GreenBubble is an open-source techno-economic optimisation model for Power-to-X industrial clusters — co-located plants that share electricity, hydrogen, CO₂, biomethane, methanol and heat infrastructure. Built on PyPSA, it co-optimises capacity expansion and hourly dispatch across a multi-energy hub over a full year at 1-hour resolution.
The model was developed based on the GreenLab Skive industrial park — an agricultural-industrial hub in Denmark that integrates biogas, electrolysis, methanation and methanol synthesis. The methodology is described in:
Optimizing hydrogen and e-methanol production through Power-to-X integration in biogas plants, Energy Conversion and Management, 2024.
DOI: 10.1016/j.enconman.2024.119175
📖 Full documentation: gls-greenbubble.readthedocs.io
| Multi-energy LP | Electricity, H₂, CO₂, biomethane, methanol and heat (3 temperature levels) in a single solve |
| Capacity + dispatch | No decomposition — capacity expansion and hourly operation co-optimised |
| Greenfield & brownfield | Existing assets parameterised by construction year and remaining investment fraction |
| Stochastic scenarios | Multi-scenario LP with expected value of perfect information (EVPI) |
| Rolling horizon | Dispatch-only mode on a fixed network for operational studies |
| Industrial symbiosis | Shapley-value cost allocation across co-located partners |
| RFNBO compliance | Additionality and emission constraints for renewable hydrogen certification |
| Economic post-processing | LCOP, short-run marginal cost (SRMC), KKT shadow prices, annual profit per technology |
GreenBubble is designed to be extensible. The following are examples of technologies currently implemented — the list is not exhaustive:
Hydrogen production — Alkaline electrolysis
Biomethane production — Biogas upgrading · Biomethanation of biogas or CO₂ · Catalytic methanation of biogas or CO₂
Methanol production — CO₂ hydrogenation
Renewable electricity — Onshore wind · Solar PV
Storage — Li-ion batteries · H₂ in steel vessels · CO₂ liquefaction · Pressurised CO₂ cylinders · Hot-water thermal storage · Concrete-based thermal energy storage
Biomass handling — Belt dryer · Digestate dewatering
Shared infrastructure — H₂, CO₂ and heat distribution networks · Gas compressors · Grid connection
git clone https://github.com/BertoGBG/GLS_greenbubble.git
cd GLS_greenbubble
# Create environment — shown for macOS Apple Silicon; see docs for other platforms
conda config --add channels conda-forge && conda config --set channel_priority strict
conda install -n base -c conda-forge conda-lock
conda-lock install -n greenbubble-pypsa107 --platform osx-arm64 envs/locks/conda-lock-osx-arm64.yml
conda activate greenbubble-pypsa107
# Copy and fill in API tokens (required for data retrieval)
cp .env.example .env
# Preview the execution plan, then run
snakemake -n
snakemake -j4See the installation guide for platform-specific instructions and solver setup (Gurobi / HiGHS). New to Snakemake? See the Snakemake documentation.
- Code: MIT
- Documentation: CC-BY-4.0
If you use GreenBubble in your research, please cite:
@article{greenbubble2024,
title = {Optimizing hydrogen and e-methanol production through
Power-to-X integration in biogas plants},
journal = {Energy Conversion and Management},
year = {2024},
doi = {10.1016/j.enconman.2024.119175},
}Developed at:
- DTU Wind and Energy Systems — Power and Energy Systems Division, Technical University of Denmark
- Department of Mechanical and Production Engineering, Aarhus University
- Department of Biological and Chemical Engineering, Aarhus University