GreenBubble#

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.

GreenBubble network diagram

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

Key features#


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

Technologies (examples)#

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

Quick start#

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 -j4

See the installation guide for platform-specific instructions and solver setup (Gurobi / HiGHS). New to Snakemake? See the Snakemake documentation.

Licence and citation#

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},
}

Contributors#

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

GreenBubble

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