Gridstatus

Contents

Gridstatus#

Subpackages#

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_fuel_regions

Retrieves the (mostly static) list of fuel regions with associated data.

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_solar_and_wind_forecast_dam

Return wind and solar forecast in hourly intervals

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

get_tie_flows_real_time

Return real time tie flow data.

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_fuel_regions(verbose=False)[source]#

Retrieves the (mostly static) list of fuel regions with associated data. This file can be joined to the gas prices on Fuel Region Id

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

Return wind and solar forecast in hourly intervals

Data at: http://oasis.caiso.com/mrioasis/logon.do at System Demand > Wind and Solar Forecast

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

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

Return real time tie flow data.

From OASIS: Energy > Energy Imbalance Market > EIM Transfer by Tie

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

  • end (datetime.date, str) – last date of range to return data.

Returns:

pandas.DataFrame

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. Returns a dict with keys

get_60_day_sced_disclosure

Get 60 day SCED Disclosure data

get_as_monitor

Get Ancillary Service Capacity Monitor.

get_as_plan

Ancillary Service requirements by type and quantity for each hour of the

get_as_prices

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

get_as_reports

Get Ancillary Services Reports.

get_available_seasonal_capacity_forecast

Retrieves the forecasted demand (Load Forecast) and the forecasted available

get_capacity_committed

Retrieves the actual committed capacity (the amount of power available from

get_capacity_forecast

Retrieves the forecasted committed capacity (Committed Capacity) and the

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_adders_and_reserves

Get Real-Time ORDC and Reliability Deployment Price Adders and

get_real_time_system_conditions

Get Real-Time System Conditions.

get_reported_outages

Retrieves the 5-minute data behind this dashboard:

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_short_term_system_adequacy

Get Short Term System Adequacy published between date and end.

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_temperature_forecast_by_weather_zone

Get temperature forecast by weather zone in hourly intervals. Published

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. Returns a dict with keys

  • “dam_gen_resource”

  • “dam_gen_resource_as_offers”

  • “dam_load_resource”

  • “dam_load_resource_as_offers”

  • “dam_energy_bids”

  • “dam_energy_bid_awards”

and values as pandas.DataFrame objects

The date passed in should be the report date. Since reports are delayed by 60 days, the passed date should not be fewer than 60 days in the past.

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

Ancillary Service requirements by type and quantity for each hour of the current day plus the next 6 days

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 ECRS, NSPIN, REGDN, REGUP, RRS

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

Retrieves the forecasted demand (Load Forecast) and the forecasted available seasonal capacity (Available Capacity) for the next 6 days.

Data is ephemeral and does not support past days.

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

Retrieves the actual committed capacity (the amount of power available from generating units that were on-line or providing operating reserves).

Data is ephemeral and does not support past days.

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

Retrieves the forecasted committed capacity (Committed Capacity) and the forecasted available capacity (Available Capacity) for the current day.

Data is ephemeral and does not support past days.

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, location_type: str = SETTLEMENT_POINT_LOCATION_TYPE, 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. Defaults to “settlement point”

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_adders_and_reserves(date, end=None, verbose=False)[source]#
Get Real-Time ORDC and Reliability Deployment Price Adders and

Reserves by SCED Interval

At: https://www.ercot.com/mp/data-products/data-product-details?id=NP6-323-CD

Parameters:
  • date (str, datetime) – date to get data for

  • end (str, datetime) – end date to get data for

  • verbose (bool, optional) – print verbose output. Defaults to False.

Returns:

A DataFrame with ORDC data

Return type:

pandas.DataFrame

NOTE: data only goes back 5 days

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

Retrieves the 5-minute data behind this dashboard: https://www.ercot.com/gridmktinfo/dashboards/generationoutages

Data available at https://www.ercot.com/api/1/services/read/dashboards/generation-outages.json

This data is ephemeral in that there is only one file available that is constantly updated. There is no historical data.

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

Get Short Term System Adequacy published between date and end.

Parameters:
  • date (str, datetime) – date to get data for

  • verbose (bool, optional) – print verbose output. Defaults to False.

Returns:

A DataFrame with system adequacy data

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

Get temperature forecast by weather zone in hourly intervals. Published once a day at 5 am central.

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 temperature forecast data

Return type:

pandas.DataFrame

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

Get Unplanned Resource Outages.

Data published at ~5am central on the 3rd day after the day of interest. Since the date argument is the publish date, if you want to get data for a specific date, pass in the date of interest - 3 days.

Parameters:
  • date (str, datetime) – publish date of the report

  • end (str, datetime, optional) – end date to download. Defaults to None.

  • 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, nonexistent='raise')[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

lmp_real_time_intervals

[‘00-04’, ‘04-08’, ‘08-12’, ‘12-16’, ‘16-20’, ‘20-24’]

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_5_MIN_WEEKLY

REAL_TIME_5_MIN_WEEKLY

REAL_TIME_HOURLY

REAL_TIME_HOURLY

REAL_TIME_HOURLY_FINAL

REAL_TIME_HOURLY_FINAL

REAL_TIME_HOURLY_PRELIM

REAL_TIME_HOURLY_PRELIM

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

EST

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

solar_and_wind_forecast_cols

None

solar_and_wind_forecast_region_cols

[‘North’, ‘Central’, ‘South’, ‘MISO’]

Methods

get_fuel_mix

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

get_generation_outages_estimated

Get the estimated generation outages published on the date for the past 30

get_generation_outages_forecast

Get the forecasted generation outages published on the date for the next

get_interconnection_queue

Get the interconnection queue

get_lmp

Supported Markets:

get_lmp_weekly

Retrieves weekly lmp data that includes price corrections to the real time

get_load

get_load_forecast

https://docs.misoenergy.org/marketreports/YYYYMMDD_df_al.xls

get_raw_interconnection_queue

get_solar_forecast

get_wind_forecast

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_generation_outages_estimated(date: str | pandas.Timestamp, end: str | pandas.Timestamp = None, verbose: bool = False)[source]#

Get the estimated generation outages published on the date for the past 30 days. NOTE: since these are estimates, they change with each file published.

get_generation_outages_forecast(date: str | pandas.Timestamp, end: str | pandas.Timestamp = None, verbose: bool = False)[source]#

Get the forecasted generation outages published on the date for the next seven days.

get_interconnection_queue(verbose=False)[source]#

Get the interconnection queue

Returns:

Interconnection queue

Return type:

pandas.DataFrame

get_lmp(date, end=None, market: str = Markets.REAL_TIME_5_MIN, locations: list = 'ALL', verbose=False)[source]#
Supported Markets:
  • REAL_TIME_5_MIN - (FiveMinLMP)

  • DAY_AHEAD_HOURLY - (DayAheadExPostLMP)

  • REAL_TIME_HOURLY_FINAL - (RealTimeFinalLMP)

  • REAL_TIME_HOURLY_PRELIM - (RealTimePrelimLMP) Only 4 days of data

    available, with the most recent being yesterday.

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

Retrieves weekly lmp data that includes price corrections to the real time data.

Data from: https://www.misoenergy.org/markets-and-operations/real-time–market-data/market-reports/#nt=%2FMarketReportType%3AHistorical%20LMP%2FMarketReportName%3AWeekly%20Real-Time%205-Min%20LMP%20(zip)&t=10&p=0&s=MarketReportPublished&sd=desc

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

https://docs.misoenergy.org/marketreports/YYYYMMDD_df_al.xls

get_raw_interconnection_queue(verbose=False) BinaryIO[source]#
get_solar_forecast(date, verbose=False)[source]#
get_wind_forecast(date, verbose=False)[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(api_key=None, retries=DEFAULT_RETRIES)[source]#

Bases: gridstatus.base.ISOBase

PJM

Parameters:

api_key (str, optional) – PJM API key. Alternatively, can be set in PJM_API_KEY environment variable. Register for an API key at https://www.pjm.com/

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

load_forecast_endpoint_name

load_frcstd_7_day

load_forecast_historical_endpoint_name

load_frcstd_hist

locale_abbreviated_to_full

None

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’]

service_type_abbreviated_to_full

None

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_dam_as_market_results

Retrieves the day-ahead ancillary service market results from :

get_forecasted_generation_outages

Retrieves the forecasted generation outages for the next 90 days from:

get_fuel_mix

Get fuel mix for a date or date range in hourly intervals

get_gen_outages_by_type

Retrieves the generation outage data

get_interconnection_queue

get_it_sced_lmp_5_min

Get 5 minute LMPs from the Integrated Forward Market (IFM)

get_lmp

Returns LMP at a previous date

get_load

Returns load at a previous date at 5 minute intervals

get_load_forecast

Load forecast made today extending for six days in hourly intervals.

get_load_forecast_historical

Historical load forecast in hourly intervals. Historical forecasts include all

get_load_metered_hourly

Retrieves the hourly metered load data from:

get_operational_reserves

Retrieves the reserve market quantities in Megawatts from:

get_pnode_ids

get_projected_area_statistics_at_peak

Area projected data for the peak of the day

get_projected_rto_statistics_at_peak

RTO-wide projected data for the peak of the day

get_raw_interconnection_queue

get_real_time_as_market_results

Retrieves the real-time ancillary service market results from :

get_solar_forecast_5_min

Retrieves the 5-min solar forecast including behind the meter solar forecast.

get_solar_forecast_hourly

Retrieves the hourly solar forecast including behind the meter solar forecast.

get_solar_generation_5_min

Retrieves the 5 min solar generation data from:

get_solar_generation_by_area

Retrieves the current solar generation information from:

get_transfer_interface_information_5_min

Retrieves the transfer interface information from:

get_transmission_limits

Retrieves the current transmission limit information from:

get_wind_forecast_5_min

Retrieves the 5-min wind forecast

get_wind_forecast_hourly

Retrieves the hourly wind forecast

get_wind_generation_by_area

Retrieves the current wind generation information from:

get_wind_generation_instantaneous

Retrieves the instantaneous wind generation data from:

to_local_datetime

get_dam_as_market_results(date: str | pandas.Timestamp, end: str | pandas.Timestamp | None = None, verbose: bool | None = False)[source]#

Retrieves the day-ahead ancillary service market results from : https://dataminer2.pjm.com/feed/da_reserve_market_results/definition Data is published daily.

Parameters:
  • date (str or pandas.Timestamp) – Start datetime for data

  • end – (str or pandas.Timestamp, optional): End datetime for data. Defaults to one day past date if not specified.

  • verbose (bool, optional) – print verbose output. Defaults to False.

Returns:

A DataFrame with day-ahead ancillary service market results.

Return type:

pandas.DataFrame

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

Retrieves the forecasted generation outages for the next 90 days from:

https://dataminer2.pjm.com/feed/frcstd_gen_outages/definition

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

Get fuel mix for a date or date range in hourly intervals

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

Retrieves the generation outage data From: https://dataminer2.pjm.com/feed/gen_outages_by_type/definition

get_interconnection_queue(verbose=False)[source]#
get_it_sced_lmp_5_min(date, end=None, verbose=False)[source]#

Get 5 minute LMPs from the Integrated Forward Market (IFM)

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, end=None, verbose=False)[source]#

Load forecast made today extending for six days in hourly intervals.

Today’s forecast updates every every half hour on the quarter E.g. 1:15 and 1:45

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

Historical load forecast in hourly intervals. Historical forecasts include all vintages of the forecast but has fewer regions than the current forecast.

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

Retrieves the hourly metered load data from:

https://dataminer2.pjm.com/feed/hrl_load_metered/definition

get_operational_reserves(date: str | pandas.Timestamp, end: str | pandas.Timestamp | None = None, verbose: bool | None = False)[source]#

Retrieves the reserve market quantities in Megawatts from: https://dataminer2.pjm.com/feed/operational_reserves/definition Only available in past 15 days.

Parameters:
  • date (str or pandas.Timestamp) – Start datetime for data

  • end – (str or pandas.Timestamp, optional): End datetime for data. Defaults to one day past date if not specified.

  • verbose (bool, optional) – print verbose output. Defaults to False.

Returns:

A DataFrame with reserve market quantities

in 15 second intervals.

Return type:

pandas.DataFrame

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

Area projected data for the peak of the day

https://dataminer2.pjm.com/feed/ops_sum_frcst_peak_area/definition

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

RTO-wide projected data for the peak of the day

https://dataminer2.pjm.com/feed/ops_sum_frcst_peak_rto/definition

get_raw_interconnection_queue(verbose=False) BinaryIO[source]#
get_real_time_as_market_results(date: str | pandas.Timestamp, end: str | pandas.Timestamp | None = None, verbose: bool | None = False)[source]#

Retrieves the real-time ancillary service market results from : https://dataminer2.pjm.com/feed/reserve_market_results/definition Data for the previous day is published daily on business days, typically between 11am and 12pm market time.

Data granularity changed on Sep 1, 2022 so when querying data, start and end dates must both be before or both after that date.

Parameters:
  • date (str or pandas.Timestamp) – Start datetime for data

  • end – (str or pandas.Timestamp, optional): End datetime for data. Defaults to one day past date if not specified.

  • verbose (bool, optional) – print verbose output. Defaults to False.

Returns:

A DataFrame with real-time ancillary service market results.

Return type:

pandas.DataFrame

get_solar_forecast_5_min(date: str | pandas.Timestamp, end: str | pandas.Timestamp | None = None, verbose: bool = False) pandas.DataFrame[source]#

Retrieves the 5-min solar forecast including behind the meter solar forecast. From: https://dataminer2.pjm.com/feed/five_min_solar_power_forecast/definition Only available in past 30 days

Parameters:
  • date (str | pd.Timestamp) – Start datetime for data

  • end (str | pd.Timestamp | None, optional) – End datetime for data. Defaults to None.

  • verbose (bool, optional) – print verbose output. Defaults to False.

Returns:

A DataFrame with the solar forecast data.

Return type:

pd.DataFrame

get_solar_forecast_hourly(date: str | pandas.Timestamp, end: str | pandas.Timestamp | None = None, verbose: bool = False) pandas.DataFrame[source]#

Retrieves the hourly solar forecast including behind the meter solar forecast. From: https://dataminer2.pjm.com/feed/hourly_solar_power_forecast/definition Only available in past 30 days

Parameters:
  • date (str | pd.Timestamp) – Start datetime for data

  • end (str | pd.Timestamp | None, optional) – End datetime for data. Defaults to None.

  • verbose (bool, optional) – print verbose output. Defaults to False.

Returns:

A DataFrame with the solar forecast data.

Return type:

pd.DataFrame

get_solar_generation_5_min(date: str | pandas.Timestamp, end: str | pandas.Timestamp | None = None, verbose: bool | None = False)[source]#

Retrieves the 5 min solar generation data from: https://dataminer2.pjm.com/feed/five_min_solar_generation/definition Only available in past 30 days.

Parameters:
  • date (str or pandas.Timestamp) – Start datetime for data

  • end – (str or pandas.Timestamp, optional): End datetime for data. Defaults to one day past date if not specified.

  • verbose (bool, optional) – print verbose output. Defaults to False.

Returns:

A DataFrame with 5 minute solar generation data.

Return type:

pandas.DataFrame

get_solar_generation_by_area(date: str | pandas.Timestamp, end: str | pandas.Timestamp | None = None, verbose: bool | None = False)[source]#

Retrieves the current solar generation information from: https://dataminer2.pjm.com/feed/solar_gen/definition Data is published daily around 7am market time.

Parameters:
  • date (str or pandas.Timestamp) – Start datetime for data

  • end – (str or pandas.Timestamp, optional): End datetime for data. Defaults to one day past date if not specified.

  • verbose (bool, optional) – print verbose output. Defaults to False.

Returns:

A DataFrame with solar generation information.

Return type:

pandas.DataFrame

get_transfer_interface_information_5_min(date: str | pandas.Timestamp, end: str | pandas.Timestamp | None = None, verbose: bool | None = False)[source]#

Retrieves the transfer interface information from: https://dataminer2.pjm.com/feed/transfer_interface_infor/definition Only available in past 30 days.

Parameters:
  • date (str or pandas.Timestamp) – Start datetime for data

  • end – (str or pandas.Timestamp, optional): End datetime for data. Defaults to one day past date if not specified.

  • verbose (bool, optional) – print verbose output. Defaults to False.

Returns:

A DataFrame with transfer interface information in 5 minute intervals.

Return type:

pandas.DataFrame

get_transmission_limits(date: str | pandas.Timestamp, end: str | pandas.Timestamp | None = None, verbose: bool | None = False)[source]#

Retrieves the current transmission limit information from: https://dataminer2.pjm.com/feed/transfer_interface_infor/definition Only available in past 30 days. Data is published only when constraints exist for that five minute interval.

Parameters:
  • date (str or pandas.Timestamp) – Start datetime for data

  • end – (str or pandas.Timestamp, optional): End datetime for data. Defaults to one day past date if not specified.

  • verbose (bool, optional) – print verbose output. Defaults to False.

Returns:

A DataFrame with transmission limit information in 5 minute intervals, when data is available.

Return type:

pandas.DataFrame

get_wind_forecast_5_min(date: str | pandas.Timestamp, end: str | pandas.Timestamp | None = None, verbose: bool = False) pandas.DataFrame[source]#

Retrieves the 5-min wind forecast From: https://dataminer2.pjm.com/feed/five_min_wind_power_forecast/definition Only available in past 30 days

Parameters:
  • date (str | pd.Timestamp) – Start datetime for data

  • end (Optional[str | pd.Timestamp], optional) – End datetime for data. Defaults to None.

  • verbose (bool, optional) – print verbose output. Defaults to False.

Returns:

A DataFrame with the wind forecast data.

Return type:

pd.DataFrame

get_wind_forecast_hourly(date: str | pandas.Timestamp, end: str | pandas.Timestamp | None = None, verbose: bool = False) pandas.DataFrame[source]#

Retrieves the hourly wind forecast From: https://dataminer2.pjm.com/feed/hourly_wind_power_forecast/definition Only available in past 30 days

Parameters:
  • date (str | pd.Timestamp) – Start datetime for data

  • end (Optional[str | pd.Timestamp], optional) – End datetime for data. Defaults to None.

  • verbose (bool, optional) – print verbose output. Defaults to False.

Returns:

A DataFrame with the wind forecast data.

Return type:

pd.DataFrame

get_wind_generation_by_area(date: str | pandas.Timestamp, end: str | pandas.Timestamp | None = None, verbose: bool | None = False)[source]#

Retrieves the current wind generation information from: https://dataminer2.pjm.com/feed/wind_gen/definition Data is published daily around 7am market time.

Parameters:
  • date (str or pandas.Timestamp) – Start datetime for data

  • end – (str or pandas.Timestamp, optional): End datetime for data. Defaults to one day past date if not specified.

  • verbose (bool, optional) – print verbose output. Defaults to False.

Returns:

A DataFrame with wind generation information.

Return type:

pandas.DataFrame

get_wind_generation_instantaneous(date: str | pandas.Timestamp, end: str | pandas.Timestamp | None = None, verbose: bool | None = False)[source]#

Retrieves the instantaneous wind generation data from: https://dataminer2.pjm.com/feed/instantaneous_wind_gen/definition Only available in past 30 days.

Parameters:
  • date (str or pandas.Timestamp) – Start datetime for data

  • end – (str or pandas.Timestamp, optional): End datetime for data. Defaults to one day past date if not specified.

  • verbose (bool, optional) – print verbose output. Defaults to False.

Returns:

A DataFrame with instantaneous wind generation data

in 15 second intervals.

Return type:

pandas.DataFrame

to_local_datetime(df, column_name)[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_hourly_load

Get Hourly Load

get_hourly_load_annual

Get Hourly Load for a year. Starting 2011.

get_interconnection_queue

Get interconnection queue

get_lmp_day_ahead_hourly

Get day ahead hourly LMP data

get_lmp_real_time_5_min_by_bus

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

get_lmp_real_time_5_min_by_location

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

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

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, verbose=True)[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=False)[source]#

Get interconnection queue

Returns:

Interconnection queue

Return type:

pandas.DataFrame

get_lmp_day_ahead_hourly(date, end=None, location_type: str = LOCATION_TYPE_ALL, verbose=False)[source]#

Get day ahead hourly LMP data

Supported Location Types:
  • Hub

  • Interface

  • ALL

get_lmp_real_time_5_min_by_bus(date, end=None, verbose=False)[source]#

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, end=None, location_type=LOCATION_TYPE_ALL, verbose=False)[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

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]#