Spp ======================== .. py:module:: gridstatus.spp Module Contents --------------- Classes Summary ~~~~~~~~~~~~~~~ .. autoapisummary:: gridstatus.spp.BAAEnum gridstatus.spp.SPP Functions ~~~~~~~~~ .. autoapisummary:: :nosignatures: gridstatus.spp.add_interval gridstatus.spp.fill_baa_column gridstatus.spp.process_gen_mix Attributes Summary ~~~~~~~~~~~~~~~~~~~ .. autoapisummary:: gridstatus.spp.BAA_LOAD_THRESHOLD_MW gridstatus.spp.BASE_LOAD_FORECAST_MID_TERM_URL gridstatus.spp.BASE_LOAD_FORECAST_SHORT_TERM_URL gridstatus.spp.BASE_SOLAR_AND_WIND_MID_TERM_URL gridstatus.spp.BASE_SOLAR_AND_WIND_SHORT_TERM_URL gridstatus.spp.DA_BINDING_CONSTRAINTS gridstatus.spp.FILE_BROWSER_API_URL gridstatus.spp.FILE_BROWSER_DOWNLOAD_URL gridstatus.spp.FS_DAM_LMP_BY_LOCATION gridstatus.spp.FS_RTBM_LMP_BY_LOCATION gridstatus.spp.HOURLY_LOAD_WIDE_FORMAT_END_DATE gridstatus.spp.LAST_UPDATED_KEYWORDS gridstatus.spp.LMP_BY_SETTLEMENT_LOCATION_WEIS gridstatus.spp.LMP_HUBS_AND_INTERFACES gridstatus.spp.LOCATION_TYPE_ALL gridstatus.spp.LOCATION_TYPE_BUS gridstatus.spp.LOCATION_TYPE_HUB gridstatus.spp.LOCATION_TYPE_INTERFACE gridstatus.spp.LOCATION_TYPE_SETTLEMENT_LOCATION gridstatus.spp.MARKETPLACE_BASE_URL gridstatus.spp.OPERATING_RESERVES gridstatus.spp.QUERY_RTM5_HUBS_URL gridstatus.spp.QUERY_RTM5_INTERFACES_URL gridstatus.spp.RELIABILITY_LEVELS gridstatus.spp.RELIABILITY_LEVELS_ALIASES gridstatus.spp.RTBM_BINDING_CONSTRAINTS gridstatus.spp.RTBM_LMP_BY_BUS gridstatus.spp.RTBM_MCP gridstatus.spp.STATUS_STOP_WORDS Contents ~~~~~~~~~~~~~~~~~~~ .. py:function:: add_interval(df: pandas.DataFrame, interval_min: int) -> pandas.DataFrame Adds Interval Start and Interval End columns to df .. py:data:: BAA_LOAD_THRESHOLD_MW :value: 5000 .. py:class:: BAAEnum Bases: :py:obj:`enum.StrEnum` Enum where members are also (and must be) strings Initialize self. See help(type(self)) for accurate signature. **Attributes** .. list-table:: :widths: 15 85 :header-rows: 0 * - **SPP** - 'SPP' * - **SWPW** - 'SWPW' .. py:data:: BASE_LOAD_FORECAST_MID_TERM_URL :value: 'https://portal.spp.org/file-browser-api/download/mtlf-vs-actual?path=' .. py:data:: BASE_LOAD_FORECAST_SHORT_TERM_URL :value: 'https://portal.spp.org/file-browser-api/download/stlf-vs-actual?path=' .. py:data:: BASE_SOLAR_AND_WIND_MID_TERM_URL :value: 'https://portal.spp.org/file-browser-api/download/midterm-resource-forecast?path=' .. py:data:: BASE_SOLAR_AND_WIND_SHORT_TERM_URL :value: 'https://portal.spp.org/file-browser-api/download/shortterm-resource-forecast?path=' .. py:data:: DA_BINDING_CONSTRAINTS :value: 'da-binding-constraints' .. py:data:: FILE_BROWSER_API_URL :value: 'https://portal.spp.org/file-browser-api/' .. py:data:: FILE_BROWSER_DOWNLOAD_URL :value: 'https://portal.spp.org/file-browser-api/download' .. py:function:: fill_baa_column(df, load_col) Fill missing BAA values based on load magnitude. If the BAA column doesn't exist, creates it. If it exists but has NaN values, fills only the missing entries. Uses BAA_LOAD_THRESHOLD_MW to distinguish between SWPW (small loads) and SPP (large loads). :param df: DataFrame with a load column to use for BAA inference. :param load_col: Name of the column containing load values. :returns: The DataFrame with BAA column filled in-place. .. py:data:: FS_DAM_LMP_BY_LOCATION :value: 'da-lmp-by-settlement-location' .. py:data:: FS_RTBM_LMP_BY_LOCATION :value: 'rtbm-lmp-by-location' .. py:data:: HOURLY_LOAD_WIDE_FORMAT_END_DATE .. py:data:: LAST_UPDATED_KEYWORDS :value: ['last updated', 'as of'] .. py:data:: LMP_BY_SETTLEMENT_LOCATION_WEIS :value: 'lmp-by-settlement-location-weis' .. py:data:: LMP_HUBS_AND_INTERFACES .. py:data:: LOCATION_TYPE_ALL :value: 'ALL' .. py:data:: LOCATION_TYPE_BUS :value: 'Bus' .. py:data:: LOCATION_TYPE_HUB :value: 'Hub' .. py:data:: LOCATION_TYPE_INTERFACE :value: 'Interface' .. py:data:: LOCATION_TYPE_SETTLEMENT_LOCATION :value: 'Settlement Location' .. py:data:: MARKETPLACE_BASE_URL :value: 'https://portal.spp.org' .. py:data:: OPERATING_RESERVES :value: 'operating-reserves' .. py:function:: process_gen_mix(df: pandas.DataFrame, detailed: bool = False) -> pandas.DataFrame Parse SPP generation mix data from https://marketplace.spp.org/pages/generation-mix-historical :param df: raw data :type df: pd.DataFrame :param detailed: whether to combine market and self columns :type detailed: bool :returns: processed data :rtype: pd.DataFrame .. py:data:: QUERY_RTM5_HUBS_URL :value: 'https://pricecontourmap.spp.org/arcgis/rest/services/MarketMaps/RTBM_FeatureData/MapServer/1/query' .. py:data:: QUERY_RTM5_INTERFACES_URL :value: 'https://pricecontourmap.spp.org/arcgis/rest/services/MarketMaps/RTBM_FeatureData/MapServer/2/query' .. py:data:: RELIABILITY_LEVELS :value: ['Normal Operations', 'Weather Advisory', 'Resource Advisory', 'Conservative Operations... .. py:data:: RELIABILITY_LEVELS_ALIASES .. py:data:: RTBM_BINDING_CONSTRAINTS :value: 'rtbm-binding-constraints' .. py:data:: RTBM_LMP_BY_BUS :value: 'rtbm-lmp-by-bus' .. py:data:: RTBM_MCP :value: 'rtbm-mcp' .. py:class:: SPP Bases: :py:obj:`gridstatus.base.ISOBase` Southwest Power Pool (SPP) **Attributes** .. list-table:: :widths: 15 85 :header-rows: 0 * - **default_timezone** - 'US/Central' * - **interconnection_homepage** - 'https://www.spp.org/engineering/generator-interconnection/' * - **iso_id** - 'spp' * - **location_types** - None * - **markets** - None * - **name** - 'Southwest Power Pool' * - **status_homepage** - 'https://www.spp.org/markets-operations/current-grid-conditions/' **Methods** .. autoapisummary:: :nosignatures: gridstatus.spp.SPP.get_as_prices_real_time_5_min gridstatus.spp.SPP.get_binding_constraints_day_ahead_hourly gridstatus.spp.SPP.get_binding_constraints_real_time_5_min gridstatus.spp.SPP.get_capacity_of_generation_on_outage gridstatus.spp.SPP.get_capacity_of_generation_on_outage_annual gridstatus.spp.SPP.get_day_ahead_operating_reserve_prices gridstatus.spp.SPP.get_fuel_mix gridstatus.spp.SPP.get_fuel_mix_by_baa gridstatus.spp.SPP.get_fuel_mix_by_baa_detailed gridstatus.spp.SPP.get_fuel_mix_detailed gridstatus.spp.SPP.get_hourly_load gridstatus.spp.SPP.get_hourly_load_annual gridstatus.spp.SPP.get_hourly_load_historical gridstatus.spp.SPP.get_interchange_real_time gridstatus.spp.SPP.get_interconnection_queue gridstatus.spp.SPP.get_lmp_day_ahead_hourly gridstatus.spp.SPP.get_lmp_real_time_5_min_by_bus gridstatus.spp.SPP.get_lmp_real_time_5_min_by_location gridstatus.spp.SPP.get_lmp_real_time_weis gridstatus.spp.SPP.get_load gridstatus.spp.SPP.get_load_by_baa gridstatus.spp.SPP.get_load_by_baa_hourly gridstatus.spp.SPP.get_load_forecast gridstatus.spp.SPP.get_load_forecast_by_baa gridstatus.spp.SPP.get_load_forecast_mid_term gridstatus.spp.SPP.get_load_forecast_short_term gridstatus.spp.SPP.get_market_clearing_day_ahead gridstatus.spp.SPP.get_market_clearing_real_time gridstatus.spp.SPP.get_operating_reserves gridstatus.spp.SPP.get_raw_interconnection_queue gridstatus.spp.SPP.get_solar_and_wind_forecast_mid_term gridstatus.spp.SPP.get_solar_and_wind_forecast_short_term gridstatus.spp.SPP.get_ver_curtailments gridstatus.spp.SPP.get_ver_curtailments_annual gridstatus.spp.SPP.get_ver_curtailments_by_baa gridstatus.spp.SPP.get_ver_curtailments_by_baa_annual gridstatus.spp.SPP.get_west_interchange_real_time gridstatus.spp.SPP.now .. py:method:: get_as_prices_real_time_5_min(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, verbose: bool = False, use_daily_files: bool = False) -> pandas.DataFrame Provides Marginal Clearing Price information by Reserve Zone for each Real-Time 5-minute Market solution. :param date: date to get data for. Supports "latest" for most recent interval. :param end: end date :param verbose: print url :param use_daily_files: if True, use daily files instead of 5 minute files. :returns: Real-Time 5-minute Marginal Clearing Prices :rtype: pd.DataFrame .. py:method:: get_binding_constraints_day_ahead_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 Day-Ahead Binding Constraints :param date: date to get data for. Supports "latest" for most recently available data. :param end: end date :param verbose: print url :returns: Day-Ahead Binding Constraints :rtype: pd.DataFrame .. py:method:: get_binding_constraints_real_time_5_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 Real-Time Binding Constraints :param date: date to get data for. Supports "latest" for most recent interval. :param end: end date :param verbose: print url :returns: Real-Time Binding Constraints :rtype: pd.DataFrame .. py:method:: get_capacity_of_generation_on_outage(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 Capacity of Generation on Outage. Published daily at 8am CT for next 7 days :param date: start date :param end: end date .. py:method:: get_capacity_of_generation_on_outage_annual(year: int, verbose: bool = True) -> pandas.DataFrame Get VER Curtailments for a year. Starting 2014. Recent data use get_capacity_of_generation_on_outage :param year: year to get data for :param verbose: print url :returns: VER Curtailments :rtype: pd.DataFrame .. py:method:: get_day_ahead_operating_reserve_prices(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 Provides Marginal Clearing Price information by Reserve Zone for each Day-Ahead Market solution for each Operating Day. Posting is updated each day after the DA Market results are posted. Available at https://portal.spp.org/pages/da-mcp# :param date: date to get data for :param end: end date :param verbose: print url :returns: Day Ahead Marginal Clearing Prices :rtype: pd.DataFrame .. py:method:: get_fuel_mix(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: pandas.Timestamp | None = None, verbose: bool = False) -> pandas.DataFrame Get combined fuel mix summed across SPP and SWPW BAAs :param date: "latest", "today", a timestamp, or a date range tuple :param end: optional end date for range queries :returns: fuel mix summed across both BAAs :rtype: pd.DataFrame .. py:method:: get_fuel_mix_by_baa(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: pandas.Timestamp | None = None, verbose: bool = False) -> pandas.DataFrame Get fuel mix for both SPP and SWPW BAAs with a BAA column :param date: "latest", "today", a timestamp, or a date range tuple :param end: optional end date for range queries :returns: fuel mix with BAA column differentiating SPP and SWPW :rtype: pd.DataFrame .. py:method:: get_fuel_mix_by_baa_detailed(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: pandas.Timestamp | None = None, verbose: bool = False) -> pandas.DataFrame Get detailed fuel mix for both SPP and SWPW BAAs with a BAA column Breaks out self scheduled and market scheduled generation. :param date: "latest", "today", a timestamp, or a date range tuple :param end: optional end date for range queries :returns: detailed fuel mix with BAA column :rtype: pd.DataFrame .. py:method:: get_fuel_mix_detailed(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: pandas.Timestamp | None = None, verbose: bool = False) -> pandas.DataFrame Get combined detailed fuel mix summed across SPP and SWPW BAAs Breaks out self scheduled and market scheduled generation. :param date: "latest", "today", a timestamp, or a date range tuple :param end: optional end date for range queries :returns: detailed fuel mix summed across both BAAs :rtype: pd.DataFrame .. py:method:: get_hourly_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 Hourly Load in the long format (on or after 2026-03-24). :param date: start date (must be on or after 2026-03-24) :param end: end date :returns: Hourly Load with columns Time, Interval Start, Interval End, Balancing Area Name, Control Zone Name, Forecast Area Type, Load :rtype: pd.DataFrame .. py:method:: get_hourly_load_annual(year: int, verbose: bool = True) -> pandas.DataFrame Get Hourly Load for a year. Starting 2011. For recent data use `get_hourly_load` :param year: year to get data for :param verbose: print url :returns: Hourly Load :rtype: pd.DataFrame .. py:method:: get_hourly_load_historical(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 Load in the legacy wide format (before 2026-03-24). Deprecated: SPP changed the hourly load data format on 2026-03-24. Use get_hourly_load for data on or after 2026-03-24. :param date: start date (must be before 2026-03-24) :param end: end date :returns: Hourly Load in wide format :rtype: pd.DataFrame .. py:method:: get_interchange_real_time(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 real-time interchange (tie flow) data. For "latest" and "today", returns ~2 days of 1-minute interchange data from the real-time endpoint. For historical dates, downloads monthly CSV files from the historical tie flow archive. Data from: - Real-time: https://portal.spp.org/pages/integrated-marketplace-interchange-trend - Historical: https://portal.spp.org/pages/historical-tie-flow :param date: supports "latest", "today", or a historical date/date range :param end: end date for historical range queries :param verbose: print info :returns: interchange data :rtype: pd.DataFrame .. py:method:: get_interconnection_queue(verbose: bool = False) -> pandas.DataFrame Get interconnection queue :returns: Interconnection queue :rtype: pandas.DataFrame .. py:method:: get_lmp_day_ahead_hourly(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, location_type: str = LOCATION_TYPE_ALL, verbose: bool = False) -> pandas.DataFrame Get day ahead hourly LMP data Supported Location Types: - ``Hub`` - ``Interface`` - ``ALL`` .. py:method:: get_lmp_real_time_5_min_by_bus(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 LMP data by bus for the Real-Time 5 Minute Market :param date: date to get data for :param end: end date :param verbose: print url NOTE: does not take a location_type argument because it always returns LOCATION_TYPE_BUS. .. py:method:: get_lmp_real_time_5_min_by_location(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, location_type: str = LOCATION_TYPE_ALL, verbose: bool = False, use_daily_files: bool = False) -> pandas.DataFrame Get LMP data by location for the Real-Time 5 Minute Market :param date: date to get data for :param end: end date :param location_type: location type to get data for. Options are: - ``ALL`` (LOCATION_TYPE_ALL) - ``Hub`` (LOCATION_TYPE_HUB) - ``Interface`` (LOCATION_TYPE_INTERFACE) - ``Settlement Location`` (LOCATION_TYPE_SETTLEMENT_LOCATION) :param verbose: print url :param use_daily_files: if True, use daily files instead of 5 minute files. .. py:method:: get_lmp_real_time_weis(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 LMP data for real time WEIS :param date: date to get data for. if end is not provided, will get data for 5 minute interval that date is in. :param end: end date :param verbose: print url .. py:method:: get_load(date: str | pandas.Timestamp, end: str | pandas.Timestamp | None = None, verbose: bool = False) -> pandas.DataFrame Returns total RTO load in 5 minute intervals from STLF data. .. py:method:: get_load_by_baa(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 Returns actual load by BAA from short-term load forecast data. .. py:method:: get_load_by_baa_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 | None Returns hourly actual load by BAA from mid-term load forecast 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 Returns total RTO load forecast in hourly intervals from MTLF data. .. py:method:: get_load_forecast_by_baa(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 Returns hourly load forecast by BAA from MTLF data. .. py:method:: get_load_forecast_mid_term(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 | None Returns load forecast for +7 days in hourly intervals. Includes actual load for the past 24 hours. Data from https://portal.spp.org/pages/mtlf-vs-actual :param date: date to get data for. Supports "latest" and "today" :type date: pd.Timestamp|str :param verbose: print info :type verbose: bool :returns: forecast as dataframe. :rtype: pd.DataFrame .. py:method:: get_load_forecast_short_term(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, verbose: bool = False, drop_null_forecast_rows: bool = True) -> pandas.DataFrame | None 5-minute load forecast data for the SPP footprint (system-wide) for +/- 10 minutes. Also includes actual load. Data from https://portal.spp.org/pages/stlf-vs-actual :param date: date to get data for. Supports "latest" and "today" :type date: pd.Timestamp|str :param verbose: print info :type verbose: bool :param end: end date :type end: pd.Timestamp|str :param drop_null_forecast_rows: if True, drop rows with null forecast values :type drop_null_forecast_rows: bool :returns: forecast as dataframe. :rtype: pd.DataFrame .. py:method:: get_market_clearing_day_ahead(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 Market Clearing Day Ahead :param date: start date :param end: end date :returns: Market Clearing Day Ahead :rtype: pd.DataFrame .. py:method:: get_market_clearing_real_time(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 Market Clearing Real Time :param date: start date :param end: end date :returns: Market Clearing Real Time :rtype: pd.DataFrame .. py:method:: get_operating_reserves(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 .. py:method:: get_raw_interconnection_queue(verbose: bool = False) -> BinaryIO .. py:method:: get_solar_and_wind_forecast_mid_term(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 | None Returns solar and wind generation forecast for +7 days in hourly intervals. Data from https://portal.spp.org/pages/midterm-resource-forecast. :param date: date to get data for. Supports "latest" and "today" :type date: pd.Timestamp|str :param verbose: print info :type verbose: bool :returns: forecast as dataframe. :rtype: pd.DataFrame .. py:method:: get_solar_and_wind_forecast_short_term(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 | None Returns solar and wind generation forecast for +4 hours in 5 minute intervals. Include actuals for past day in 5 minute intervals. Data from https://portal.spp.org/pages/shortterm-resource-forecast :param date: date to get data for. Supports "latest" and "today" :type date: pd.Timestamp|str :param verbose: print info :type verbose: bool :returns: forecast as dataframe. :rtype: pd.DataFrame .. py:method:: get_ver_curtailments(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 VER Curtailments summed across BAAs. Supports recent data. For historical annual data use get_ver_curtailments_annual. For data broken down by BAA use get_ver_curtailments_by_baa. :param date: start date :param end: end date .. py:method:: get_ver_curtailments_annual(year: int, verbose: bool = True) -> pandas.DataFrame Get VER Curtailments summed across BAAs for a year. Starting 2014. Recent data use get_ver_curtailments. For data broken down by BAA use get_ver_curtailments_by_baa_annual. :param year: year to get data for :param verbose: print url :returns: VER Curtailments :rtype: pd.DataFrame .. py:method:: get_ver_curtailments_by_baa(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 VER Curtailments broken down by BAA. Supports recent data. For historical annual data use get_ver_curtailments_by_baa_annual. :param date: start date :param end: end date .. py:method:: get_ver_curtailments_by_baa_annual(year: int, verbose: bool = True) -> pandas.DataFrame Get VER Curtailments broken down by BAA for a year. Starting 2014. Recent data use get_ver_curtailments_by_baa. :param year: year to get data for :param verbose: print url :returns: VER Curtailments :rtype: pd.DataFrame .. py:method:: get_west_interchange_real_time(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 real-time interchange (tie flow) data for SPP West (SWPW). For "latest" and "today", returns ~2 days of 1-minute interchange data from the real-time endpoint. For historical dates, downloads monthly CSV files from the historical tie flow archive. Data from: - Real-time: https://portal.spp.org/pages/integrated-marketplace-interchange-trend - Historical: https://portal.spp.org/pages/historical-tie-flow :param date: supports "latest", "today", or a historical date/date range :param end: end date for historical range queries :param verbose: print info :returns: interchange data :rtype: pd.DataFrame .. py:method:: now() :staticmethod: .. py:data:: STATUS_STOP_WORDS :value: ['as', 'at', 'ct', 'eea', 'of', 'on']