Source-linked AI summary
The Open Energy Modelling Framework (oemof) - A new approach to facilitate open science in energy system modelling
Simon Hilpert, Cord Kaldemeyer, Uwe Krien, Stefan Günther, Clemens Wingenbach, Guido Plessmann
TL;DR
Increasingly complex, cross-sectoral energy systems need flexible modelling tools, while energy modelling faces demands for greater openness and transparency. The paper presents oemof, an open, collaboratively developed framework using a generic graph-based representation, and reports applications across modelling approaches and scales.
Problem
Increasing cross-sectoral and decentralized complexity requires sophisticated flexible models, while energy modelling has been criticized for opaque logic and assumptions.
Method
oemof combines a generic graph-based energy-system representation with open-source software, collaborative development, documentation, and multiple modelling tools.
Results
oemof has been applied across electricity markets, district heating, and sector-coupled regional systems, from distributed or urban systems to national scale.
Takeaways & Limitations
oemof provides a multi-purpose environment for strategic energy analysis and planning while supporting transparency and participation in model development.
Takeaways & Limitations
New users require effort to learn how to build an oemof-based application.
Abstract
from arXiv · showhide
Energy system models have become indispensable tools for planning future energy systems by providing insights into different development trajectories. However, sustainable systems with high shares of renewable energy are characterized by growing cross-sectoral interdependencies and decentralized structures. To capture important properties of increasingly complex energy systems, sophisticated and flexible modelling tools are needed. At the same time, open science is becoming increasingly important in energy system modelling. This paper presents the Open Energy Modelling Framework (oemof) as a novel approach to energy system modelling, representation and analysis. The framework provides a toolbox to construct comprehensive energy system models and has been published open source under a free licence. Through collaborative development based on open processes, the framework supports a maximum level of participation, transparency and open science principles in energy system modelling. Based on a generic graph-based description of energy systems, it is well-suited to flexibly model complex cross-sectoral systems and incorporate various modelling approaches. This makes the framework a multi-purpose modelling environment for modelling and analyzing different systems at scales ranging from urban to transnational.
1. Introduction
Future low-carbon energy systems require alternative trajectories and quantitative modelling to support decisions, but increasing cross-sectoral and decentralized complexity demands flexible tools. Open science is also becoming central to making energy models more transparent, reproducible, and reusable, motivating oemof.
- Future sustainable energy systems require alternative trajectories for scientific discussion and decision support at transnational and local levels.
- Growing technical, economic, and socioeconomic complexity arises from increasingly cross-sectoral and decentralized energy-system structures.
- Computer-based modelling provides a quantitative basis for analysing complex systems and informing related discussion and decision processes.
- Energy-system models span applications from transnational equilibrium studies to detailed local infrastructure planning, depending on the research question.
- Closed model logic and assumptions have prompted greater openness, motivated by scrutiny, reproducibility, re-use, efficiency, and transparency.
- The paper presents oemof as an open framework intended to foster open science in energy modelling and analysis.
2. Scientific contribution
The paper positions oemof within energy-modelling software and distinguishes it through open collaborative processes, a generic graph-based data model, and a multi-model toolbox. These features separate system representation from computation and support diverse modelling applications.
- 2.1. Overview of modelling landscape: Energy-modelling software includes proprietary first-generation tools and diverse open-source second-generation models serving regional, technical, simulation, and planning needs.
- 2.2. Comparison to other software: The framework comparison evaluates tools by open-science suitability, technical concept, and modelling functionality.
- 2.2. Comparison to other software: Open licences support reproducibility, transparency, source-code reuse, and collaborative improvement by allowing software access, inspection, modification, and redistribution.
- 2.2. Comparison to other software: oemof differentiates itself through collaborative development, a generic data model, and a multi-model toolbox compared with selected existing tools.
- 2.3. Unique framework features: Its open development process seeks transparent review and decision-making beyond merely publishing source code.
- 2.3. Unique framework features: The generic graph-based model separates energy-system topology from mathematical calculation and represents systems from high-level abstractions to detailed power plants.
- 2.3. Unique framework features: oemof combines model generators for economic dispatch, investment, and unit commitment with tools for renewable-feed-in and regional heat-demand simulation.
- 2.3. Unique framework features: Open meetings, code review, web conferences, platforms, and pull requests enable participation and form a basis for open science in energy modelling.
3. Concept, architecture and implementation
oemof represents energy systems as generic graphs and separates their topological description from subsequent computations. Its layered, collaboratively documented architecture supports multiple modelling approaches and extensible optimization applications.
- Underlying concept: The framework separates an energy system’s topological description from its computation, enabling graph algorithms, exploratory analyses, and alternative modelling methods.The representation can support models and model generators using different approaches and programming languages.
- Underlying concept: oemof represents an energy system as a bipartite graph of buses and components connected by edges.Buses connect only to components, while components represent producers, consumers, or processes.
- Underlying concept: oemof’s specific graph structure is implemented object-orientedly as a representation intended for all types of energy systems.Calculations based on specific models are derived from this common network representation.
- Mathematical description: The solph-library: The solph library generates mixed-integer linear optimization problems from predefined component classes with associated variables, objective terms, and constraints.The resulting formulation depends on user-defined object attributes and supports time-dependent costs and weights.
- Project architecture: oemof organizes libraries into four dependency- and responsibility-based layers, keeping the core graph API free from energy-system-specific logic.Higher layers can apply cost, power-flow, simulation, or optimization methods to uniformly represented energy-system graphs.
- Documentation and implementation: The project uses documentation across four levels to support users and developers, although maintaining consistent documentation across releases requires substantial effort.The documentation records formulas and helps external users understand model logic and assumptions.
4. Usage: Applications
oemof applications combine modular framework libraries and external tools to model, optimize, process, and analyze energy systems. The examples demonstrate flexible workflows spanning different technologies, sectors, regions, and model scales.
- Application development: oemof is used as a library toolbox rather than a standalone executable, with applications combining selected framework libraries according to scope and purpose.Specific functionalities can be substituted to adapt the modelling process to different tasks.
- System optimization: The optimization workflow generates renewable feed-in and demand data, uses them as exogenous parameters in solph, and processes results with outputlib.The workflow links feedinlib, demandlib, solph, and outputlib in sequence.
- System optimization: solph supports mixed-integer linear models whose graph representations are translated into constraints, while users can add constraints or custom components.These use cases can be applied separately or combined within one model.
- System optimization: Model type is determined by parametrization, allowing applications to shift between economic dispatch, investment, and unit commitment formulations by exchanging components.For example, fixed-capacity storage can be replaced by storage with cost-determined variable capacity.
- Existing applications: Existing applications cover electricity markets, participative regional climate scenarios, detailed heating optimization, and combined local and large-scale energy-system models.The examples include a database-oriented representation, public-server deployment, and models combining different scales.
- Existing applications: The applications show that existing functionalities and combinations of models can be adapted with little effort to create tools for specific research purposes.The paper presents this flexibility as supporting challenging research questions within a single framework.
5. Conclusion
oemof combines an open, collaborative development process with a generic graph-based technical foundation for energy-system modelling. Its applications span multiple institutions and scales, while learning to build applications remains an effort for new users.
- Conclusion: oemof is presented as a contribution designed to support transparency, participation, and open science through collaborative development.The paper identifies the project’s open development process as complementary to its technical features.
- Conclusion: Its generic graph-based foundation separates energy-system description from subsequent computations and supports a common structure developed across institutions.The foundation is implemented with object-oriented methods in Python.
- Conclusion: The framework has been applied across institutions to electricity-market, district-heating unit-commitment, and sector-coupled regional models from distributed or urban to national scales.These applications make oemof a multi-purpose environment for strategic energy analysis and planning.
- Conclusion: New users must invest effort to learn how to build an oemof-based application.The authors connect this learning effort with the broader reasons they give for choosing oemof.