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Electricity Sector

This repository aggregates the Canadian electricity sector for the CANOE model. It processes raw data from various national and international sources into a unified, aggregated format suitable for large-scale energy system modeling.

Data Sources

The aggregation process draws from several primary databases and datasets:

  • CODERS: The Canadian Open-Source Database for Energy Research and Systems-Modelling is the primary source for existing capacity, generator types, and generic technology data.
  • NREL Annual Technology Baseline (ATB): Used for cost projections (CAPEX/OPEX), efficiency metrics (heat rates), and future technology parameters. It is particularly used for "new" technology candidates.
  • IESO Public Data: Provides hourly production and demand data for Ontario, which is used to derive representative capacity factors and demand profiles.
  • Renewables.ninja: Used for high-resolution variable renewable energy (VRE) capacity factors (wind and solar).
  • Internal Configuration:
    • params.yaml: Main configuration for model years, currencies, and aggregation switches.
    • generator_technologies.csv: Defines the mapping between CODERS/ATB techs and the internal model tech codes.
    • atb_master_tables.csv: Maps specific spreadsheet locations in the NREL ATB workbook.

Processing & Assumptions

The aggregation script (electricity_sector.py) follows a structured pipeline:

  1. Pre-processing: Loads configuration and cleans base parameters.
  2. Temporal & Spatial Aggregation:
    • Time: Converts hourly data into representative days/slices (e.g., peak demand days, average seasonal days).
    • Regions: Aggregates data at the provincial level (e.g., AB, BC, ON, QC).
  3. Existing vs. New Capacity:
    • Existing: Aggregated from CODERS snapshots for a specific base year (default 2020). Small capacities below a threshold (0.001 GW) are filtered out.
    • New: Technology candidates are generated based on ATB projections, with options for batched capacity limits.
  4. Technology Assumptions:
    • VRE Modeling: Solar and wind are modeled with performance degradation (solar) and specific LCOE/Capacity Factor sorting.
    • CCS Retrofits: Optional flags allow for modeling carbon capture and storage retrofits on existing fossil fuel plants.
    • Interties: Both boundary (external to model) and endogenous (between provinces) interties are included.
  5. Currency and Inflation: All costs are converted to a common currency (CAD) and adjusted for inflation using indices like the GDP deflator.

Final Data

The output of the aggregation process consists of:

  • SQLite Database (electricity.sqlite): A fully structured database containing all aggregated tables (Commodities, Technologies, Costs, Capacity Factors, etc.) according to the canoe_dataset_schema.sql.
  • Excel Summary (electricity.xlsx): A flattened version of the database for easier manual inspection and reporting.
  • Data Cache (data_cache/): Locally cached versions of pulled source data to allow for offline execution and reproducibility.
  • Provincial Summaries: Processed grid data including demand profiles and reserve margins for each Canadian province.