Spp

Spp#

Module Contents#

Classes Summary#

SPP

Southwest Power Pool (SPP)

Functions#

`add_interval`(→ pandas.DataFrame)	Adds Interval Start and Interval End columns to df
`process_gen_mix`(→ pandas.DataFrame)	Parse SPP generation mix data from

Attributes Summary#

Contents#

gridstatus.spp.add_interval(df: pandas.DataFrame, interval_min: int) → pandas.DataFrame[source]#: Adds Interval Start and Interval End columns to df

gridstatus.spp.BASE_LOAD_FORECAST_MID_TERM_URL = 'https://portal.spp.org/file-browser-api/download/mtlf-vs-actual?path='[source]#

gridstatus.spp.BASE_LOAD_FORECAST_SHORT_TERM_URL = 'https://portal.spp.org/file-browser-api/download/stlf-vs-actual?path='[source]#

gridstatus.spp.BASE_SOLAR_AND_WIND_MID_TERM_URL = 'https://portal.spp.org/file-browser-api/download/midterm-resource-forecast?path='[source]#

gridstatus.spp.BASE_SOLAR_AND_WIND_SHORT_TERM_URL = 'https://portal.spp.org/file-browser-api/download/shortterm-resource-forecast?path='[source]#

gridstatus.spp.DA_BINDING_CONSTRAINTS = 'da-binding-constraints'[source]#

gridstatus.spp.FILE_BROWSER_API_URL = 'https://portal.spp.org/file-browser-api/'[source]#

gridstatus.spp.FILE_BROWSER_DOWNLOAD_URL = 'https://portal.spp.org/file-browser-api/download'[source]#

gridstatus.spp.FS_DAM_LMP_BY_LOCATION = 'da-lmp-by-location'[source]#

gridstatus.spp.FS_RTBM_LMP_BY_LOCATION = 'rtbm-lmp-by-location'[source]#

gridstatus.spp.LAST_UPDATED_KEYWORDS = ['last updated', 'as of'][source]#

gridstatus.spp.LMP_BY_SETTLEMENT_LOCATION_WEIS = 'lmp-by-settlement-location-weis'[source]#

gridstatus.spp.LMP_HUBS_AND_INTERFACES[source]#

gridstatus.spp.LOCATION_TYPE_ALL = 'ALL'[source]#

gridstatus.spp.LOCATION_TYPE_BUS = 'Bus'[source]#

gridstatus.spp.LOCATION_TYPE_HUB = 'Hub'[source]#

gridstatus.spp.LOCATION_TYPE_INTERFACE = 'Interface'[source]#

gridstatus.spp.LOCATION_TYPE_SETTLEMENT_LOCATION = 'Settlement Location'[source]#

gridstatus.spp.MARKETPLACE_BASE_URL = 'https://portal.spp.org'[source]#

gridstatus.spp.OPERATING_RESERVES = 'operating-reserves'[source]#

gridstatus.spp.process_gen_mix(df: pandas.DataFrame, detailed: bool = False) → pandas.DataFrame[source]#

Parse SPP generation mix data from https://marketplace.spp.org/pages/generation-mix-historical

Parameters:

df (pd.DataFrame) – raw data
detailed (bool) – whether to combine market and self columns

Returns:

processed data

Return type:

pd.DataFrame

gridstatus.spp.QUERY_RTM5_HUBS_URL = 'https://pricecontourmap.spp.org/arcgis/rest/services/MarketMaps/RTBM_FeatureData/MapServer/1/query'[source]#

gridstatus.spp.QUERY_RTM5_INTERFACES_URL = 'https://pricecontourmap.spp.org/arcgis/rest/services/MarketMaps/RTBM_FeatureData/MapServer/2/query'[source]#

gridstatus.spp.RELIABILITY_LEVELS = ['Normal Operations', 'Weather Advisory', 'Resource Advisory', 'Conservative Operations...[source]#

gridstatus.spp.RELIABILITY_LEVELS_ALIASES[source]#

gridstatus.spp.RTBM_BINDING_CONSTRAINTS = 'rtbm-binding-constraints'[source]#

gridstatus.spp.RTBM_LMP_BY_BUS = 'rtbm-lmp-by-bus'[source]#

gridstatus.spp.RTBM_MCP = 'rtbm-mcp'[source]#

class gridstatus.spp.SPP[source]#

Bases: gridstatus.base.ISOBase

Southwest Power Pool (SPP)

Attributes

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

`get_as_prices_real_time_5_min`(→ pandas.DataFrame)	Provides Marginal Clearing Price information by Reserve Zone for each
`get_binding_constraints_day_ahead_hourly`(...)	Get Day-Ahead Binding Constraints
`get_binding_constraints_real_time_5_min`(→ pandas.DataFrame)	Get Real-Time Binding Constraints
`get_capacity_of_generation_on_outage`(→ pandas.DataFrame)	Get Capacity of Generation on Outage.
`get_capacity_of_generation_on_outage_annual`(...)	Get VER Curtailments for a year. Starting 2014.
`get_day_ahead_operating_reserve_prices`(→ pandas.DataFrame)	Provides Marginal Clearing Price information by Reserve Zone for each
`get_fuel_mix`(→ pandas.DataFrame)	Get fuel mix
`get_hourly_load`(→ pandas.DataFrame)	Get Hourly Load
`get_hourly_load_annual`(→ pandas.DataFrame)	Get Hourly Load for a year. Starting 2011.
`get_interconnection_queue`(→ pandas.DataFrame)	Get interconnection queue
`get_lmp_day_ahead_hourly`(→ pandas.DataFrame)	Get day ahead hourly LMP data
`get_lmp_real_time_5_min_by_bus`(→ pandas.DataFrame)	Get LMP data by bus for the Real-Time 5 Minute Market
`get_lmp_real_time_5_min_by_location`(→ pandas.DataFrame)	Get LMP data by location for the Real-Time 5 Minute Market
`get_lmp_real_time_weis`(→ pandas.DataFrame)	Get LMP data for real time WEIS
`get_load`(→ pandas.DataFrame)	Returns load for last 24hrs in 5 minute intervals
`get_load_forecast`(→ pandas.DataFrame)	Returns load forecast for next 7 days in hourly intervals
`get_load_forecast_mid_term`(→ pandas.DataFrame \| None)	Returns load forecast for +7 days in hourly intervals. Includes actual load
`get_load_forecast_short_term`(→ pandas.DataFrame \| None)	5-minute load forecast data for the SPP footprint (system-wide) for +/- 10
`get_market_clearing_day_ahead`(→ pandas.DataFrame)	Get Market Clearing Day Ahead
`get_market_clearing_real_time`(→ pandas.DataFrame)	Get Market Clearing Real Time
`get_operating_reserves`(→ pandas.DataFrame)
`get_raw_interconnection_queue`(→ BinaryIO)
`get_solar_and_wind_forecast_mid_term`(...)	Returns solar and wind generation forecast for +7 days in hourly intervals.
`get_solar_and_wind_forecast_short_term`(...)	Returns solar and wind generation forecast for +4 hours in 5 minute intervals.
`get_ver_curtailments`(→ pandas.DataFrame)	Get VER Curtailments
`get_ver_curtailments_annual`(→ pandas.DataFrame)	Get VER Curtailments for a year. Starting 2014.
`now`()

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[source]#

Provides Marginal Clearing Price information by Reserve Zone for each Real-Time 5-minute Market solution.

Parameters:

date – date to get data for. Supports “latest” for most recent interval.
end – end date
verbose – print url
use_daily_files – if True, use daily files instead of 5 minute files.

Returns:

Real-Time 5-minute Marginal Clearing Prices

Return type:

pd.DataFrame

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[source]#

Get Day-Ahead Binding Constraints

Parameters:

date – date to get data for. Supports “latest” for most recently available data.
end – end date
verbose – print url

Returns:

Day-Ahead Binding Constraints

Return type:

pd.DataFrame

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[source]#

Get Real-Time Binding Constraints

Parameters:

date – date to get data for. Supports “latest” for most recent interval.
end – end date
verbose – print url

Returns:

Real-Time Binding Constraints

Return type:

pd.DataFrame

Get Capacity of Generation on Outage.

Published daily at 8am CT for next 7 days

Parameters:

date – start date
end – end date

get_capacity_of_generation_on_outage_annual(year: int, verbose: bool = True) → pandas.DataFrame[source]#

Get VER Curtailments for a year. Starting 2014. Recent data use get_capacity_of_generation_on_outage

Parameters:

year – year to get data for
verbose – print url

Returns:

VER Curtailments

Return type:

pd.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#

Parameters:

date – date to get data for
end – end date
verbose – print url

Returns:

Day Ahead Marginal Clearing Prices

Return type:

pd.DataFrame

get_fuel_mix(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], detailed: bool = False, verbose: bool = False) → pandas.DataFrame[source]#

Get fuel mix

Parameters:

date – supports today and latest
detailed – if True, breaks out self scheduled and market scheduled

Note

if today, returns last 2 hours of data. maybe include previous day

Returns:: fuel mix
Return type:: pd.DataFrame

Get Hourly Load

Supports recent data. For historical annual data use get_hourly_load_annual

Parameters:

date – start date
end – end date

Returns:

Hourly Load

Return type:

pd.DataFrame

get_hourly_load_annual(year: int, verbose: bool = True) → pandas.DataFrame[source]#

Get Hourly Load for a year. Starting 2011. For recent data use get_hourly_load

Parameters:

year – year to get data for
verbose – print url

Returns:

Hourly Load

Return type:

pd.DataFrame

get_interconnection_queue(verbose: bool = False) → pandas.DataFrame[source]#

Get interconnection queue

Returns:: Interconnection queue
Return type:: pandas.DataFrame

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[source]#

Get day ahead hourly LMP data

Supported Location Types:

Hub
Interface
ALL

Get LMP data by bus for the Real-Time 5 Minute Market

Parameters:

date – date to get data for
end – end date
verbose – print url

NOTE: does not take a location_type argument because it always returns LOCATION_TYPE_BUS.

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[source]#

Get LMP data by location for the Real-Time 5 Minute Market

Parameters:

date – date to get data for
end – end date
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)
verbose – print url
use_daily_files – if True, use daily files instead of 5 minute files.

Get LMP data for real time WEIS

Parameters:

date – date to get data for. if end is not provided, will get data for 5 minute interval that date is in.
end – end date
verbose – print url

get_load(date: str | pandas.Timestamp, verbose: bool = False) → pandas.DataFrame[source]#: Returns load for last 24hrs in 5 minute intervals

get_load_forecast(date: str | pandas.Timestamp, forecast_type: str = 'MID_TERM', verbose: bool = False) → pandas.DataFrame[source]#

Returns load forecast for next 7 days in hourly intervals

Parameters:: forecast_type (str) – MID_TERM is hourly for next 7 days or SHORT_TERM is every five minutes for a few hours
Returns:: forecast for current day
Return type:: pd.DataFrame

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

Parameters:

date (pd.Timestamp|str) – date to get data for. Supports “latest” and “today”
verbose (bool) – print info

Returns:

forecast as dataframe.

Return type:

pd.DataFrame

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

Parameters:

date (pd.Timestamp|str) – date to get data for. Supports “latest” and “today”
verbose (bool) – print info
end (pd.Timestamp|str) – end date
drop_null_forecast_rows (bool) – if True, drop rows with null forecast values

Returns:

forecast as dataframe.

Return type:

pd.DataFrame

Get Market Clearing Day Ahead

Parameters:

date – start date
end – end date

Returns:

Market Clearing Day Ahead

Return type:

pd.DataFrame

Get Market Clearing Real Time

Parameters:

date – start date
end – end date

Returns:

Market Clearing Real Time

Return type:

pd.DataFrame

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[source]#

get_raw_interconnection_queue(verbose: bool = False) → BinaryIO[source]#

Returns solar and wind generation forecast for +7 days in hourly intervals.

Data from https://portal.spp.org/pages/midterm-resource-forecast.

Parameters:

date (pd.Timestamp|str) – date to get data for. Supports “latest” and “today”
verbose (bool) – print info

Returns:

forecast as dataframe.

Return type:

pd.DataFrame

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

Parameters:

date (pd.Timestamp|str) – date to get data for. Supports “latest” and “today”
verbose (bool) – print info

Returns:

forecast as dataframe.

Return type:

pd.DataFrame

Get VER Curtailments

Supports recent data. For historical annual data use get_ver_curtailments_annual

Parameters:

date – start date
end – end date

get_ver_curtailments_annual(year: int, verbose: bool = True) → pandas.DataFrame[source]#

Get VER Curtailments for a year. Starting 2014. Recent data use get_ver_curtailments

Parameters:

year – year to get data for
verbose – print url

Returns:

VER Curtailments

Return type:

pd.DataFrame

static now()[source]#

gridstatus.spp.STATUS_STOP_WORDS = ['as', 'at', 'ct', 'eea', 'of', 'on'][source]#

`BASE_LOAD_FORECAST_MID_TERM_URL`
`BASE_LOAD_FORECAST_SHORT_TERM_URL`
`BASE_SOLAR_AND_WIND_MID_TERM_URL`
`BASE_SOLAR_AND_WIND_SHORT_TERM_URL`
`DA_BINDING_CONSTRAINTS`
`FILE_BROWSER_API_URL`
`FILE_BROWSER_DOWNLOAD_URL`
`FS_DAM_LMP_BY_LOCATION`
`FS_RTBM_LMP_BY_LOCATION`
`LAST_UPDATED_KEYWORDS`
`LMP_BY_SETTLEMENT_LOCATION_WEIS`
`LMP_HUBS_AND_INTERFACES`
`LOCATION_TYPE_ALL`
`LOCATION_TYPE_BUS`
`LOCATION_TYPE_HUB`
`LOCATION_TYPE_INTERFACE`
`LOCATION_TYPE_SETTLEMENT_LOCATION`
`MARKETPLACE_BASE_URL`
`OPERATING_RESERVES`
`QUERY_RTM5_HUBS_URL`
`QUERY_RTM5_INTERFACES_URL`
`RELIABILITY_LEVELS`
`RELIABILITY_LEVELS_ALIASES`
`RTBM_BINDING_CONSTRAINTS`
`RTBM_LMP_BY_BUS`
`RTBM_MCP`
`STATUS_STOP_WORDS`