, Davide; Kiprakis, Aristides; Fioriti, Davide (13 September 2022). PyPSA-Earth. A new global open energy system optimization model demonstrated in Africa — Preprint. doi:10.48550/arXiv.2209.04663. Retrieved 13 September 2022. open access
  210. ^ Hunter, Kevin; Sreepathi, Sarat; DeCarolis, Joseph F (2013). "Modeling for insight using Tools for Energy Model Optimization and Analysis (TEMOA)" (PDF). Energy Economics. 40: 339–349. doi:10.1016/j.eneco.2013.07.014. Retrieved 8 July 2016.
  211. ^ DeCarolis, Joseph; Hunter, Kevin; Sreepathi, Sarat (2010). The TEMOA project: Tools for Energy Model Optimization and Analysis (PDF). Raleigh, North Carolina, USA: Department of Civil, Construction, and Environmental Engineering, North Carolina State University. Retrieved 17 June 2016.
  212. ^ DeCarolis, Joe (24 December 2020). An Open Energy Outlook for the United States powered by TEMOA. Raleigh, North Carolina, USA: NC State University. Retrieved 26 February 2021. YouTube video, duration 00:15:16. open access
  213. ^ DeCarolis, Joe (22 September 2021). Temoa on the cloud. Raleigh, North Carolina, USA: North Carolina State University. Retrieved 28 September 2021. YouTube video, duration 00:10:03.
  214. ^ "TEMOA cloud model". Retrieved 1 October 2021. User registration is required.
  215. ^ a b Wulff, Niklas; Steck, Felix; Gils, Hans Christian; Hoyer-Klick, Carsten; van den Adel, Bent; Anderson, John E (March 2020). "Comparing power-system and user-oriented battery electric vehicle charging representation and its implications on energy system modeling". Energies. 13 (5): 1093. doi:10.3390/en13051093. ISSN 1996-1073.
  216. ^ Wulff, Niklas; Miorelli, Fabia; Gils, Hans Christian; Jochem, Patrick (July 2021). "Vehicle Energy Consumption in Python (VencoPy): presenting and demonstrating an open-source tool to calculate electric vehicle charging flexibility". Energies. 14 (14): 4349. doi:10.3390/en14144349. ISSN 1996-1073.
  217. ^ Costales, Diego Luca de Tena (April 2014). Large scale renewable power integration with electric vehicles: long term analysis for Germany with a renewable based power supply (PDF) (PhD). Stuttgart, Germany: Stuttgart University. Retrieved 8 November 2021. Describes a spreadsheet prototype.
  218. ^ GAMS — Commercial Price List (PDF). 15 March 2016. Retrieved 11 July 2016.
  219. ^ King, David L; Boyson, William E; Kratochvill, Jay A (2004). Photovoltaic array performance model — Sandia report SAND2004-3535 (PDF). USA: Sandia Corporation. Retrieved 17 June 2016.
  220. ^ Holmgren, William F; Hansen, Clifford W; Mikofski, Mark A (2018). "pvlib python: a python package for modeling solar energy systems" (PDF). Journal of Open Source Software. 3 (29): 884. Bibcode:2018JOSS....3..884F. doi:10.21105/joss.00884. ISSN 2475-9066. S2CID 240160353. Retrieved 27 September 2021.
  221. ^ Tjaden, Tjarko; Hoops, Hauke (22 September 2021). "hplib: heat pump library". Zenodo. doi:10.5281/zenodo.5521598. Retrieved 27 September 2021.
  222. ^ "windpowerlib". GitHub. 9 March 2021. Retrieved 3 January 2022.
  223. ^ "hydropowerlib". GitHub. 11 June 2019. Retrieved 3 January 2022.
  224. ^ Guan, Ziming; Philpott, Andy (2011). Modelling summary for the paper "Production inefficiency of electricity markets with hydro generation" (PDF). Auckland, New Zealand: Electric Power Optimization Centre (EPOC), University of Auckland. Retrieved 17 June 2016.
  225. ^ "Clp homepage". Retrieved 23 April 2017.
  226. ^ "COIN-OR linear programming solver". Retrieved 23 April 2017.
  227. ^ Brown, Tom. "PyPSA-Eur-Sec optimization server". Retrieved 22 July 2022. A web interface to PyPsa‑Eur‑Sec model.
  228. ^ Koch, Thorsten; Achterberg, Tobias; Andersen, Erling; Bastert, Oliver; Berthold, Timo; Bixby, Robert E; Danna, Emilie; Gamrath, Gerald; Gleixner, Ambros M (2011). "MIPLIB 2010: mixed integer programming library version 5". Mathematical Programming Computation. 3 (2): 103–163. doi:10.1007/s12532-011-0025-9. S2CID 45013649. Retrieved 17 June 2016.
  229. ^ Frangioni, Antonio; Charousset, Sandrine (28 October 2021). plan4res SMS++: an open modelling library for evaluating long term electricity system costs and flexibilities (PDF). Italy: University of Pisa. Retrieved 10 November 2021. EMP‑E 2021 presentation. open access

Further information[edit]

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

External links[edit]

Modeling efforts by region