---
title: "Open energy system models"
chunk: 12/16
source: "https://en.wikipedia.org/wiki/Open_energy_system_models"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:49:30.157219+00:00"
instance: "kb-cron"
---

GENeSYS‑MOD couples the demand sectors covering electricity, buildings, industry, and transport and finds the cost-optimal investment into conventional and renewable energy generation, storage, and infrastructure.  The research focus is on long-term system development and pathway analysis.
The model was first used to analyze decarbonization scenarios at the global level, broken down into ten regions.  However, the framework is highly flexible, allowing for calculations at various levels of detail, from individual households to global aggregations, depending on the desired research question and availability of input data.
A 2019 study examined the low‑carbon transition of the European energy system and specifically the problem of stranded assets under a range of scenarios.  It found that up to €200 billion in fossil-fueled capacities could be stranded by 2035 unless stronger policy signals are able to address short‑term planning biases.  Another 2019 study evaluates China's energy system transformation, highlighting the need to reduce coal consumption by 60% by 2050 to meet global climate targets.  Renewable energies, and in particular photovoltaics and onshore wind, emerge as cost-effective solutions, but overcoming local resistance and increasing stakeholder engagement remain crucial for success.  A 2021 study investigates the European Green Deal goal of achieving 100% greenhouse gas reductions by 2050, examining the interplay of technological developments, policy imperatives, and societal attitudes.  The study presents four future storylines that highlight the critical contribution of high rates of electrification combined with near‑term technology deployment to achieve the necessarily rapid decarbonization.

=== GenX ===

GenX is multi‑commodity sector capacity expansion model originally developed by researchers in the United States. The framework is written in Julia and deploys the JuMP library for building the underlying optimization problem. GenX through JuMP can utilize various open source (including CBC/CLP) and commercial optimization solvers (including CPLEX). In June 2021, the project launched as an active open source project and test suites are available to assist onboarding.
In parallel, the PowerGenome project is designed to provide GenX with a comprehensive current state dataset of the United States electricity system. That dataset can then be used as a springboard to develop future scenarios.
GenX has been used to explore long-term storage options in systems with high renewables shares, to explore the value of 'firm' low-carbon power generation options, and a variety of other applications. While North America remains a key focus, the software has been applied to problems in India, Italy, and Spain.
GenX was deployed in a 2021 case study with Louisville Gas and Electric and Kentucky Utilities that showed that stakeholder-driven modeling utilizing open‑source tools and public data can contribute productively to utility‑led analysis and planning.
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. 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.

=== 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. A masters project in 2020 compared oemof and OSeMOSYS.

=== OSeMOSYS ===

OSeMOSYS stands for Open Source Energy Modelling System. OSeMOSYS is intended for national and regional policy development and uses an intertemporal optimization framework. The model posits a single socially motivated operator/investor with perfect foresight. The OSeMOSYS project is a community endeavor, supported by the division of Energy Systems, KTH Royal Institute of Technology, Stockholm, Sweden. The project maintains a website providing background. The project also offers several active internet forums on Google Groups. OSeMOSYS was originally written in MathProg, a high-level mathematical programming language. It was subsequently reimplemented in GAMS and Python and all three codebases are now maintained. The project also provides a test model called UTOPIA. A manual is available.

OSeMOSYS provides a framework for the analysis of energy systems over the medium (10–15 years) and long term (50–100 years). OSeMOSYS uses pure linear optimization, with the option of mixed integer programming for the treatment of, for instance, discrete power plant capacity expansions. It covers most energy sectors, including heat, electricity, and transport. OSeMOSYS is driven by exogenously defined energy services demands. These are then met through a set of technologies which draw on a set of resources, both characterized by their potentials and costs. These resources are not limited to energy commodities and may include, for example, water and land-use. This enables OSeMOSYS to be applied in domains other than energy, such as water systems. Technical constraints, economic restrictions, and/or environmental targets may also be imposed to reflect policy considerations. OSeMOSYS is available in extended and compact MathProg formulations, either of which should give identical results. In its extended version, OSeMOSYS comprises a little more than 400 lines of code. OSeMOSYS has been used as a base for constructing reduced models of energy systems.