The Open Energy Modelling Framework (oemof) - A new approach to facilitate open science in energy system modelling (1808.08070v1)

Abstract: 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.

The Open Energy Modelling Framework (oemof): Enhancing Transparency and Collaboration in Energy System Modelling

The paper "The Open Energy Modelling Framework (oemof) - A new approach to facilitate open science in energy system modelling" presents a sophisticated framework for constructing and analyzing energy system models. The authors propose oemof as a flexible, open-source toolkit that supports collaborative development and fosters open science principles. This paper focuses on how oemof addresses the challenges posed by increasingly complex and decentralized energy systems aiming for sustainable solutions with high renewable energy share.

oemof is distinguished by its graph-based representation of energy systems, allowing for a separation between the topological description of the system and its computational analysis. The framework is built around an object-oriented programming approach using Python, ensuring wide accessibility and leveraging the abundance of scientific computing libraries available in this language environment. This aspect of oemof aligns with the open science principles, supporting transparency and reproducibility of research, as well as fostering a collaborative community of developers.

Scientific Contribution of oemof

The framework stands out from conventional energy system modelling tools due to several unique features:

1. Collaborative Development: oemof champions an open development process, encouraging contributions from diverse research backgrounds while implementing a clear governance structure for collaborative development. This enhances the quality and flexibility of the framework, supporting continuous improvements.
2. Generic Data Model: The generic graph-based data model is a distinctive feature that facilitates diverse modelling approaches, including optimization, simulation, and power flow calculations. This model enables coherent representation and analysis across multiple energy sectors, such as electricity, heat, and transport.
3. Modularity and Flexibility: Designed as a multi-purpose toolbox, oemof encompasses various libraries allowing users to construct specific models tailored to their research queries. The modular structure encourages integration of additional components and libraries, making it adaptable to continually evolving energy modelling needs.

Implementation and Applications

The framework consists of several layers, notably the core, namespace, and cosmos layers, which organize the framework's functionality and extend its adaptability. oemof serves as a foundation upon which users build specific applications, ranging from electricity market models to regional energy system optimizations. Documenting these applications in an open and transparent manner ensures the replicability and scrutiny of results, adhering to scientific rigor.

Significantly, the oemof project uses GitHub as a central platform for version control, code hosting, and bug tracking, which is crucial for accessibility and collaborative development. The project also emphasizes extensive documentation, including in-code comments, API docstrings, and high-level manuals, which are indispensable for fostering understanding and usage among new developers and modellers.

Implications and Future Directions

oemof has proven efficacy in diverse applications, such as the renpassG!S model for Western European electricity markets and the openMod.sh participative workshop models, highlighting its extensive applicability across scales and sectors. The framework's open-source nature and collaborative ethos promote innovation and shared learning, aligning with broader trends towards transparency and public engagement in scientific research.

Looking forward, the development of oemof hints at several implications for future developments in AI and computational modelling. The open, community-driven approach may inspire similar frameworks across different domains of computational research, fostering innovative solutions to complex interdisciplinary challenges. As energy systems become more integrated and data-driven, platforms like oemof will likely become pivotal in shaping robust, transparent, and adaptable modelling environments.

Overall, oemof exemplifies how open science and collaborative principles can transform energy system modelling, aligning technological advancement with societal needs for more sustainable and transparent energy solutions.