Technical Implementation

Technical Implementation#

This section provides technical details about GB-specific implementation features.

FES Powerplants Data#

Overview#

The GB model uses powerplant capacity data from the Future Energy Scenarios (FES) workbook, enriched with technical and cost parameters to create a complete dataset ready for PyPSA network composition. This data replaces the default PyPSA-Eur powerplants dataset with GB-specific capacity projections.

Data Pipeline#

The powerplants data flows through two main stages:

  1. Capacity Aggregation (create_powerplants_table.py)

    • Processes FES workbook data (GB regions)

    • Processes European supply data (neighboring countries)

    • Maps technology names to PyPSA carriers

    • Aggregates capacities by bus, year, carrier, and set

  2. Cost Enrichment (create_powerplants_table.py)

    • Joins technology cost data (efficiency, VOM, fuel costs, etc.)

    • Calculates marginal costs

    • Fills missing values with sensible defaults

    • Creates unique generator indices

Output Format#

The resulting fes_powerplants.csv contains complete generator data:

Core Attributes:

  • bus - Network bus ID (string)

  • year - Planning year (integer)

  • carrier - Technology type (CCGT, nuclear, onwind, etc.)

  • set - Generator classification (PP, CHP, Store)

  • p_nom - Nominal capacity in MW (float)

  • build_year - Year of installation (integer)

Technical Parameters:

  • efficiency - Energy conversion efficiency (0-1)

  • lifetime - Asset lifetime in years (float)

Economic Parameters:

  • VOM - Variable O&M cost (GBP/MWh)

  • FOM - Fixed O&M cost (€/MW/year)

  • capital_cost - Investment cost (€/MW)

  • fuel - Fuel cost (GBP/MWh_thermal)

  • marginal_cost - Total variable cost (GBP/MWh_el)

Index Format: "{bus} {carrier}-{year}-{counter}"

Example: "GB0 CCGT-2030-0", "GB0 CCGT-2030-1"

Marginal Cost Calculation#

Marginal cost combines variable O&M and fuel costs:

marginal_cost = VOM + fuel / efficiency

Where:

  • VOM - Variable operations and maintenance (GBP/MWh_el)

  • fuel - Fuel cost (GBP/MWh_thermal)

  • efficiency - Conversion efficiency (MWh_el / MWh_thermal)

Example for CCGT with efficiency=0.55, VOM=2.5, fuel=25.0:

marginal_cost = 2.5 + 25.0/0.55 = 47.95 GBP/MWh

Default Values#

When cost data is unavailable for specific carriers, defaults are applied:

  • efficiency: 1 (100% conversion efficiency)

  • capital_cost: 0.0 €/MW

  • lifetime: 25.0 years

  • marginal_cost: 0.0 GBP/MWh

These defaults prevent missing data from blocking network composition while logging warnings for review.

Integration with compose_network#

The compose_network rule loads the enriched powerplants data directly:

ppl = pd.read_csv(powerplants_path, index_col=0, dtype={"bus": "str"})

This data is then used by:

  • attach_conventional_generators() - Adds fossil/nuclear generators

  • attach_wind_and_solar() - Adds renewable generators

  • attach_hydro() - Adds hydro and storage units

  • attach_chp_constraints() - Applies CHP heat demand constraints

No additional preprocessing is required; all necessary attributes are present in the CSV.

Configuration#

The create_powerplants_table rule requires:

Inputs:

  • gsp_data - GB regional capacity data from FES workbook

  • eur_data - European national capacity data

  • tech_costs - Technology cost assumptions

Parameters:

  • gb_config - GB technology mapping configuration

  • eur_config - European technology mapping configuration

  • default_set - Default generator classification (typically “PP”)

Output:

  • fes_powerplants.csv - Complete powerplants dataset

File Location#

scripts/gb_model/
└── create_powerplants_table.py   # Data processing and enrichment

rules/
└── gb-model.smk                  # Snakemake rule definitions

results/{run}/resources/gb-model/
└── fes_powerplants.csv           # Generated output
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