Default config file

Below is an example ispypsa_config.yaml file, the file configures a simple fast running model designed to be useful for learning and testing purposes. The model implemented by the config:
Optimises capacity expansion to met demand in 2050 in a single build step
Only considers the NSW region
Is based on 2024 ISP Step Change scenario assumption
Retains existing capacity which has not reached retirement age by 2050
Uses three weeks of timeseries data based on 2018 reference year data the weeks in 2050 with residual peak demand, peak-consumption, and residual minimum demand are used.
# ===== Paths ==========================================================================

paths:
  # The run directory where all inputs and outputs will be stored
  run_directory: "ispypsa_runs"

  # The name of the ISPyPSA model run
  # This name is used to select the output folder within `ispypsa_runs`
  ispypsa_run_name: example_model_run

  # The path to the folder containing parsed demand, wind and solar traces.
  parsed_traces_directory: "data/trace_data"

  # The path to the ISP workbook Excel file
  workbook_path: "data/2024-isp-inputs-and-assumptions-workbook.xlsx"

  # The path to the workbook table cache directory
  parsed_workbook_cache: "data/workbook_table_cache"


# ===== Trace Data =====================================================================

trace_data:
  # The type of trace dataset to download when using CLI download tasks
  # Options: "example" (smaller, for testing) or "full" (complete dataset)
  # Default: "example"
  dataset_type: example

  # The year of trace dataset to download when using CLI download tasks
  # Currently only 2024 is supported
  # Default: 2024
  dataset_year: 2024


# ===== ISPyPSA templating =============================================================

# The version of IASR workbook that the template inputs are generated from.
iasr_workbook_version: "6.0"

# The ISP scenario for which to generate ISPyPSA inputs
scenario: Step Change


# ===== Financial/Economic =============================================================

# Weighted average cost of capital for annuitisation of generation and transmission
# costs, as a fraction, i.e. 0.07 is 7%.
wacc: 0.07
# Discount rate applied to model objective function, as a fraction, i.e. 0.07 is 7%.
discount_rate: 0.05


# ===== Unserved energy ================================================================

unserved_energy:
  # Cost of unserved energy in $/MWh. Set to 'None' to disable unserved energy generators.
  cost: 10000.0
  # Maximum capacity of each unserved energy generator in MW. Defaults to 1e5 (100,000 MW).
  max_per_node: 100000.0


# ===== Regional scope =================================================================

# Filter the model by NEM regions or ISP sub regions.
filter_by_nem_regions: [NSW]
# filter_by_isp_sub_regions: [NNSW, CNSW]


# ===== Network ========================================================================

network:
  # Does the model consider the expansion of sub-region to sub-region transmission
  # capacity
  transmission_expansion: True
  # Does the model consider the expansion of renewable energy zone transmission
  # capacity
  rez_transmission_expansion: True
  # Years to annuitise transmission project capital costs over.
  annuitisation_lifetime: 30
  nodes:
    # The regional granularity of the nodes in the modelled network (only sub_regions is implemented)
    regional_granularity: sub_regions
    # Whether Renewable Energy Zones (REZs) are modelled as distinct nodes
    # Only discrete_nodes implemented
    rezs: discrete_nodes
  # Line capacity limit for rez to node connections that have their limit's modelled
  # through custom constraint (MW).
  rez_to_sub_region_transmission_default_limit: 1e5


# ===== Temporal =======================================================================

temporal:
  year_type: fy
  range:
    # Model begin at the start of the start year. E.g. the first time interval for a
    # financial year model starting in 2025 would be 2024-07-01 00:30:00.
    start_year: 2050
    # Model ends at the end of the start year. E.g. the last time interval for a
    # financial year model ending in 2028 would be 2028-06-01 23:30:00.
    end_year: 2050
  capacity_expansion:
    resolution_min: 30
    reference_year_cycle: [2018]
    # List of investment period start years. An investment period runs from the beginning
    # of the year (financial or calendar depending on the config) until the next the
    # period begins.
    investment_periods: [2050]
    aggregation:
      # Representative weeks to use instead of full yearly temporal representation.
      # Options:
      #   ~ (None): Full yearly temporal representation is used or another aggregation.
      #   list[int]: a list of integers specifying weeks of year to use as representative. Weeks of year are defined as
      #   full weeks (Monday-Sunday) falling within the year. For example, if the list is "[1]" the model will only use the
      #   first full week of each modelled year.

      representative_weeks: ~

      # Named representative weeks based on demand characteristics
      #   ~ (None): Full yearly temporal representation is used or another aggregation.
      #   list[str]: peak-demand, residual-peak-demand, minimum-demand,
      #     residual-minimum-demand, peak-consumption, residual-peak-consumption
      named_representative_weeks: [residual-peak-demand]

  operational:
    resolution_min: 30
    reference_year_cycle: [2018]
    horizon: 336
    overlap: 48
    aggregation:
      # Representative weeks to use instead of full yearly temporal representation.
      # Options:
      #    ~ (None): Full yearly temporal representation is used or another aggregation.
      #   list[int]: a list of integers specifying weeks of year to use as representative. Weeks of year are defined as
      #   full weeks (Monday-Sunday) falling within the year. For example, if the list is "[1]" the model will only use the
      #   first full week of each modelled year.
      representative_weeks: ~

      # Named representative weeks based on demand characteristics
      #   ~ (None): Full yearly temporal representation is used or another aggregation.
      #   list[str]: peak-demand, residual-peak-demand, minimum-demand,
      #     residual-minimum-demand, peak-consumption, residual-peak-consumption
      named_representative_weeks: [residual-peak-demand, peak-consumption, residual-minimum-demand]


# ===== Solver =======================================================================

# External solver to use
solver: highs


# ===== Plotting =====================================================================
create_plots: True