Gridstatus

Gridstatus#

Submodules#

Package Contents#

Classes Summary#

`CAISO`	California Independent System Operator (CAISO)
`Ercot`	Electric Reliability Council of Texas (ERCOT)
`IESO`	Independent Electricity System Operator (IESO)
`ISONE`	ISO New England (ISONE)
`Markets`	Names of LMP Markets
`MISO`	Midcontinent Independent System Operator (MISO)
`NYISO`	New York Independent System Operator (NYISO)
`PJM`	PJM
`SPP`	Southwest Power Pool (SPP)

Exceptions Summary#

Functions#

`get_iso`	Get an ISO by its id
`list_isos`	List available ISOs
`load_folder`	Load a single DataFrame for same schema csv files in a folder

Contents#

class gridstatus.CAISO[source]#

Bases: gridstatus.base.ISOBase

California Independent System Operator (CAISO)

Attributes

default_timezone	US/Pacific
interconnection_homepage	https://rimspub.caiso.com/rimsui/logon.do
iso_id	caiso
markets	None
name	California ISO
status_homepage	https://www.caiso.com/TodaysOutlook/Pages/default.aspx
trading_hub_locations	[‘TH_NP15_GEN-APND’, ‘TH_SP15_GEN-APND’, ‘TH_ZP26_GEN-APND’]

Methods

`get_as_prices`	Return AS prices for a given date for each region
`get_as_procurement`	Get ancillary services procurement data from CAISO.
`get_curtailed_non_operational_generator_report`	Return curtailed non-operational generator report for a given date.
`get_curtailment`	Return curtailment data for a given date
`get_fuel_mix`	Get fuel mix in 5 minute intervals for a provided day
`get_gas_prices`	Return gas prices at a previous date
`get_ghg_allowance`	Return ghg allowance at a previous date
`get_interconnection_queue`
`get_lmp`	Get LMP pricing starting at supplied date for a list of locations.
`get_load`	Return load at a previous date in 5 minute intervals
`get_load_forecast`	Returns load forecast for a previous date in 1 hour intervals
`get_oasis_dataset`	Return data from OASIS for a given dataset
`get_pnodes`
`get_raw_interconnection_queue`
`get_stats`
`get_status`	Get Current Status of the Grid. Only date="latest" is supported
`get_storage`	Return storage charging or discharging for today in 5 minute intervals
`list_oasis_datasets`

get_as_prices(date, end=None, market='DAM', sleep=4, verbose=False)[source]#

Return AS prices for a given date for each region

Parameters:

date (datetime.date, str) – date to return data
end (datetime.date, str) – last date of range to return data. If None, returns only date. Defaults to None.
market (str) – DAM or HASP. Defaults to DAM.
verbose (bool, optional) – print out url being fetched. Defaults to False.

Returns:

A DataFrame of AS prices

Return type:

pandas.DataFrame

get_as_procurement(date, end=None, market='DAM', sleep=4, verbose=False)[source]#

Get ancillary services procurement data from CAISO.

Parameters:

date (datetime.date, str) – date to return data
end (datetime.date, str) – last date of range to return data. If None, returns only date. Defaults to None.
market – DAM or RTM. Defaults to DAM.

Returns:

A DataFrame of ancillary services data

Return type:

pandas.DataFrame

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

Return curtailed non-operational generator report for a given date.: Earliest available date is June 17, 2021.

Parameters:

date (datetime.date, str) – date to return data
end (datetime.date, str) – last date of range to return data. If None, returns only date. Defaults to None.
verbose (bool, optional) – print out url being fetched. Defaults to False.

Returns:

A DataFrame of curtailed non-operational generator report

column glossary: http://www.caiso.com/market/Pages/OutageManagement/Curtailed -OperationalGeneratorReportGlossary.aspx

if requesting multiple days, may want to run following to remove outages that get reported across multiple days: ```df.drop_duplicates(

subset=[“OUTAGE MRID”, “CURTAILMENT START DATE TIME”], keep=”last”)```

Return type:

pandas.DataFrame

get_curtailment(date, verbose=False)[source]#

Return curtailment data for a given date

Notes

requires java to be installed in order to run
Data available from June 30, 2016 to present

Parameters:

date (datetime.date, str) – date to return data
end (datetime.date, str) – last date of range to return data. If None, returns only date. Defaults to None.
verbose – print out url being fetched. Defaults to False.

Returns:

A DataFrame of curtailment data

Return type:

pandas.DataFrame

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

Get fuel mix in 5 minute intervals for a provided day

Parameters:

date (datetime.date, str) – “latest”, “today”, or an object that can be parsed as a datetime for the day to return data.
start (datetime.date, str) – start of date range to return. alias for date parameter. Only specify one of date or start.
end (datetime.date, str) – “today” or an object that can be parsed as a datetime for the day to return data. Only used if requesting a range of dates.
verbose (bool, optional) – print verbose output. Defaults to False.

Returns:

A DataFrame with columns - ‘Time’ and columns for each fuel type.

Return type:

pandas.DataFrame

get_gas_prices(date, end=None, fuel_region_id='ALL', sleep=4, verbose=False)[source]#

Return gas prices at a previous date

Parameters:

date (datetime.date, str) – date to return data
end (datetime.date, str) – last date of range to return data. If None, returns only date. Defaults to None.
fuel_region_id (str, or list) – single fuel region id or list of fuel region ids to return data for. Defaults to ALL, which returns all fuel regions.

Returns:

A DataFrame of gas prices

Return type:

pandas.DataFrame

get_ghg_allowance(date, end=None, sleep=4, verbose=False)[source]#

Return ghg allowance at a previous date

Parameters:

date (datetime.date, str) – date to return data
end (datetime.date, str) – last date of range to return data. If None, returns only date. Defaults to None.

get_interconnection_queue(verbose=False)[source]#

get_lmp(date, market: str, locations: list = None, sleep: int = 5, end=None, verbose=False)[source]#

Get LMP pricing starting at supplied date for a list of locations.

Parameters:

date (datetime.date, str) – date to return data
market – market to return from. supports:
locations (list) – list of locations to get data from. If no locations are provided, defaults to NP15, SP15, and ZP26, which are the trading hub locations. USE “ALL_AP_NODES” for all Aggregate Pricing Node. Use “ALL” to get all nodes. For a list of locations, call CAISO.get_pnodes()
sleep (int) – number of seconds to sleep before returning to avoid hitting rate limit in regular usage. Defaults to 5 seconds.

Returns:

A DataFrame of pricing data

Return type:

pandas.DataFrame

get_load(date, end=None, verbose=False)[source]#: Return load at a previous date in 5 minute intervals

get_load_forecast(date, end=None, sleep=4, verbose=False)[source]#

Returns load forecast for a previous date in 1 hour intervals

Parameters:

date (datetime.date, pd.Timestamp, str) – day to return. If string, format should be YYYYMMDD e.g 20200623
sleep (int) – number of seconds to sleep before returning to avoid hitting rate limit in regular usage. Defaults to 5 seconds.

get_oasis_dataset(dataset, date, end=None, params=None, raw_data=True, sleep=5, verbose=False)[source]#

Return data from OASIS for a given dataset

Parameters:

dataset (str) – dataset to return data for. See CAISO.list_oasis_datasets for supported datasets
date (datetime.date, str) – date to return data
end (datetime.date, str) – last date of range to return data. If None, returns only date. Defaults to None.
params (dict) – dictionary of parameters to pass to dataset. See CAISO.list_oasis_datasets for supported parameters
raw_data (bool, optional) – return raw data from OASIS. Defaults to True.
sleep (int, optional) – number of seconds to sleep between requests. Defaults to 5.
verbose (bool, optional) – print out url being fetched. Defaults to False.

Returns:

A DataFrame of OASIS data

Return type:

pandas.DataFrame

get_pnodes(verbose=False)[source]#

get_raw_interconnection_queue(verbose)[source]#

get_stats(verbose=False)[source]#

get_status(date='latest', verbose=False) → str[source]#

Get Current Status of the Grid. Only date=”latest” is supported

Known possible values: Normal, Restricted Maintenance Operations, Flex Alert

get_storage(date, verbose=False)[source]#

Return storage charging or discharging for today in 5 minute intervals

Negative means charging, positive means discharging

Parameters:: date (datetime.date, str) – date to return data

list_oasis_datasets(dataset=None)[source]#

class gridstatus.Ercot[source]#

Bases: gridstatus.base.ISOBase

Electric Reliability Council of Texas (ERCOT)

Attributes

ACTUAL_LOADS_FORECAST_ZONES_URL_FORMAT	https://www.ercot.com/content/cdr/html/{timestamp}_actual_loads_of_forecast_zones.html
ACTUAL_LOADS_WEATHER_ZONES_URL_FORMAT	https://www.ercot.com/content/cdr/html/{timestamp}_actual_loads_of_weather_zones.html
BASE	https://www.ercot.com/api/1/services/read/dashboards
default_timezone	US/Central
interconnection_homepage	http://mis.ercot.com/misapp/GetReports.do?reportTypeId=15933
iso_id	ercot
LOAD_HISTORICAL_MAX_DAYS	14
location_types	None
markets	None
name	Electric Reliability Council of Texas
status_homepage	https://www.ercot.com/gridmktinfo/dashboards/gridconditions

Methods

`get_60_day_dam_disclosure`	Get 60 day DAM Disclosure data
`get_60_day_sced_disclosure`	Get 60 day SCED Disclosure data
`get_as_monitor`	Get Ancillary Service Capacity Monitor.
`get_as_prices`	Get ancillary service clearing prices in hourly intervals in Day Ahead Market
`get_as_reports`	Get Ancillary Services Reports.
`get_dam_price_corrections`	Get RTM Price Corrections
`get_dam_spp`	Get Historical DAM Settlement Point Prices(SPPs)
`get_dam_system_lambda`	Get Day-Ahead Market System Lambda
`get_energy_storage_resources`	Get energy storage resources.
`get_fuel_mix`	Get fuel mix 5 minute intervals
`get_highest_price_as_offer_selected`	Get the offer price and the name of the Entity submitting
`get_hourly_resource_outage_capacity`	Hourly Resource Outage Capacity report sourced
`get_hourly_solar_report`	Get Hourly Solar Report.
`get_hourly_wind_report`	Get Hourly Wind Report.
`get_interconnection_queue`	Get interconnection queue for ERCOT
`get_lmp`	Get LMP data for ERCOT normally produced by SCED every five minutes
`get_load`	Get load for a date
`get_load_by_forecast_zone`	Get hourly load for ERCOT forecast zones
`get_load_by_weather_zone`	Get hourly load for ERCOT weather zones
`get_load_forecast`	Returns load forecast of specified forecast type.
`get_raw_interconnection_queue`
`get_real_time_system_conditions`	Get Real-Time System Conditions.
`get_rtm_price_corrections`	Get RTM Price Corrections
`get_rtm_spp`	Get Historical RTM Settlement Point Prices(SPPs)
`get_sara`	Parse SARA data from url.
`get_sced_system_lambda`	Get System lambda of each successful SCED
`get_spp`	Get SPP data for ERCOT
`get_status`	Returns status of grid
`get_system_wide_actual_load`	Get 15-minute system-wide actual load.
`get_unplanned_resource_outages`	Get Unplanned Resource Outages.
`parse_doc`
`read_doc`
`read_docs`

get_60_day_dam_disclosure(date, end=None, process=False, verbose=False)[source]#: Get 60 day DAM Disclosure data

get_60_day_sced_disclosure(date, end=None, process=False, verbose=False)[source]#

Get 60 day SCED Disclosure data

Parameters:

date (datetime.date, str) – date to return
end (datetime.date, str, optional) – if declared, function will return data as a range, from “date” to “end”
process (bool, optional) – if True, will process the data into standardized format. if False, will return raw data
verbose (bool, optional) – print verbose output. Defaults to False.

Returns:

dictionary with keys “sced_load_resource”, “sced_gen_resource”, and: ”sced_smne”, mapping to pandas.DataFrame objects

Return type:

dict

get_as_monitor(date='latest', verbose=False)[source]#

Get Ancillary Service Capacity Monitor.

Parses table from https://www.ercot.com/content/cdr/html/as_capacity_monitor.html

Parameters:

date (str) – only supports “latest”
verbose (bool, optional) – print verbose output. Defaults to False.

Returns:

A DataFrame with ancillary service capacity monitor data

Return type:

pandas.DataFrame

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

Get ancillary service clearing prices in hourly intervals in Day Ahead Market

Parameters:

date (datetime.date, str) – date of delivery for AS services
end (datetime.date, str, optional) – if declared, function will return data as a range, from “date” to “end”
verbose (bool, optional) – print verbose output. Defaults to False.

Returns:

A DataFrame with prices for “Non-Spinning Reserves”, “Regulation Up”, “Regulation Down”, “Responsive Reserves”, “ERCOT Contingency Reserve Service”

Return type:

pandas.DataFrame

get_as_reports(date, verbose=False)[source]#

Get Ancillary Services Reports.

Published with a 2 day delay around 3am central

get_dam_price_corrections(dam_type, verbose=False)[source]#

Get RTM Price Corrections

Parameters:: rtm_type (str) – ‘DAM_SPP’, ‘DAM_MCPC’, ‘DAM_EBLMP’

get_dam_spp(year, verbose=False)[source]#

Get Historical DAM Settlement Point Prices(SPPs) for each of the Hubs and Load Zones

Parameters:: year (int) – year to get data for. Starting 2011, returns data for the entire year

Source:: https://www.ercot.com/mp/data-products/data-product-details?id=NP4-180-ER

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

Get Day-Ahead Market System Lambda

File is typically published around 12:30 pm for the day ahead

https://www.ercot.com/mp/data-products/data-product-details?id=NP4-523-CD

Parameters:

date (str, datetime) – date to get data for
end (str, datetime, optional) – end time to get data for. If None, return 1 day of data. Defaults to None.
verbose (bool, optional) – print verbose output. Defaults to False.

Returns:

A DataFrame with day-ahead market system lambda data

Return type:

pandas.DataFrame

get_energy_storage_resources(date='latest', verbose=False)[source]#: Get energy storage resources. Always returns data from previous and current day

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

Get fuel mix 5 minute intervals

Parameters:

date (datetime.date, str) – “latest”, “today”, and yesterday’s date are supported.
verbose (bool) – print verbose output. Defaults to False.

Returns:

A DataFrame with columns; Time and columns for each fuel type

Return type:

pandas.DataFrame

get_highest_price_as_offer_selected(date, verbose=False)[source]#

Get the offer price and the name of the Entity submitting the offer for the highest-priced Ancillary Service (AS) Offer.

Published with 3 day delay

Parameters:

date (str, datetime) – date to get data for
verbose (bool, optional) – print verbose output. Defaults to False.

Returns:

A DataFrameq

Return type:

pandas.DataFrame

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

Hourly Resource Outage Capacity report sourced from the Outage Scheduler (OS).

Returns outage data for for next 7 days.

Total Resource MW doesn’t include IRR, New Equipment outages, retirement of old equipment, seasonal mothballed (during the outage season), and mothballed.

As such, it is a proxy for thermal outages.

Parameters:

date (str) – time to download. Returns last hourly report before this time. Supports “latest”
end (str, optional) – end time to download. Defaults to None.
verbose (bool, optional) – print verbose output. Defaults to False.

Returns:

A DataFrame with hourly resource outage capacity data

Return type:

pandas.DataFrame

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

Get Hourly Solar Report.

Posted every hour and includes System-wide and geographic regional hourly averaged solar power production, STPPF, PVGRPP, and COP HSL for On-Line PVGRs for a rolling historical 48-hour period as well as the system-wide and regional STPPF, PVGRPP, and COP HSL for On-Line PVGRs for the rolling future 168-hour period.

Parameters:

date (str) – date to get report for. Supports “latest” or a date string
end (str, optional) – end date for date range. Defaults to None.
verbose (bool, optional) – print verbose output. Defaults to False.

Returns:

A DataFrame with hourly solar report data

Return type:

pandas.DataFrame

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

Get Hourly Wind Report.

This report is posted every hour and includes System-wide and Regional actual hourly averaged wind power production, STWPF, WGRPP and COP HSLs for On-Line WGRs for a rolling historical 48-hour period as well as the System-wide and Regional STWPF, WGRPP and COP HSLs for On-Line WGRs for the rolling future 168-hour period. Our forecasts attempt to predict HSL, which is uncurtailed power generation potential.

Parameters:

date (str) – date to get report for. Supports “latest”
verbose (bool, optional) – print verbose output. Defaults to False.

Returns:

A DataFrame with hourly wind report data

Return type:

pandas.DataFrame

get_interconnection_queue(verbose=False)[source]#

Get interconnection queue for ERCOT

Monthly historical data available here:: http://mis.ercot.com/misapp/GetReports.do?reportTypeId=15933&reportTitle=GIS%20Report&showHTMLView=&mimicKey

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

Get LMP data for ERCOT normally produced by SCED every five minutes

Can specify the location type to return “electrical bus” or “settlement point” data

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

Get load for a date

Parameters:: date (datetime.date, str) – “latest”, “today”, or a date string are supported.

get_load_by_forecast_zone(date, verbose=False)[source]#

Get hourly load for ERCOT forecast zones

Parameters:

date (datetime.date, str) – “today”, or a date string are supported.
verbose (bool) – print verbose output. Defaults to False.

Returns:

pandas.DataFrame

get_load_by_weather_zone(date, verbose=False)[source]#

Get hourly load for ERCOT weather zones

Parameters:

date (datetime.date, str) – “today”, or a date string are supported.
verbose (bool) – print verbose output. Defaults to False.

Returns:

pandas.DataFrame

get_load_forecast(date, end=None, forecast_type=ERCOTSevenDayLoadForecastReport.BY_FORECAST_ZONE, verbose=False)[source]#

Returns load forecast of specified forecast type.

If date range provided, returns all hourly reports published within.

Note: only limited historical data is available

Parameters:

date (str, datetime) – datetime to download. If end not provided, returns last hourly report published before. if “latest”, returns most recent hourly report. if end provided, returns all hourly reports published after this date and before end.
end (str, datetime,) – if provided, returns all hourly reports published after date and before end
forecast_type (ERCOTSevenDayLoadForecastReport) – The load forecast type. Enum of possible values.
verbose (bool, optional) – print verbose output. Defaults to False.

get_raw_interconnection_queue(verbose=False)[source]#

get_real_time_system_conditions(date='latest', verbose=False)[source]#

Get Real-Time System Conditions.

Parses table from https://www.ercot.com/content/cdr/html/real_time_system_conditions.html

Parameters:

date (str) – only supports “latest”
verbose (bool, optional) – print verbose output. Defaults to False.

Returns:

A DataFrame with real-time system conditions

Return type:

pandas.DataFrame

get_rtm_price_corrections(rtm_type, verbose=False)[source]#

Get RTM Price Corrections

Parameters:: rtm_type (str) – ‘RTM_SPP’, ‘RTM_SPLMP’, ‘RTM_EBLMP’, ‘RTM_ShadowPrice’, ‘RTM_SOGLMP’, ‘RTM_SOGPRICE’

get_rtm_spp(year, verbose=False)[source]#

Get Historical RTM Settlement Point Prices(SPPs): for each of the Hubs and Load Zones

Parameters:: year (int) – year to get data for Starting 2011, returns data for the entire year

Source:: https://www.ercot.com/mp/data-products/data-product-details?id=NP6-785-ER

get_sara(url='https://www.ercot.com/files/docs/2023/05/05/SARA_Summer2023_Revised.xlsx', verbose=False)[source]#

Parse SARA data from url.

Seasonal Assessment of Resource Adequacy for the ERCOT Region (SARA)

Parameters:: url (str, optional) – url to download SARA data from. Defaults to Summer 2023 SARA data.

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

Get System lambda of each successful SCED

Normally published every 5 minutes

Parameters:

date (str, datetime, pd.Timestamp) – date or start time to get data for
end (str, datetime, optional) – end time to get data for. If None, return 1 day of data. Defaults to None.
verbose (bool, optional) – print verbose output. Defaults to False.

Returns:

A DataFrame

Return type:

pandas.DataFrame

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

Get SPP data for ERCOT

Supported Markets:

REAL_TIME_15_MIN
DAY_AHEAD_HOURLY

Supported Location Types:

Load Zone
Trading Hub
Resource Node

get_status(date, verbose=False)[source]#: Returns status of grid

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

Get 15-minute system-wide actual load.

This report is posted every hour five minutes after the hour.

Parameters:

date (str, datetime) – date to get data for
end (str, datetime, optional) – end time to get data for. Defaults to None.
verbose (bool, optional) – print verbose output. Defaults to False.

Returns:

A DataFrame with system actuals data

Return type:

pandas.DataFrame

get_unplanned_resource_outages(date, verbose=False)[source]#

Get Unplanned Resource Outages.

Data published at ~5am central on the 3rd day after the day of interest.

Parameters:

date (str, datetime) – date to get data for
verbose (bool, optional) – print verbose output. Defaults to False.

Returns:

A DataFrame with unplanned resource outages

Return type:

pandas.DataFrame

parse_doc(doc, dst_ambiguous_default='infer', verbose=False)[source]#

read_doc(doc, parse=True, verbose=False)[source]#

read_docs(docs, parse=True, empty_df=None, verbose=False)[source]#

gridstatus.get_iso(iso_id)[source]#: Get an ISO by its id

class gridstatus.IESO[source]#

Bases: gridstatus.base.ISOBase

Independent Electricity System Operator (IESO)

Attributes

default_timezone	EST
iso_id	ieso
name	Independent Electricity System Operator
status_homepage	https://www.ieso.ca/en/Power-Data

Methods

`get_fuel_mix`	Hourly output and capability for each fuel type (summed over all generators)
`get_generator_report_hourly`	Hourly output for each generator for a given date or from date to end.
`get_load`	Get 5-minute load for the Market and Ontario for a given date or from
`get_load_forecast`	Get forecasted load for Ontario. Supports only "latest" and "today" because
`get_zonal_load_forecast`	Get forecasted load by forecast zone (Ontario, East, West) for a given date

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

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.

Parameters:

date (datetime.date | datetime.datetime | str) – 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.
end (datetime.date | datetime.datetime, optional) – End date. Defaults None If provided, returns data from date to end date. The end can be a datetime.date or datetime.datetime object.
verbose (bool, optional) – Print verbose output. Defaults to False.

Returns:

fuel mix

Return type:

pd.DataFrame

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

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.

Parameters:

date (datetime.date | datetime.datetime | str) – 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.
end (datetime.date | datetime.datetime, optional) – End date. Defaults None If provided, returns data from date to end date. The end can be a datetime.date or datetime.datetime object.
verbose (bool, optional) – Print verbose output. Defaults to False.

Returns:

generator output and capability/available capacity

Return type:

pd.DataFrame

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

Get 5-minute load for the Market and Ontario for a given date or from date to end date.

Parameters:

date (datetime.date | datetime.datetime | str) – 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.
end (datetime.date | datetime.datetime, optional) – End date. Defaults None If provided, returns data from date to end date. The end can be a datetime.date or datetime.datetime object.
verbose (bool, optional) – Print verbose output. Defaults to False.
frequency (str, optional) – Frequency of data. Defaults to “5min”.

Returns:

zonal load as a wide table with columns for each zone

Return type:

pd.DataFrame

get_load_forecast(date, verbose=False)[source]#

Get forecasted load for Ontario. Supports only “latest” and “today” because there is only one load forecast.

Parameters:

date (str) – Either “today” or “latest”
verbose (bool, optional) – Print verbose output. Defaults to False.

Returns:

Ontario load forecast

Return type:

pd.DataFrame

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

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.

Parameters:

date (datetime.date | datetime.datetime | str) – 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.
end (datetime.date | datetime.datetime, optional) – End date. Defaults None If provided, returns data from date to end date. The end can be a datetime.date or datetime.datetime object.
verbose (bool, optional) – Print verbose output. Defaults to False.

Returns:

forecasted load as a wide table with columns for each zone

Return type:

pd.DataFrame

class gridstatus.ISONE[source]#

Bases: gridstatus.base.ISOBase

ISO New England (ISONE)

Attributes

default_timezone	US/Eastern
hubs	None
interconnection_homepage	https://irtt.iso-ne.com/reports/external
interfaces	None
iso_id	isone
markets	None
name	ISO New England
status_homepage	https://www.iso-ne.com/markets-operations/system-forecast-status/current-system-status
zones	None

Methods

`get_btm_solar`	Return BTM solar at a previous date in 5 minute intervals
`get_fuel_mix`	Return fuel mix at a previous date
`get_interconnection_queue`	Get the interconnection queue. Contains active and withdrawm applications.
`get_lmp`	Find Node ID mapping:
`get_load`	Return load at a previous date in 5 minute intervals
`get_load_forecast`	Return forecast at a previous date
`get_raw_interconnection_queue`	Extract raw ISONE interconnection queue data.
`get_solar_forecast`	Return solar forecast published on a specific date
`get_status`	Get latest status for ISO NE
`get_wind_forecast`	Return wind forecast published on a specific date

get_btm_solar(date, end=None, verbose=False)[source]#: Return BTM solar at a previous date in 5 minute intervals

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

Return fuel mix at a previous date

Provided at frequent, but irregular intervals by ISONE

get_interconnection_queue(verbose=False)[source]#

Get the interconnection queue. Contains active and withdrawm applications.

More information: https://www.iso-ne.com/system-planning/interconnection-service/interconnection-request-queue/

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

get_lmp(date, end=None, market: str = None, locations: list = None, include_id=False, verbose=False)[source]#

Find Node ID mapping:: https://www.iso-ne.com/markets-operations/settlements/pricing-node-tables/

get_load(date, end=None, verbose=False)[source]#: Return load at a previous date in 5 minute intervals

get_load_forecast(date, end=None, verbose=False)[source]#: Return forecast at a previous date

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

Extract raw ISONE interconnection queue data.

ISONE interconnection queue data is available on a webpage as an HTML table or you can download it as an excel file. Obviously an excel file would be much easier to work with however, the helpful generalized “Status” column (Withdrawn, Active, Commercial) and the “Jurisdiction” column are only available as HTML.

Also, there is helpful detailed status information in the FS, SIS, OS, FAC, IA columns that are represented as <img> tags in the HTML.

This function replaces the <img> tags that convey detailed status information as text and extracts the html as a dataframe. You can see the image to text mapping in the upper left hand corner of the ISONE Queue data page: https://irtt.iso-ne.com/reports/external.

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

Return solar forecast published on a specific date

Forecast is published for 7 days and generated daily by 10 am. https://www.iso-ne.com/isoexpress/web/reports/operations/-/tree/seven-day-solar-power-forecast

get_status(date, verbose=False)[source]#: Get latest status for ISO NE

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

Return wind forecast published on a specific date

Forecast is published for 7 days and generated daily by 10 am. https://www.iso-ne.com/isoexpress/web/reports/operations/-/tree/seven-day-wind-power-forecast

gridstatus.list_isos()[source]#: List available ISOs

gridstatus.load_folder(path, time_zone=None, verbose=True)[source]#

Load a single DataFrame for same schema csv files in a folder

Parameters:

path (str) – path to folder
time_zone (str) – time zone to localize to timestamps. By default returns as UTC
verbose (bool, optional) – print verbose output. Defaults to True.

Returns:

A DataFrame of all files

Return type:

pandas.DataFrame

class gridstatus.Markets[source]#

Bases: enum.Enum

Names of LMP Markets

Attributes

DAY_AHEAD_HOURLY	DAY_AHEAD_HOURLY
REAL_TIME_15_MIN	REAL_TIME_15_MIN
REAL_TIME_5_MIN	REAL_TIME_5_MIN
REAL_TIME_HOURLY	REAL_TIME_HOURLY
REAL_TIME_SCED	REAL_TIME_SCED

Methods

__contains__

__contains__(item)[source]#

class gridstatus.MISO[source]#

Bases: gridstatus.base.ISOBase

Midcontinent Independent System Operator (MISO)

Attributes

BASE	https://api.misoenergy.org/MISORTWDDataBroker/DataBrokerServices.asmx
default_timezone	US/Eastern
hubs	[‘ILLINOIS.HUB’, ‘INDIANA.HUB’, ‘LOUISIANA.HUB’, ‘MICHIGAN.HUB’, ‘MINN.HUB’, ‘MS.HUB’, ‘TEXAS.HUB’, ‘ARKANSAS.HUB’]
interconnection_homepage	https://www.misoenergy.org/planning/generator-interconnection/GI_Queue/
iso_id	miso
markets	None
name	Midcontinent ISO

Methods

`get_fuel_mix`	Get the fuel mix for a given day for a provided MISO.
`get_interconnection_queue`	Get the interconnection queue
`get_lmp`
`get_load`
`get_load_forecast`
`get_raw_interconnection_queue`

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

Get the fuel mix for a given day for a provided MISO.

Parameters:

date (datetime.date, str) – “latest”, “today”, or an object that can be parsed as a datetime for the day to return data.
verbose (bool, optional) – print verbose output. Defaults to False.

Returns:

DataFrame with columns “Time”, “Load”, “Fuel Mix”

Return type:

pandas.DataFrame

get_interconnection_queue(verbose=False)[source]#

Get the interconnection queue

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

get_lmp(date, market: str, locations: list = 'ALL', verbose=False)[source]#

get_load(date, verbose=False)[source]#

get_load_forecast(date, verbose=False)[source]#

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

exception gridstatus.NotSupported[source]#

Bases: Exception

Common base class for all non-exit exceptions.

Initialize self. See help(type(self)) for accurate signature.

class gridstatus.NYISO[source]#

Bases: gridstatus.base.ISOBase

New York Independent System Operator (NYISO)

Attributes

default_timezone	US/Eastern
interconnection_homepage	https://www.nyiso.com/interconnections
iso_id	nyiso
markets	None
name	New York ISO
status_homepage	https://www.nyiso.com/system-conditions

Methods

`get_btm_solar`	Returns estimated BTM solar generation at a previous date in hourly
`get_btm_solar_forecast`
`get_capacity_prices`	Pull the most recent capacity market report's market clearing prices
`get_fuel_mix`
`get_generators`	Get a list of generators in NYISO
`get_interconnection_queue`	Return NYISO interconnection queue
`get_lmp`	Supported Markets:
`get_load`	Returns load at a previous date in 5 minute intervals for
`get_load_forecast`	Get load forecast for a date in 1 hour intervals
`get_loads`	Get a list of loads in NYISO
`get_raw_interconnection_queue`
`get_status`

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

Returns estimated BTM solar generation at a previous date in hourly

intervals for system and each zone.

Available ~8 hours after the end of the operating day.

Parameters:

date (str, pd.Timestamp, datetime.datetime) – Date to get load for. Can be “latest”, “today”, or a date
end (str, pd.Timestamp, datetime.datetime) – End date for date range. Optional.
verbose (bool) – Whether to print verbose output. Optional.

Returns:

BTM solar data for NYISO system and each zone

Return type:

pandas.DataFrame

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

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

Pull the most recent capacity market report’s market clearing prices

Parameters:

date (pandas.Timestamp) – date that will be used to pull latest capacity report (will refer to month and year)
verbose (bool, optional) – print out requested url. Defaults to False.

Returns:

a DataFrame of monthly capacity prices (all three auctions) for each of the four capacity localities within NYISO

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

get_generators(verbose=False)[source]#

Get a list of generators in NYISO

When possible return capacity and fuel type information

Parameters:

verbose (bool, optional) – print out requested url. Defaults to False.

Returns:

a DataFrame of generators and locations

Possible Columns

Generator Name
PTID
Subzone
Zone
Latitude
Longitude
Owner, Operator, and / or Billing Organization
Station Unit
Town
County
State
In-Service Date
Name Plate Rating (V) MW
2022 CRIS MW Summer
2022 CRIS MW Winter
2022 Capability MW Summer
2022 Capability MW Winter
Is Dual Fuel
Unit Type
Fuel Type 1
Fuel Type 2
2021 Net Energy GWh
Notes
Generator Type

Return type:

pandas.DataFrame

get_interconnection_queue(verbose=False)[source]#

Return NYISO interconnection queue

Additional Non-NYISO queue info: https://www3.dps.ny.gov/W/PSCWeb.nsf/All/286D2C179E9A5A8385257FBF003F1F7E?OpenDocument

Returns:: Interconnection queue containing, active, withdrawn, and completed project
Return type:: pandas.DataFrame

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

Supported Markets:

REAL_TIME_5_MIN (RTC)
REAL_TIME_15_MIN (RTD)
DAY_AHEAD_HOURLY

Supported Location Types:

zone
generator

REAL_TIME_5_MIN is the Real Time Dispatch (RTD) market. REAL_TIME_15_MIN is the Real Time Commitment (RTC) market. For documentation on real time dispatch and real time commitment, see: https://www.nyiso.com/documents/20142/1404816/RTC-RTD%20Convergence%20Study.pdf/f3843982-dd30-4c66-6c21-e101c3cb85af

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

Returns load at a previous date in 5 minute intervals for: each zone and total load

Parameters:

date (str) – Date to get load for. Can be “latest”, “today”, or a date in the format YYYY-MM-DD
end (str) – End date for date range. Optional.
verbose (bool) – Whether to print verbose output. Optional.

Returns:

Load data for NYISO and each zone

Return type:

pandas.DataFrame

get_load_forecast(date, end=None, verbose=False)[source]#: Get load forecast for a date in 1 hour intervals

get_loads(verbose=False)[source]#

Get a list of loads in NYISO

Parameters:: verbose (bool, optional) – print out requested url. Defaults to False.
Returns:: a DataFrame of loads and locations
Return type:: pandas.DataFrame

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

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

class gridstatus.PJM[source]#

Bases: gridstatus.base.ISOBase

PJM

Attributes

default_timezone	US/Eastern
hub_node_ids	[‘51217’, ‘116013751’, ‘35010337’, ‘34497151’, ‘34497127’, ‘34497125’, ‘33092315’, ‘33092313’, ‘33092311’, ‘4669664’, ‘51288’, ‘51287’]
interconnection_homepage	https://www.pjm.com/planning/service-requests/services-request-status
iso_id	pjm
location_types	[‘ZONE’, ‘LOAD’, ‘GEN’, ‘AGGREGATE’, ‘INTERFACE’, ‘EXT’, ‘HUB’, ‘EHV’, ‘TIE’, ‘RESIDUAL_METERED_EDC’]
markets	None
name	PJM
price_node_ids	[‘5021703’, ‘5021704’, ‘5021723’, ‘5021724’, ‘93354015’, ‘93354017’, ‘93354019’, ‘34887765’, ‘34887767’, ‘34887769’, ‘34887771’, ‘34887773’, ‘34887775’, ‘34887777’, ‘2156111970’, ‘34887779’, ‘34887781’, ‘34887783’, ‘34887787’, ‘34887789’, ‘34887791’, ‘34887793’, ‘74008711’, ‘34887819’, ‘34887821’, ‘34887823’, ‘2156112027’, ‘34887845’, ‘1439658151’, ‘34887847’, ‘34887849’, ‘74008743’, ‘34887851’, ‘34887853’, ‘1123180720’, ‘34887857’, ‘1123180722’, ‘34887859’, ‘1123180723’, ‘34887861’, ‘1123180721’, ‘34887871’, ‘34887873’, ‘34887887’, ‘34887889’, ‘34887891’, ‘34887893’, ‘34887895’, ‘1207075032’, ‘34887897’, ‘34887899’, ‘34887901’, ‘34887911’, ‘34887913’, ‘34887915’, ‘34887917’, ‘34887923’, ‘1097732340’, ‘34887925’, ‘34887927’, ‘34887929’, ‘34887935’, ‘34887937’, ‘34887939’, ‘34887941’, ‘34887949’, ‘34887951’, ‘34887953’, ‘34887955’, ‘1552845076’, ‘34887957’, ‘1552845077’, ‘34887959’, ‘1552845078’, ‘34887961’, ‘34887963’, ‘34887965’, ‘34887967’, ‘34887969’, ‘34887971’, ‘1305131304’, ‘34887977’, ‘1305131306’, ‘34887993’, ‘34887997’, ‘34887999’, ‘34888001’, ‘119118151’, ‘2156114262’, ‘1379266905’, ‘1379266906’, ‘1097732449’, ‘1292915048’, ‘1132294512’, ‘1132294513’, ‘1132294514’, ‘1132294515’, ‘1552845186’, ‘106856851’, ‘2156112284’, ‘1305131444’, ‘119118263’, ‘119118265’, ‘119118267’, ‘119118269’, ‘119118271’, ‘106856905’, ‘2156110343’, ‘40243747’, ‘71856675’, ‘40243749’, ‘71856677’, ‘40243751’, ‘40243753’, ‘40243755’, ‘40243757’, ‘40243759’, ‘40243761’, ‘40243763’, ‘40243765’, ‘40243767’, ‘40243769’, ‘40243771’, ‘40243773’, ‘40243775’, ‘40243777’, ‘40243779’, ‘1248991825’, ‘1248991826’, ‘1248991827’, ‘40243801’, ‘40243803’, ‘40243805’, ‘40243807’, ‘135389793’, ‘135389819’, ‘40243837’, ‘1666116222’, ‘1666116223’, ‘1666116224’, ‘1666116225’, ‘40243839’, ‘1356163765’, ‘38367965’, ‘38367967’, ‘38367969’, ‘1218915048’, ‘1218915049’, ‘1218915050’, ‘1218915051’, ‘1388614399’, ‘2156110624’, ‘32418611’, ‘32418613’, ‘32418615’, ‘32418617’, ‘1388614460’, ‘1084390238’, ‘1218915186’, ‘1218915187’, ‘1369011076’, ‘1369011077’, ‘1369011078’, ‘1268571042’, ‘98370477’, ‘1084390354’, ‘93140’, ‘93141’, ‘93142’, ‘93143’, ‘93144’, ‘93145’, ‘98370523’, ‘98370525’, ‘98370527’, ‘98370529’, ‘98370531’, ‘98370533’, ‘98370535’, ‘1552843818’, ‘57967665’, ‘1552843913’, ‘1552843915’, ‘1552843916’, ‘1356162213’, ‘1356162214’, ‘50401’, ‘48934161’, ‘48934163’, ‘48934165’, ‘48934167’, ‘48934169’, ‘36181299’, ‘50488’, ‘50489’, ‘50490’, ‘36181325’, ‘2156113262’, ‘50542’, ‘50543’, ‘50557’, ‘50558’, ‘2156113284’, ‘50578’, ‘50579’, ‘50581’, ‘50621’, ‘50622’, ‘87901631’, ‘50628’, ‘50629’, ‘50654’, ‘50655’, ‘50659’, ‘50660’, ‘50661’, ‘50662’, ‘2156111333’, ‘1048047’, ‘1048049’, ‘1048050’, ‘1048051’, ‘1048052’, ‘21601782’, ‘21601783’, ‘21601784’, ‘21601785’, ‘21601786’, ‘50695’, ‘50696’, ‘50697’, ‘50698’, ‘50699’, ‘2041990671’, ‘123901459’, ‘123901461’, ‘123901463’, ‘123901465’, ‘123901467’, ‘50715’, ‘50716’, ‘50717’, ‘50727’, ‘50728’, ‘50729’, ‘50730’, ‘2156113457’, ‘2156113469’, ‘50764’, ‘2156113488’, ‘50769’, ‘50770’, ‘50771’, ‘50777’, ‘50778’, ‘50779’, ‘123901537’, ‘123901539’, ‘123901543’, ‘31020649’, ‘123901545’, ‘31020651’, ‘31020653’, ‘50809’, ‘50810’, ‘50811’, ‘50812’, ‘50813’, ‘50814’, ‘50817’, ‘50818’, ‘1165479564’, ‘2156109456’, ‘50887’, ‘50888’, ‘50893’, ‘50894’, ‘50911’, ‘50915’, ‘32417525’, ‘32417527’, ‘2156111608’, ‘1218914041’, ‘1218914042’, ‘1218914043’, ‘32417545’, ‘32417547’, ‘1183231801’, ‘32417599’, ‘32417601’, ‘32417603’, ‘32417605’, ‘51019’, ‘51020’, ‘51021’, ‘1348263767’, ‘32417625’, ‘32417627’, ‘32417629’, ‘32417631’, ‘32417633’, ‘32417635’, ‘1379268471’, ‘1379268472’, ‘1379268473’, ‘1379268474’, ‘1379268475’, ‘1379268476’, ‘63381383’, ‘63381385’, ‘2156111770’, ‘2156109760’, ‘2156109763’, ‘2156109765’, ‘2156109768’, ‘2156109772’, ‘2156109777’, ‘5021665’, ‘5021666’, ‘5021667’, ‘2156111847’, ‘93353961’, ‘93353963’, ‘93353965’]
zone_node_ids	[‘1’, ‘3’, ‘51291’, ‘51292’, ‘51293’, ‘51295’, ‘51296’, ‘51297’, ‘51298’, ‘51299’, ‘51300’, ‘51301’, ‘7633629’, ‘8394954’, ‘8445784’, ‘33092371’, ‘34508503’, ‘34964545’, ‘37737283’, ‘116013753’, ‘124076095’, ‘970242670’, ‘1709725933’]

Methods

`get_fuel_mix`	Get fuel mix for a date or date range in hourly intervals
`get_interconnection_queue`
`get_lmp`	Returns LMP at a previous date
`get_load`	Returns load at a previous date at 5 minute intervals
`get_load_forecast`	Get forecast for today in hourly intervals.
`get_pnode_ids`
`get_raw_interconnection_queue`

get_fuel_mix(date, end=None, verbose=False)[source]#: Get fuel mix for a date or date range in hourly intervals

get_interconnection_queue(verbose=False)[source]#

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

Returns LMP at a previous date

Notes

If start date is prior to the PJM archive date, all data

must be downloaded before location filtering can be performed due to limitations of PJM API. The archive date is 186 days (~6 months) before today for the 5 minute real time market and 731 days (~2 years) before today for the Hourly Real Time and Day Ahead Hourly markets. Node type filter can be performed for Real Time Hourly and Day Ahead Hourly markets.

If location_type is provided, it is filtered after data

is retrieved for Real Time 5 Minute market regardless of the date. This is due to PJM api limitations

Return Location Id, Location Name, Location Short Name.

Parameters:

date (datetime.date, str) – date to get LMPs for
end (datetime.date, str) – end date to get LMPs for
market (str) – Supported Markets: REAL_TIME_5_MIN, REAL_TIME_HOURLY, DAY_AHEAD_HOURLY
locations (list, optional) – list of pnodeid to get LMPs for. Defaults to “hubs”. Use get_pnode_ids() to get a list of possible pnode ids. If “all”, will return data from all p nodes (warning there are over 10,000 unique pnodes, so expect millions or billions of rows!)
location_type (str, optional) – If specified, will only return data for nodes of this type. Defaults to None. Possible location types are: ‘ZONE’, ‘LOAD’, ‘GEN’, ‘AGGREGATE’, ‘INTERFACE’, ‘EXT’, ‘HUB’, ‘EHV’, ‘TIE’, ‘RESIDUAL_METERED_EDC’.

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

Returns load at a previous date at 5 minute intervals

Parameters:

date (datetime.date, str) – date to get load for. must be in last 30 days

Returns:

Load data time series. Columns: Time, Load, and all areas

Load columns represent PJM-wide load
Returns data for the following areas: AE, AEP, APS, ATSI,

BC, COMED, DAYTON, DEOK, DOM, DPL, DUQ, EKPC, JC, ME, PE, PEP, PJM MID ATLANTIC REGION, PJM RTO, PJM SOUTHERN REGION, PJM WESTERN REGION, PL, PN, PS, RECO

Return type:

pd.DataFrame

get_load_forecast(date, verbose=False)[source]#

Get forecast for today in hourly intervals.

Updates every Every half hour on the quarter E.g. 1:15 and 1:45

get_pnode_ids()[source]#

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

class gridstatus.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_capacity_of_generation_on_outage`	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`	Provides Marginal Clearing Price information by Reserve Zone for each
`get_fuel_mix`	Get fuel mix
`get_interconnection_queue`	Get interconnection queue
`get_lmp`	Get LMP data
`get_lmp_real_time_weis`	Get LMP data for real time WEIS
`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_load_forecast_mid_term`	Returns load forecast for +7 days in hourly intervals. Includes actual load
`get_load_forecast_short_term`	5-minute load forecast data for the SPP footprint (system-wide) for +/- 10
`get_operating_reserves`
`get_raw_interconnection_queue`
`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`	Get VER Curtailments
`get_ver_curtailments_annual`	Get VER Curtailments for a year. Starting 2014.
`now`

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

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, verbose=True)[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

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

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, 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, location_type: str = LOCATION_TYPE_ALL, verbose=False)[source]#

Get LMP data

Supported Markets:

REAL_TIME_5_MIN
DAY_AHEAD_HOURLY

Supported Location Types:

Hub
Interface
ALL

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

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

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

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

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

Returns:

forecast as dataframe.

Return type:

pd.DataFrame

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

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

get_solar_and_wind_forecast_mid_term(date, end=None, verbose=False)[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

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

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(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

static now()[source]#

Gridstatus

Contents

Gridstatus#

Subpackages#

Submodules#

Package Contents#

Classes Summary#

Exceptions Summary#

Functions#

Contents#