Aeso ========================= .. py:module:: gridstatus.aeso Submodules ---------- .. toctree:: :titlesonly: :maxdepth: 1 aeso/index.rst aeso_constants/index.rst Package Contents ---------------- Classes Summary ~~~~~~~~~~~~~~~ .. autoapisummary:: gridstatus.aeso.AESO Contents ~~~~~~~~~~~~~~~~~~~ .. py:class:: AESO(api_key: str | None = None) API client for Alberta Electric System Operator (AESO) data. Handles authentication and provides methods to access various AESO datasets including supply and demand, market data, and operational information. Initialize the AESO API client. :param api_key: AESO API key. If not provided, will try to get from AESO_API_KEY environment variable. **Attributes** .. list-table:: :widths: 15 85 :header-rows: 0 * - **api_key** - None * - **base_url** - 'https://apimgw.aeso.ca/public' * - **default_headers** - None * - **default_timezone** - 'US/Mountain' * - **HISTORICAL_FORECAST_EARLIEST** - None * - **HISTORICAL_FORECAST_LATEST** - None * - **MAX_NAVIGATION_ATTEMPTS** - 100 **Methods** .. autoapisummary:: :nosignatures: gridstatus.aeso.AESO.get_asset_list gridstatus.aeso.AESO.get_daily_average_pool_price gridstatus.aeso.AESO.get_forecast_pool_price gridstatus.aeso.AESO.get_fuel_mix gridstatus.aeso.AESO.get_generator_outages_hourly gridstatus.aeso.AESO.get_interchange gridstatus.aeso.AESO.get_load gridstatus.aeso.AESO.get_load_forecast gridstatus.aeso.AESO.get_pool_price gridstatus.aeso.AESO.get_reserves gridstatus.aeso.AESO.get_solar_10_min gridstatus.aeso.AESO.get_solar_forecast_12_hour gridstatus.aeso.AESO.get_solar_forecast_7_day gridstatus.aeso.AESO.get_solar_hourly gridstatus.aeso.AESO.get_supply_and_demand gridstatus.aeso.AESO.get_system_marginal_price gridstatus.aeso.AESO.get_transmission_outages gridstatus.aeso.AESO.get_unit_status gridstatus.aeso.AESO.get_wind_10_min gridstatus.aeso.AESO.get_wind_forecast_12_hour gridstatus.aeso.AESO.get_wind_forecast_7_day gridstatus.aeso.AESO.get_wind_hourly .. py:method:: get_asset_list(asset_id: str | None = None, pool_participant_id: str | None = None, operating_status: str | None = None, asset_type: str | None = None) -> pandas.DataFrame Get list of assets in the AESO system. :param asset_id: Filter by specific asset ID :param pool_participant_id: Filter by pool participant ID :param operating_status: Filter by operating status :param asset_type: Filter by asset type :returns: DataFrame containing asset information .. py:method:: get_daily_average_pool_price(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, verbose: bool = False) -> pandas.DataFrame Get daily average pool price data with on-peak and off-peak breakdowns. On-peak hours are defined as hours ending 8 through 23 (inclusive). Off-peak hours are all other hours. :returns: DataFrame containing daily average price data .. py:method:: get_forecast_pool_price(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, verbose: bool = False) -> pandas.DataFrame Get pool price data. :returns: DataFrame containing pool price data .. py:method:: get_fuel_mix() -> pandas.DataFrame Get current generation by fuel type. :returns: DataFrame containing generation data by fuel type, with each fuel type as a column containing its net generation value .. py:method:: get_generator_outages_hourly(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, verbose: bool = False) -> pandas.DataFrame Get hourly generator outage data. :param date: Start date for the data. Can be "latest" to get current data. :param end: End date for the data. If not provided, will get 24 months of data. :param verbose: Whether to print verbose output. :returns: DataFrame containing generator outage data .. py:method:: get_interchange() -> pandas.DataFrame Get current interchange flows with neighboring regions. :returns: DataFrame containing interchange data with separate columns for each region's flow and a net interchange flow column .. py:method:: get_load(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, verbose: bool = False) -> pandas.DataFrame Get current load data. :returns: DataFrame containing load data .. py:method:: get_load_forecast(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, verbose: bool = False) -> pandas.DataFrame Get load forecast data. The AESO publishes load forecasts daily at 7am Mountain Time. The forecast covers the next 13 days. The publish time is determined as follows: - For historical data: 7am on the day of the interval if interval is after 7am, otherwise 7am the previous day - For future data: 7am today (if after 7am) or 7am yesterday (if before 7am) :returns: DataFrame containing load forecast data with publish times. .. py:method:: get_pool_price(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, verbose: bool = False) -> pandas.DataFrame Get pool price data. :returns: DataFrame containing pool price data .. py:method:: get_reserves() -> pandas.DataFrame Get current reserve data. :returns: DataFrame containing reserve information .. py:method:: get_solar_10_min(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, verbose: bool = False) -> pandas.DataFrame Get actual solar generation data with 10-minute intervals. :returns: DataFrame containing actual solar generation data with 10-minute intervals .. py:method:: get_solar_forecast_12_hour(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, verbose: bool = False) -> pandas.DataFrame Get 12-hour solar forecast data. :returns: DataFrame containing 12-hour solar forecast data with min, most likely, and max values .. py:method:: get_solar_forecast_7_day(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, verbose: bool = False) -> pandas.DataFrame Get 7-day solar forecast data. :returns: DataFrame containing 7-day solar forecast data with min, most likely, and max values .. py:method:: get_solar_hourly(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, verbose: bool = False) -> pandas.DataFrame Get actual solar generation data with hourly intervals. :returns: DataFrame containing actual solar generation data with hourly intervals .. py:method:: get_supply_and_demand() -> pandas.DataFrame Get current supply and demand summary data. :returns: DataFrame containing current supply and demand information .. py:method:: get_system_marginal_price(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, verbose: bool = False) -> pandas.DataFrame Get system marginal price data. :returns: DataFrame containing system marginal price data with minutely intervals .. py:method:: get_transmission_outages(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None) -> pandas.DataFrame Get transmission outages data. :param date: Start date for the data. Can be "latest" to get current data. :param end: End date for the data. If not provided, will get data for the specified date. :returns: DataFrame containing transmission outage data .. py:method:: get_unit_status(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], verbose: bool = False) -> pandas.DataFrame Get current unit status data for all assets in the AESO system. :returns: - Time: Timestamp of the data - Asset: Asset identifier - Fuel Type: Type of fuel used - Sub Fuel Type: Sub-category of fuel type - Maximum Capability: Maximum generation capability in MW - Net Generation: Current net generation in MW - Dispatched Contingency Reserve: Amount of contingency reserve dispatched in MW :rtype: DataFrame containing unit status data with columns .. py:method:: get_wind_10_min(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, verbose: bool = False) -> pandas.DataFrame Get actual wind generation data with 10-minute intervals. :returns: DataFrame containing actual wind generation data with 10-minute intervals .. py:method:: get_wind_forecast_12_hour(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, verbose: bool = False) -> pandas.DataFrame Get 12-hour wind forecast data. :returns: DataFrame containing 12-hour wind forecast data with min, most likely, and max values .. py:method:: get_wind_forecast_7_day(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, verbose: bool = False) -> pandas.DataFrame Get 7-day wind forecast data. :returns: DataFrame containing 7-day wind forecast data with min, most likely, and max values .. py:method:: get_wind_hourly(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, verbose: bool = False) -> pandas.DataFrame Get actual wind generation data with hourly intervals. :returns: DataFrame containing actual wind generation data with hourly intervals