Network Model#

GreenBubble represents an industrial cluster as a multi-energy network built with PyPSA and solved as a linear programme via Linopy. This page describes the network structure, the optimisation problem, and how the two fit together.

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Multi-energy representation#

The cluster is modelled as a set of buses — one per energy carrier. Components (generators, conversion units, storage) connect to these buses via links, stores, and loads.

Energy and material carriers#
Carrier	Unit	Role in the network
Electricity	MW_el	Grid connection, wind, solar, electrolysis input, internal distribution
Hydrogen	MW_H₂	Electrolysis output, methanation / methanol feed, internal distribution
CO₂	t_CO₂/h	Captured from biogas, consumed by methanation and methanol synthesis
Biomethane (CH₄)	MW_CH₄	Biogas upgrading output, methanation output, product delivery
Methanol	t_MeOH/h	Methanol synthesis output, product delivery
Heat — medium temperature	MW_th	Waste heat from conversion processes, biomass drying
Heat — district heating (DH)	MW_th	Optional sale to external DH grid
Heat — low temperature	MW_th	Low-grade heat from cooling loops
Biomass (moist / pellets)	MW_bio	Biogas feedstock

Each carrier may have multiple buses representing distinct physical locations or pressure levels (e.g. H₂ at the electrolyser vs. H₂ at the methanation inlet). Internal pipes and compressors are explicit links with their own efficiency losses and, optionally, expandable capacity.

PyPSA component types#

Component	Use in GreenBubble
`Generator`	Renewable electricity sources (wind, solar) and biogas input; may be extendable (greenfield) or capacity-fixed (brownfield)
`Link`	All conversion processes: electrolysis, methanation, methanol synthesis, biogas engine, belt dryer, compressors. A link can have up to five outputs (bus0 … bus4), enabling combined heat-and-power units
`Store`	Energy buffers where carrier is stored without direct dispatch control: CO₂ tanks, product delivery buffers
`StorageUnit`	Storage with explicit charge/discharge dispatch: batteries, H₂ vessels, thermal stores
`Load`	Demand time series for each product and internal energy consumer

Technology activation is controlled by the n_flags dict in config/config.default.yaml. Setting a flag to false removes all components of that technology group from the network before the solve — no parameters need to change elsewhere.

Optimisation problem#

GreenBubble solves a single linear programme that simultaneously determines optimal installed capacities and hourly dispatch over a full year (8 760 time steps at 1-hour resolution).

Objective function

Minimise total annualised system cost (or maximise revenue, depending on the targets.driver setting):

Cost mode (driver: 'demand'): minimise capital cost + operating cost subject to fixed annual production requirements.
Revenue mode (driver: 'price'): maximise net revenue from product sales minus system costs; production becomes an upper bound rather than a constraint.

Capital costs are annualised using the discount rate and component lifetime from the technology-cost database. Operating costs include variable O&M and fuel purchases.

Key constraints

Energy balance at every bus and every hour (enforced by PyPSA/Linopy)
Capacity bounds: p_nom_min ≤ p_nom ≤ p_nom_max per component
Ramp limits: |p(t) - p(t-1)| ≤ ramp_limit × p_nom for selected technologies (electrolysis, methanation, methanol synthesis)
Minimum load: p(t) ≥ min_load × p_nom for online units
Annual product targets: total production ≥ demand (demand mode) or ≤ capacity × 8760 (price mode)
RFNBO constraints: optional additionality/temporal correlation requirements on electrolytic hydrogen (see RFNBO compliance)
CO₂ balance: captured CO₂ = consumed CO₂ (closed internal loop with optional liquefaction and sequestration)
Renewable export cap: RE export ≤ max_RE_to_grid × (internal use + export)

Solver

The LP is passed to HiGHS (open-source) or Gurobi via Linopy. Solver profiles with pre-tuned tolerance and thread settings are defined in scripts/solver_profiles.py.

Brownfield vs. greenfield#

Each technology can be configured independently in n_config.default.yaml:

initial capacity > 0 + expansion: false → brownfield fixed asset (costs zeroed, capacity locked)
initial capacity > 0 + expansion: true → brownfield with expansion allowed above the initial installed size
initial capacity: 0 + expansion: true → greenfield (capacity is a free variable from zero)

This allows modelling real plants at GreenLab Skive (or any other site) by setting existing capacities for known assets while leaving greenfield expansion open for new investments.

Output files#

After the solve, the optimised PyPSA network is exported to:

outputs/<run>/<network_name>/
  networks/
    <network>_PRE.nc   ← pre-optimisation network (built by build_network)
    <network>_OPT.nc   ← post-optimisation network with p_nom_opt, dispatch TS
  plots/
    ...                ← dispatch figures, capacity charts, shadow prices

Network Model

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