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Demand Settings

These parameters control electricity demand data sources for the model.

Demand Data

demand_table

Type: String or dictionary Required: Yes Example: See below

Table containing hourly electricity demand projections for all model regions and planning periods.

Simple:

demand_table: load_curves.csv

Advanced (with scenario selection):

demand_table:
  table_name: demand_timeseries.parquet
  scenario: high_electrification

Required columns:

  • region: Model region name
  • time_index: Hour index
  • load_mw: Demand in MW
  • year: Planning year

Optional columns:

  • scenario: Demand scenario identifier (reference, high_ev, high_electrification, etc.)
  • weather_year: Weather data vintage year

Format: Tidy/long format with one row per region-time-year observation.

Example demand CSV:

time_index,weather_year,region,load_mw,year,scenario
1,2012,CA_N,15234.5,2030,reference
2,2012,CA_N,14123.2,2030,reference
3,2012,CA_N,13890.4,2030,reference
1,2012,CA_S,12450.8,2030,reference
...
1,2012,CA_N,16890.2,2040,reference
2,2012,CA_N,15678.9,2040,reference
...

Multi-Period Coverage

The demand_table should contain hourly demand projections for all future modeling periods. For example, if your model runs from 2030 to 2050 in 5-year increments, the table should include demand data for 2030, 2035, 2040, 2045, and 2050.

Demand Scenarios

Use the scenario column to manage different demand futures:

demand_table:
  table_name: demand_projections.parquet
  scenario: high_electrification  # Options: reference, high_ev, high_electrification

Common scenarios:

  • reference: Base case load growth
  • high_ev: Increased electric vehicle adoption
  • high_electrification: Widespread building/industrial electrification
  • low_growth: Slower demand growth with efficiency improvements