Ieso ========================= .. py:module:: gridstatus.ieso Module Contents --------------- Classes Summary ~~~~~~~~~~~~~~~ .. autoapisummary:: gridstatus.ieso.IESO gridstatus.ieso.SurplusState Functions ~~~~~~~~~ .. autoapisummary:: :nosignatures: gridstatus.ieso.retired_data_warning Attributes Summary ~~~~~~~~~~~~~~~~~~~ .. autoapisummary:: gridstatus.ieso.CERTIFICATES_CHAIN_FILE gridstatus.ieso.CURRENT_DIR gridstatus.ieso.RETIRED_DATE Contents ~~~~~~~~~~~~~~~~~~~ .. py:data:: CERTIFICATES_CHAIN_FILE .. py:data:: CURRENT_DIR .. py:class:: IESO Bases: :py:obj:`gridstatus.base.ISOBase` Independent Electricity System Operator (IESO) **Attributes** .. list-table:: :widths: 15 85 :header-rows: 0 * - **default_timezone** - 'EST' * - **iso_id** - 'ieso' * - **name** - 'Independent Electricity System Operator' * - **status_homepage** - 'https://www.ieso.ca/en/Power-Data' **Methods** .. autoapisummary:: :nosignatures: gridstatus.ieso.IESO.get_forecast_surplus_baseload_generation gridstatus.ieso.IESO.get_fuel_mix gridstatus.ieso.IESO.get_generator_report_hourly gridstatus.ieso.IESO.get_hoep_historical_hourly gridstatus.ieso.IESO.get_hoep_real_time_hourly gridstatus.ieso.IESO.get_in_service_transmission_limits gridstatus.ieso.IESO.get_intertie_actual_schedule_flow_hourly gridstatus.ieso.IESO.get_intertie_flow_5_min gridstatus.ieso.IESO.get_intertie_limits_day_ahead_hourly gridstatus.ieso.IESO.get_intertie_limits_real_time_5_min gridstatus.ieso.IESO.get_lmp_day_ahead_hourly gridstatus.ieso.IESO.get_lmp_day_ahead_hourly_intertie gridstatus.ieso.IESO.get_lmp_day_ahead_hourly_ontario_zonal gridstatus.ieso.IESO.get_lmp_day_ahead_hourly_virtual_zonal gridstatus.ieso.IESO.get_lmp_day_ahead_operating_reserves gridstatus.ieso.IESO.get_lmp_predispatch_hourly gridstatus.ieso.IESO.get_lmp_predispatch_hourly_intertie gridstatus.ieso.IESO.get_lmp_predispatch_hourly_ontario_zonal gridstatus.ieso.IESO.get_lmp_predispatch_hourly_virtual_zonal gridstatus.ieso.IESO.get_lmp_real_time_5_min gridstatus.ieso.IESO.get_lmp_real_time_5_min_intertie gridstatus.ieso.IESO.get_lmp_real_time_5_min_ontario_zonal gridstatus.ieso.IESO.get_lmp_real_time_5_min_virtual_zonal gridstatus.ieso.IESO.get_lmp_real_time_operating_reserves gridstatus.ieso.IESO.get_load gridstatus.ieso.IESO.get_load_forecast gridstatus.ieso.IESO.get_load_zonal_5_min gridstatus.ieso.IESO.get_load_zonal_hourly gridstatus.ieso.IESO.get_mcp_historical_5_min gridstatus.ieso.IESO.get_mcp_real_time_5_min gridstatus.ieso.IESO.get_outage_transmission_limits gridstatus.ieso.IESO.get_real_time_totals gridstatus.ieso.IESO.get_resource_adequacy_report gridstatus.ieso.IESO.get_resource_adequacy_report_by_last_modified gridstatus.ieso.IESO.get_shadow_prices_day_ahead_hourly gridstatus.ieso.IESO.get_shadow_prices_real_time_5_min gridstatus.ieso.IESO.get_solar_embedded_forecast gridstatus.ieso.IESO.get_solar_market_participant_forecast gridstatus.ieso.IESO.get_transmission_outages_planned gridstatus.ieso.IESO.get_wind_embedded_forecast gridstatus.ieso.IESO.get_wind_market_participant_forecast gridstatus.ieso.IESO.get_yearly_intertie_actual_schedule_flow_hourly gridstatus.ieso.IESO.get_zonal_load_forecast .. py:method:: get_forecast_surplus_baseload_generation(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: pandas.Timestamp | None = None, verbose: bool = False) -> pandas.DataFrame Get forecast surplus baseload generation. :param date: The publish date to get data for. The forecast will be for the day after this date. :param end: The end date to get data for. If None, only get data for the start date. :param verbose: Whether to print verbose output. :returns: - Interval Start: The start of the interval - Interval End: The end of the interval - Publish Time: The time the forecast was published - Surplus Baseload MW: The forecast surplus baseload generation in MW - Surplus State: The state of the surplus baseload generation - Action: The action taken for the surplus baseload generation - Export Forecast MW: The forecast export in MW - Minimum Generation Status: The minimum generation status :rtype: DataFrame with columns .. py:method:: get_fuel_mix(date: str | datetime.date | datetime.datetime, end: datetime.date | datetime.datetime | None = None, verbose: bool = False) Hourly output and capability for each fuel type (summed over all generators) for a given date or from date to end. Variable generators (solar and wind) have a forecast. :param date: The date to get the load for Can be a `datetime.date` or `datetime.datetime` object, or a string with the values "today" or "latest". If `end` is None, returns only data for this date. :type date: datetime.date | datetime.datetime | str :param end: End date. Defaults None If provided, returns data from `date` to `end` date. The `end` can be a `datetime.date` or `datetime.datetime` object. :type end: datetime.date | datetime.datetime, optional :param verbose: Print verbose output. Defaults to False. :type verbose: bool, optional :returns: fuel mix :rtype: pd.DataFrame .. py:method:: get_generator_report_hourly(date: str | datetime.date | datetime.datetime, end: datetime.date | datetime.datetime | None = None, verbose: bool = False) Hourly output for each generator for a given date or from date to end. Variable generators (solar and wind) have a forecast and available capacity. Non-variable generators have a capability. :param date: The date to get the load for Can be a `datetime.date` or `datetime.datetime` object, or a string with the values "today" or "latest". If `end` is None, returns only data for this date. :type date: datetime.date | datetime.datetime | str :param end: End date. Defaults None If provided, returns data from `date` to `end` date. The `end` can be a `datetime.date` or `datetime.datetime` object. :type end: datetime.date | datetime.datetime, optional :param verbose: Print verbose output. Defaults to False. :type verbose: bool, optional :returns: generator output and capability/available capacity :rtype: pd.DataFrame .. py:method:: get_hoep_historical_hourly(date: str | datetime.date | datetime.datetime, end: datetime.date | datetime.datetime | None = None, verbose: bool = False) .. py:method:: get_hoep_real_time_hourly(date: str | datetime.date | datetime.datetime, end: datetime.date | datetime.datetime | None = None, verbose: bool = False) -> pandas.DataFrame .. py:method:: get_in_service_transmission_limits(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: pandas.Timestamp | None = None, verbose: bool = False) .. py:method:: get_intertie_actual_schedule_flow_hourly(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: pandas.Timestamp | None = None, verbose: bool = False) -> pandas.DataFrame .. py:method:: get_intertie_flow_5_min(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: pandas.Timestamp | None = None, verbose: bool = False) -> pandas.DataFrame .. py:method:: get_intertie_limits_day_ahead_hourly(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: pandas.Timestamp | None = None, verbose: bool = False) -> pandas.DataFrame Get day-ahead intertie scheduling limits. This returns hourly data showing import and export limits for each of Ontario's intertie zones used in the day-ahead market. :param date: Date or date range to get data for, or "latest" :param end: End date for date range (optional) :param verbose: Whether to print verbose output :returns: DataFrame with columns for interval start/end and import/export limits for each intertie zone .. py:method:: get_intertie_limits_real_time_5_min(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: pandas.Timestamp | None = None, verbose: bool = False) -> pandas.DataFrame Get real-time intertie scheduling limits. This returns 5-minute interval data showing import and export limits for each of Ontario's intertie zones. :param date: Date or date range to get data for, or "latest" :param end: End date for date range (optional) :param verbose: Whether to print verbose output :returns: DataFrame with columns for interval start/end and import/export limits for each intertie zone .. py:method:: get_lmp_day_ahead_hourly(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: pandas.Timestamp | None = None, verbose: bool = False) -> pandas.DataFrame Get day-ahead LMP data. :param date: The date to get the data for. :param end: The end date to get the data for. :param verbose: Whether to print verbose output. :returns: DataFrame with LMP data. .. py:method:: get_lmp_day_ahead_hourly_intertie(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: pandas.Timestamp | None = None, verbose: bool = False) -> pandas.DataFrame .. py:method:: get_lmp_day_ahead_hourly_ontario_zonal(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: pandas.Timestamp | None = None, verbose: bool = False) .. py:method:: get_lmp_day_ahead_hourly_virtual_zonal(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: pandas.Timestamp | None = None, verbose: bool = False) -> pandas.DataFrame Get day-ahead zonal virtual LMP data. :param date: The date to get the data for. :param end: The end date to get the data for. :param verbose: Whether to print verbose output. :returns: DataFrame with LMP data. .. py:method:: get_lmp_day_ahead_operating_reserves(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: pandas.Timestamp | None = None, verbose: bool = False) -> pandas.DataFrame Get day-ahead operating reserves LMP data. :param date: The date to get the data for. :param end: The end date to get the data for. :param verbose: Whether to print verbose output. :returns: DataFrame with operating reserves LMP data. .. py:method:: get_lmp_predispatch_hourly(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: pandas.Timestamp | None = None, verbose: bool = False) -> pandas.DataFrame .. py:method:: get_lmp_predispatch_hourly_intertie(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: pandas.Timestamp | None = None, verbose: bool = False) -> pandas.DataFrame .. py:method:: get_lmp_predispatch_hourly_ontario_zonal(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: pandas.Timestamp | None = None, verbose: bool = False) .. py:method:: get_lmp_predispatch_hourly_virtual_zonal(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: pandas.Timestamp | None = None, verbose: bool = False) .. py:method:: get_lmp_real_time_5_min(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: pandas.Timestamp | None = None, verbose: bool = False) .. py:method:: get_lmp_real_time_5_min_intertie(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: pandas.Timestamp | None = None, verbose: bool = False) .. py:method:: get_lmp_real_time_5_min_ontario_zonal(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: pandas.Timestamp | None = None, verbose: bool = False) .. py:method:: get_lmp_real_time_5_min_virtual_zonal(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: pandas.Timestamp | None = None, verbose: bool = False) .. py:method:: get_lmp_real_time_operating_reserves(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: pandas.Timestamp | None = None, verbose: bool = False) .. py:method:: get_load(date: str | datetime.date | datetime.datetime, end: datetime.date | datetime.datetime | None = None, verbose: bool = False) Get 5-minute load for the Market and Ontario for a given date or from date to end date. :param date: The date to get the load for Can be a `datetime.date` or `datetime.datetime` object, or a string with the values "today" or "latest". If `end` is None, returns only data for this date. :type date: datetime.date | datetime.datetime | str :param end: End date. Defaults None If provided, returns data from `date` to `end` date. The `end` can be a `datetime.date` or `datetime.datetime` object. :type end: datetime.date | datetime.datetime, optional :param verbose: Print verbose output. Defaults to False. :type verbose: bool, optional :param frequency: Frequency of data. Defaults to "5min". :type frequency: str, optional :returns: zonal load as a wide table with columns for each zone :rtype: pd.DataFrame .. py:method:: get_load_forecast(date: str, verbose: bool = False) Get forecasted load for Ontario. Supports only "latest" and "today" because there is only one load forecast. :param date: Either "today" or "latest" :type date: str :param verbose: Print verbose output. Defaults to False. :type verbose: bool, optional :returns: Ontario load forecast :rtype: pd.DataFrame .. py:method:: get_load_zonal_5_min(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: pandas.Timestamp | None = None, verbose: bool = False) -> pandas.DataFrame .. py:method:: get_load_zonal_hourly(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: pandas.Timestamp | None = None, verbose: bool = False) -> pandas.DataFrame .. py:method:: get_mcp_historical_5_min(date: str | datetime.date | datetime.datetime, end: datetime.date | datetime.datetime | None = None, verbose: bool = False) .. py:method:: get_mcp_real_time_5_min() .. py:method:: get_outage_transmission_limits(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: pandas.Timestamp | None = None, verbose: bool = False) .. py:method:: get_real_time_totals(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: pandas.Timestamp | None = None, verbose: bool = False) -> pandas.DataFrame .. py:method:: get_resource_adequacy_report(date: str | datetime.date | datetime.datetime, end: datetime.date | datetime.datetime | None = None, vintage: Literal['all', 'latest'] = 'latest', last_modified: str | datetime.date | datetime.datetime | None = None) -> pandas.DataFrame Retrieve and parse the Resource Adequacy Report for a given date. :param date: The date for which to get the report :type date: str | datetime.date | datetime.datetime :param end: The end date for the range of reports to get :type end: datetime.date | datetime.datetime | None :param vintage: The version of the report to get :type vintage: Literal["all", "latest"] :param last_modified: The last modified time after which to get report(s) :type last_modified: str | datetime.date | datetime.datetime | None :returns: The Resource Adequacy Report df for the given date :rtype: pd.DataFrame .. py:method:: get_resource_adequacy_report_by_last_modified(last_modified: str | datetime.date | datetime.datetime) -> pandas.DataFrame Retrieve and parse Resource Adequacy Reports modified after last_modified time. This method bypasses date iteration and gets all files across all dates. This is useful for ETL systems that want to get all new files at once. :param last_modified: The last modified time after which to get report(s) :param vintage: The version of the report to get :returns: The Resource Adequacy Report df with all files modified after last_modified :rtype: pd.DataFrame .. py:method:: get_shadow_prices_day_ahead_hourly(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: pandas.Timestamp | None = None, verbose: bool = False, last_modified: str | pandas.Timestamp | None = None) -> pandas.DataFrame .. py:method:: get_shadow_prices_real_time_5_min(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: pandas.Timestamp | None = None, verbose: bool = False, last_modified: str | pandas.Timestamp | None = None) -> pandas.DataFrame .. py:method:: get_solar_embedded_forecast(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: pandas.Timestamp | None = None, vintage: Literal['latest', 'all'] = 'latest', verbose: bool = False) -> pandas.DataFrame .. py:method:: get_solar_market_participant_forecast(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: pandas.Timestamp | None = None, vintage: Literal['latest', 'all'] = 'latest', verbose: bool = False) -> pandas.DataFrame .. py:method:: get_transmission_outages_planned(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: pandas.Timestamp | None = None, verbose: bool = False) .. py:method:: get_wind_embedded_forecast(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: pandas.Timestamp | None = None, vintage: Literal['latest', 'all'] = 'latest', verbose: bool = False) -> pandas.DataFrame .. py:method:: get_wind_market_participant_forecast(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: pandas.Timestamp | None = None, vintage: Literal['latest', 'all'] = 'latest', verbose: bool = False) -> pandas.DataFrame .. py:method:: get_yearly_intertie_actual_schedule_flow_hourly(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: pandas.Timestamp | None = None, verbose: bool = False, vintage: Literal['all', 'latest'] = 'latest', last_modified: str | datetime.date | datetime.datetime | None = None) -> pandas.DataFrame Get yearly intertie actual schedule flow hourly. Since this is a yearly file it is updated less frequency than the daily files. These can be retrieved via the get_intertie_schedule_flow_hourly method. :param date: The date to get the data for. :param end: The end date to get the data for. :param verbose: Whether to print verbose output. :param vintage: Whether to get the latest version or all versions of the report. :param last_modified: Only return reports modified after this date. :returns: DataFrame with hourly intertie schedule flow data. .. py:method:: get_zonal_load_forecast(date: str | datetime.date | tuple[datetime.date, datetime.date], end: datetime.date | datetime.datetime | None = None, verbose: bool = False) Get forecasted load by forecast zone (Ontario, East, West) for a given date or from date to end date. This method supports future dates. Supports data 90 days into the past and up to 34 days into the future. :param date: The date to get the load for Can be a `datetime.date` or `datetime.datetime` object, or a string with the values "today" or "latest". If `end` is None, returns only data for this date. :type date: datetime.date | datetime.datetime | str :param end: End date. Defaults None If provided, returns data from `date` to `end` date. The `end` can be a `datetime.date` or `datetime.datetime` object. :type end: datetime.date | datetime.datetime, optional :param verbose: Print verbose output. Defaults to False. :type verbose: bool, optional :returns: forecasted load as a wide table with columns for each zone :rtype: pd.DataFrame .. py:function:: retired_data_warning() .. py:data:: RETIRED_DATE .. py:class:: SurplusState Bases: :py:obj:`str`, :py:obj:`enum.Enum` Enum for surplus baseload generation states. The state is determined by the Action field in the report: - No Action (empty/null) -> No Surplus (white) - Other -> Managed with exports/curtailments/VG dispatch (green) - Manoeuvre -> Potential to dispatch nuclear units (yellow) - Shutdown -> Potential to shutdown nuclear units (red) Initialize self. See help(type(self)) for accurate signature. **Attributes** .. list-table:: :widths: 15 85 :header-rows: 0 * - **MANAGED_WITH_EXPORTS** - 'Managed with Exports' * - **NO_SURPLUS** - 'No Surplus' * - **NUCLEAR_DISPATCH** - 'Nuclear Dispatch' * - **NUCLEAR_SHUTDOWN** - 'Nuclear Shutdown'