Requirements for modern energy system models

As part of the zusammEn2040 project, APG — together with its cooperation partner TransnetBW — developed an energy system model based on a further advancement of the open source model PyPSA. 

Today, a wide range of energy system models (e.g., PyPSA, TIMES, LEGO) are available to address questions in energy economics and energy policy. Depending on the application, different models offer varying strengths and limitations. In the following guest article, Robert Gaugl (Assistant Professor at Graz University of Technology) takes a detailed look at the PyPSA energy system model used by APG and highlights its most important functionalities. 

Further information can be found in the publication “A comparative analysis of energy system modeling frameworks” [1]. 

What makes holistic modeling of energy systems so complex?

Planning a climate-neutral energy system requires models that meet several key requirements.

• Modeling of networks and transport infrastructure: This is essential for identifying bottlenecks, transport corridors, and the need for grid expansion.
• Sector coupling: Interactions between electricity, heat, transport, industry, and gas must be represented to fully understand how technologies and energy sources interact.
• Realistic representation of flexibility options: Storage technologies, demand-side management, electrolysis, and e-mobility must be adequately modeled to support economically efficient, environmentally sustainable, and secure system planning.
• Transparency: Open-source models facilitate scientific reproducibility, independent validation, and broad applicability in research and policy advice.

Models that combine all these points facilitate holistic and cost-optimized system planning along the entire process chain: from generation and conversion to storage, transport, and consumption. Furthermore, existing models differ in their level of detail, resolution, and computational effort, but each fulfills important components of modern energy system analysis. Examples include PyPSA [2], TIMES/TIAM [3], AnyMOD [4], Balmorel [5], and LEGO [6]. In recent years, PyPSA in particular has enjoyed increasing popularity for answering various questions in different areas.

What relevant aspects can be modeled or taken into account by PyPSA?

Overall system analysis and cost optimization

PyPSA enables the joint optimization of generation, storage, conversion, and consumption processes. This allows alternative energy pathways to be compared and evaluated using system-wide cost indicators.

Modeling of flexibility and storage options

The model supports the representation of a wide range of flexibility options, including short- and long-term storage, power-to-X technologies, demand-side management, and e-mobility. These features are essential for analyzing the integration of fluctuating renewable energy sources.

Sector coupling

PyPSA models electricity, heat, gas, and hydrogen systems, as well as diverse industrial and transport-related energy demands. This facilitates the analysis of interactions between energy carriers and sectors and the quantification of potential synergies.

Consideration of network restrictions

PyPSA also provides simplified approaches for incorporating constraints at lower voltage levels. Aggregated “distribution links” or similar representations can be used to connect local generation, flexible loads, and storage to higher-level network nodes. In this way, limited absorption capacities and local flexibility within distribution networks can be accounted for in system-wide optimization analyses.

[1] R. Gaugl, K. Walenta ,and S. Wogrin, “A comparative analysis of energy system modeling frameworks,” Elektrotech. Inftech. 142, 508–526, Oct. 2025, doi.org/10.1007/s00502-025-01345-x.

[2] T. Brown, J. Hörsch, and D. Schlachtberger, “PyPSA: Python for Power System Analysis,” J. Open Res. Softw., Jan. 2018, doi: 10.5334/jors.188.

[3] R. Loulou, “ETSAP-TIAM: the TIMES integrated assessment model. part II: mathematical formulation,” Comput. Manag. Sci., vol. 5, no. 1, pp. 41–66, Feb. 2008, doi: 10.1007/s10287-007-0045-0.

[4] L. Göke, “AnyMOD.jl: A Julia package for creating energy system models,” SoftwareX, vol. 16, p. 100871, Dec. 2021, doi: 10.1016/j.softx.2021.100871.

[5]  F. Wiese et al., “Balmorel open source energy system model,” Energy Strategy Rev., vol. 20, pp. 26–34, Apr. 2018, doi: 10.1016/j.esr.2018.01.003.

[6] S. Wogrin, D. A. Tejada-Arango, R. Gaugl, T. Klatzer, and U. Bachhiesl, “LEGO: The open-source Low-carbon Expansion Generation Optimization model,” SoftwareX, vol. 19, 2022, doi: 10.1016/j.softx.2022.101141.