PyPSA-DE: German Integrated Energy Model

• PyPSA-DE is an open-source energy system model designed for integrated planning of Germany's multi-sector networks under net-zero constraints.
• The model employs a linear cost minimization framework with high spatial and temporal resolution to optimize electricity, hydrogen, and other sectoral networks.
• It demonstrates that joint optimization reduces transmission expansion and grid tariffs, offering a flexible platform for policy innovation and energy transition studies.

PyPSA-DE is an open-source German energy system model for integrated, cross-sectoral planning under net-zero transition constraints. It is formulated as a linear optimization model that simulates the electricity and hydrogen transmission networks, as well as supply, demand, and storage in all sectors of the energy system in Germany and its neighboring countries with high spatial and temporal resolution. Its central analytical contribution is to compare integrated planning against existing national plans and to show lower transmission expansion and lower grid tariffs under joint optimization of sectors, networks, and locations (Lindner et al., 10 Oct 2025).

1. Definition and analytical purpose

PyPSA-DE is designed to study the German energy transition as a coupled infrastructure planning problem rather than as a set of separate sectoral subproblems. In the reported formulation, electricity, hydrogen, heating, transport, agriculture, waste, and industry are optimized jointly, with sector coupling represented explicitly. The model therefore differs from siloed planning approaches that treat electricity and hydrogen separately, because it captures synergies between network expansion, flexible demand, storage, and conversion technologies such as electrolysis (Lindner et al., 10 Oct 2025).

A defining feature of the model is its emphasis on integrated planning and operation. The paper identifies three drivers of the reported savings relative to the German National Grid Development Plan: integrated planning and operation, a market design with regional prices, and a system-optimal usage of offshore wind. This places PyPSA-DE at the intersection of transmission planning, sector coupling, and market design analysis rather than within a narrowly electricity-only planning tradition (Lindner et al., 10 Oct 2025).

The model is also framed as fully open-source and readily adaptable. A plausible implication is that PyPSA-DE is intended not only as a single scenario study for Germany, but also as a reusable research platform for policy interventions, technology innovation, and market design variants.

2. Geographic, sectoral, and temporal scope

PyPSA-DE uses high spatial and temporal resolution for a national planning model. The German energy system is represented with 30 regions in Germany plus 19 regions for neighboring countries, giving 49 regions in total. Time is represented at 3-hourly resolution over full weather years, and the transition is modeled for multiple years from 2020 to 2045 in 5-year steps (Lindner et al., 10 Oct 2025).

Dimension	Representation	Notes
Spatial scope	30 regions in Germany + 19 neighboring regions	49 regions total
Temporal scope	3-hourly resolution over full weather years	2020–2045 in 5-year steps
Sectoral scope	Electricity, hydrogen, heating, transport, agriculture, waste, industry	Sector coupling included

This resolution is paired with explicit transmission and hydrogen infrastructure. The electricity network includes both AC and DC transmission, while the hydrogen network is modeled as pipelines connecting sources such as electrolysers and imports to demand centers such as industry, e-fuels, and backup power. The neighboring-country regions are part of the modeled system rather than an exogenous boundary condition, which is important for import/export effects and weekly flexibility provision (Lindner et al., 10 Oct 2025).

The sectoral breadth is central to the model’s interpretation. PyPSA-DE is not restricted to electricity balancing; it represents supply, demand, and storage across all sectors. This suggests that its outputs on transmission needs and tariffs are conditioned by end-use electrification, hydrogen production, and flexible sector coupling options rather than by electricity demand alone.

3. Optimization framework and network representation

PyPSA-DE is formulated as a linear cost minimization problem. In the reported summary, the objective minimizes generation, storage, and transmission costs over time and assets. The model includes network energy balance constraints at each node, linearized power flow equations for electricity, piecewise linear losses for transmission, capacity limits for lines and pipelines, generation and storage constraints, a CO$_2$ budget, and technology and policy constraints such as phase-outs, renewable targets, land-use bounds, and hydrogen network expansion bounds (Lindner et al., 10 Oct 2025).

For the electricity grid, the model uses a linear DC load flow with a cycle-based linearization and piecewise linear losses, calibrated and validated versus real-world data. The core flow relation is represented as

$f_{e,t} = B_e (\theta_{m,t} - \theta_{n,t}),$

with line capacities bounded by

$|f_{e,t}| \leq \text{capacity}_e.$

For $N-1$ security, flows are restricted to 70% of technical rating (Lindner et al., 10 Oct 2025).

For hydrogen, PyPSA-DE uses a simpler transport abstraction. Hydrogen pipelines are modeled as linear transport links without detailed gas flow physics. This is an important modeling boundary: the hydrogen network is endogenous and spatially explicit, but it is not represented through detailed gas hydraulics. The paper states that such linear flows suffice for projected 2045 demands and that the planned National Development Plan hydrogen network is sufficient or even oversized (Lindner et al., 10 Oct 2025).

The model also permits regional electricity pricing rather than a single bidding zone for Germany. The summary explicitly links this to locational marginal pricing and treats it as crucial for asset siting, congestion management, and reducing grid expansion requirements (Lindner et al., 10 Oct 2025).

4. Inputs, policy constraints, and open implementation

PyPSA-DE embeds hard-coded German policy assumptions, including nuclear and coal phase-outs and the net-zero target for 2045. It uses national development datasets and registers for the grid, plants, and related infrastructure, and scenarios are validated for 2020 before projecting the transition out to 2045 (Lindner et al., 10 Oct 2025).

The model is fully open-source, and its code and data are presented as available for adaptation. The repository is identified as https://github.com/PyPSA/pypsa-de. The paper further states that the model can readily be adapted to study policy interventions, tech innovation, new market designs, or extension to other countries (Lindner et al., 10 Oct 2025).

In methodological lineage, PyPSA-DE sits on top of the broader PyPSA ecosystem. PyPSA itself is a free software toolbox for simulating and optimising modern electrical power systems over multiple periods, with support for conventional generators with unit commitment, variable renewable generation, storage units, coupling to other energy sectors, and mixed alternating and direct current networks (Brown et al., 2017). PyPSA-Earth, in turn, generalizes PyPSA-Eur workflows to global scope and describes PyPSA-DE as the German PyPSA-Eur derivative in the context of reusable national-scale model generation and workflow reuse (Parzen et al., 2022).

This ecosystem context matters because it clarifies that PyPSA-DE is not an isolated codebase with bespoke formulations for every subsystem. Rather, it belongs to a family of open, modular energy system models that share network-based formulations, reproducible workflows, and extensibility across spatial scales.

5. Main findings on transmission, prices, and flexibility

The principal finding reported for PyPSA-DE is that integrated, cross-sectoral regional planning lowers transmission expansion relative to the official National Grid Development Plan. The paper states that total expansion is one third lower than in the national grid development plan, lowering costs by 92 billion EUR$_{2020}$ to 191 billion EUR$_{2020}$ and average grid tariffs by 7.5 EUR$_{2020}$/MWh (Lindner et al., 10 Oct 2025).

In the expanded summary, the transmission investment need is reported as reduced from 283 B€ to 191 B€ for 2025–2045, implying savings of 92 B€. Total energy system investment for Germany is reported as greater than 730 B€ (Lindner et al., 10 Oct 2025). Lower grid costs translate into lower grid tariffs for consumers, and with regional pricing end-user prices decrease everywhere, with particularly large reductions of up to 14.2 €/MWh in wind-rich coastal areas (Lindner et al., 10 Oct 2025).

A major mechanism behind these results is the system-optimal usage of offshore wind through coastal electrolysis. Optimally sited electrolysis at the coast allows direct use of coastal offshore wind for hydrogen and reduces the need for expensive long onshore DC cables for power transfer. Electrolysis also provides flexibility: during periods of high wind, hydrogen is produced and stored or transported, while in low-wind periods hydrogen-fired plants provide backup power (Lindner et al., 10 Oct 2025).

The paper also quantifies changing flexibility requirements. Daily flexibility needs increase 10-fold by 2045, mainly provided by EV charging management and batteries. Weekly flexibility needs increase 5-fold, mainly provided by import/export and hydrogen electrolysis (Lindner et al., 10 Oct 2025). These results are significant because they connect transmission expansion, sector coupling, and end-use flexibility in a single optimization framework rather than treating them as separate studies.

6. Position within the PyPSA landscape and model boundaries

PyPSA-DE should be distinguished both from the general PyPSA framework and from short-term operational extensions built on PyPSA. PyPSA provides the underlying capabilities for multi-period optimization, storage modeling, sector coupling, mixed AC-DC networks, locational marginal prices, and security-constrained linear optimal power flow (Brown et al., 2017). PyPSA-DE applies this broader framework to a Germany-centered, cross-sectoral planning problem with endogenous electricity and hydrogen networks (Lindner et al., 10 Oct 2025).

A common misconception is to equate PyPSA-DE with plant-level or portfolio-level bidding tools. The stochastic unit commitment tool described in “An Open Source Stochastic Unit Commitment Tool using the PyPSA-Framework” is a different extension family: it adds market and bidding mechanisms, stochastic optimization, and multistaging for short-term, uncertainty-aware, profit-maximizing unit commitment, demonstrated for a German waste-to-energy plant with heat storage and a battery energy storage system under uncertain day-ahead, aFRR, and heat-load conditions (Welfonder et al., 2024). That paper explicitly states that PyPSA-DE commonly models large-scale German or European energy systems with a focus on least-cost dispatch, capacity expansion, and sector coupling, usually under perfect foresight or deterministic optimization, whereas the stochastic UC tool fills the gap for realistic operational market-participation strategies under uncertainty (Welfonder et al., 2024).

Another important boundary concerns physical detail. PyPSA-DE represents hydrogen pipelines as linear transport links rather than detailed gas flow physics (Lindner et al., 10 Oct 2025). This does not remove hydrogen from the model; it specifies the level of abstraction at which hydrogen transport is optimized. Similarly, the model’s emphasis is on integrated planning under policy and network constraints, not on detailed market microstructure or stochastic intraday operations.

Within the broader open-model ecosystem, PyPSA-Earth demonstrates how reproducible workflows, automated data extraction, validation routines, and modular “linkers” can support national studies such as Germany-focused models (Parzen et al., 2022). This suggests a broader methodological role for PyPSA-DE: it is both a substantive German energy transition model and a reference implementation for transparent, high-resolution, sector-coupled planning in an open-source setting.