Spp#

Module Contents#

Classes Summary#

SPP

Southwest Power Pool (SPP)

Functions#

`add_interval`	Adds Interval Start and Interval End columns to df
`process_gen_mix`	Parse SPP generation mix data from

Attributes Summary#

`FILE_BROWSER_API_URL`
`FS_DAM_LMP_BY_LOCATION`
`FS_RTBM_LMP_BY_LOCATION`
`LAST_UPDATED_KEYWORDS`
`LOCATION_TYPE_HUB`
`LOCATION_TYPE_INTERFACE`
`LOCATION_TYPE_SETTLEMENT_LOCATION`
`MARKETPLACE_BASE_URL`
`QUERY_RTM5_HUBS_URL`
`QUERY_RTM5_INTERFACES_URL`
`RELIABILITY_LEVELS`
`RELIABILITY_LEVELS_ALIASES`
`STATUS_STOP_WORDS`

Contents#

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

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

gridstatus.spp.process_gen_mix(df, detailed=False)[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]#

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_fuel_mix`	Get fuel mix
`get_interconnection_queue`	Get interconnection queue
`get_lmp`	Get LMP data
`get_load`	Returns load for last 24hrs in 5 minute intervals
`get_load_forecast`	Returns load forecast for next 7 days in hourly intervals
`get_status`
`get_ver_curtailments`	Get VER Curtailments
`get_ver_curtailments_annual`	Get VER Curtailments for a year. Starting 2014.

get_fuel_mix(date, detailed=False, verbose=False)[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_interconnection_queue(verbose=False)[source]#

Get interconnection queue

Returns: Interconnection queue
Return type: pandas.DataFrame

get_lmp(date, end=None, market: str = None, locations: list = 'ALL', location_type: str = LOCATION_TYPE_HUB, verbose=False)[source]#

Get LMP data

Supported Markets:

REAL_TIME_5_MIN
DAY_AHEAD_HOURLY

Supported Location Types:

hub
interface
settlement_location

get_load(date, verbose=False)[source]#: Returns load for last 24hrs in 5 minute intervals

get_load_forecast(date, forecast_type='MID_TERM', verbose=False)[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

get_status(date=None, verbose=False)[source]#

get_ver_curtailments(date, end=None, verbose=False)[source]#

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, verbose=True)[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

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