Ercot

Ercot#

Module Contents#

Classes Summary#

`Document`
`Ercot`	Electric Reliability Council of Texas (ERCOT)
`ERCOTSevenDayLoadForecastReport`	Enum class for the Medium Term (Seven Day) Load Forecasts.

Functions#

parse_timestamp_from_friendly_name(→ pandas.Timestamp)

Attributes Summary#

`ACTUAL_SYSTEM_LOAD_BY_FORECAST_ZONE`
`ACTUAL_SYSTEM_LOAD_BY_WEATHER_ZONE`
`AS_EXCLUDE_PRODUCTS`
`AS_PRODUCTS`
`COP_ADJUSTMENT_PERIOD_SNAPSHOT_RTID`
`DAILY_RUC_AS_DEMAND_CURVES_RTID`
`DAILY_RUC_AS_DEPLOYMENT_FACTORS_RTID`
`DAM_AGGREGATED_AS_OFFER_CURVE_RTID`
`DAM_ANCILLARY_SERVICE_PLAN_RTID`
`DAM_AND_SCED_ANCILLARY_SERVICE_DEMAND_CURVES_RTID`
`DAM_CLEARING_PRICES_FOR_CAPACITY_RTID`
`DAM_LMPS_BY_ELECTRICAL_BUS_RTID`
`DAM_PRICE_CORRECTIONS_RTID`
`DAM_SETTLEMENT_POINT_PRICES_RTID`
`DAM_SHADOW_PRICES_RTID`
`DAM_SYSTEM_LAMBDA_RTID`
`DAM_TOTAL_AS_SOLD_RTID`
`DAM_TOTAL_ENERGY_PURCHASED_RTID`
`DAM_TOTAL_ENERGY_SOLD_RTID`
`ELECTRICAL_BUS_LOCATION_TYPE`
`ERCOT_INDICATIVE_LMP_BY_SETTLEMENT_POINT_RTID`
`GIS_REPORT_RTID`
`HISTORICAL_DAM_LOAD_ZONE_AND_HUB_PRICES_RTID`
`HISTORICAL_RTM_LOAD_ZONE_AND_HUB_PRICES_RTID`
`HOURLY_RESOURCE_OUTAGE_CAPACITY_RTID`
`HOURLY_RUC_AS_DEMAND_CURVES_RTID`
`HOURLY_RUC_AS_DEPLOYMENT_FACTORS_RTID`
`HUB_SETTLEMENT_TYPES`
`LMPS_BY_ELECTRICAL_BUS_RTID`
`LMPS_BY_SETTLEMENT_POINT_RTID`
`LOAD_ZONE_SETTLEMENT_TYPES`
`LOCATION_TYPE_HUB`
`LOCATION_TYPE_RESOURCE_NODE`
`LOCATION_TYPE_ZONE`
`LOCATION_TYPE_ZONE_DC`
`LOCATION_TYPE_ZONE_DC_EW`
`LOCATION_TYPE_ZONE_EW`
`PROJECTED_ANCILLARY_SERVICE_DEPLOYMENTS_FACTORS_RTID`
`REAL_TIME_ADDERS_AND_RESERVES_RTID`
`REAL_TIME_ADDERS_RTID`
`REAL_TIME_CLEARING_LMPS_BY_RESOURCE_NODES_LOAD_ZONES_AND_TRADING_HUBS_RTD`
`REAL_TIME_CLEARING_PRICES_FOR_CAPACITY_15_MIN_RTID`
`REAL_TIME_CLEARING_PRICES_FOR_CAPACITY_BY_SCED_INTERVAL_RTID`
`RESOURCE_NODE_SETTLEMENT_TYPES`
`RTD_INDICATIVE_REAL_TIME_MCPC_RTID`
`RTM_PRICE_CORRECTIONS_RTID`
`SCED_SYSTEM_LAMBDA_RTID`
`SETTLEMENT_POINT_LOCATION_TYPE`	Report Type IDs
`SETTLEMENT_POINT_PRICES_AT_RESOURCE_NODES_HUBS_AND_LOAD_ZONES_RTID`
`SETTLEMENT_POINTS_LIST_AND_ELECTRICAL_BUSES_MAPPING_RTID`
`SHORT_TERM_SYSTEM_ADEQUACY_REPORT_RTID`
`SIXTY_DAY_DAM_DISCLOSURE_REPORTS_RTID`
`SIXTY_DAY_SCED_DISCLOSURE_REPORTS_RTID`
`SOLAR_POWER_PRODUCTION_HOURLY_AVERAGED_ACTUAL_AND_FORECASTED_VALUES_BY_GEOGRAPHICAL_REGION_RTID`
`SOLAR_POWER_PRODUCTION_HOURLY_AVERAGED_ACTUAL_AND_FORECASTED_VALUES_RTID`	Settlement Point Type Description
`SYSTEM_WIDE_ACTUALS_RTID`
`TEMPERATURE_FORECAST_BY_WEATHER_ZONE_RTID`
`THREE_DAY_DAM_HIGHEST_PRICE_AS_OFFER_SELECTED_RTID`
`THREE_DAY_HIGHEST_PRICE_BIDS_SCED_RTID`
`THREE_DAY_SCED_HIGHEST_PRICE_AS_OFFER_SELECTED_RTID`
`TOTAL_CAPABILITY_OF_RESOURCES_AS_RTID`
`TWO_DAY_ANCILLARY_SERVICES_REPORTS_RTID`
`TWO_DAY_SCED_ANCILLARY_SERVICES_REPORTS_RTID`
`UNPLANNED_RESOURCE_OUTAGES_REPORT_RTID`
`WEEKLY_RUC_AS_DEMAND_CURVES_RTID`
`WEEKLY_RUC_AS_DEPLOYMENT_FACTORS_RTID`
`WIND_POWER_PRODUCTION_HOURLY_AVERAGED_ACTUAL_AND_FORECASTED_VALUES_BY_GEOGRAPHICAL_REGION_RTID`
`WIND_POWER_PRODUCTION_HOURLY_AVERAGED_ACTUAL_AND_FORECASTED_VALUES_RTID`

Contents#

gridstatus.ercot.ACTUAL_SYSTEM_LOAD_BY_FORECAST_ZONE = 14836[source]#

gridstatus.ercot.ACTUAL_SYSTEM_LOAD_BY_WEATHER_ZONE = 13101[source]#

gridstatus.ercot.AS_EXCLUDE_PRODUCTS = ['ECRSM', 'ECRSS', 'NSPNM', 'RRSFFR', 'RRSUFR', 'RRSPFR'][source]#

gridstatus.ercot.AS_PRODUCTS = ['RRSPFR', 'RRSUFR', 'RRSFFR', 'ECRSM', 'ECRSS', 'REGUP', 'REGDN', 'NSPIN', 'NSPNM'][source]#

gridstatus.ercot.COP_ADJUSTMENT_PERIOD_SNAPSHOT_RTID = 10038[source]#

gridstatus.ercot.DAILY_RUC_AS_DEMAND_CURVES_RTID = 26383[source]#

gridstatus.ercot.DAILY_RUC_AS_DEPLOYMENT_FACTORS_RTID = 24895[source]#

gridstatus.ercot.DAM_AGGREGATED_AS_OFFER_CURVE_RTID = 12330[source]#

gridstatus.ercot.DAM_ANCILLARY_SERVICE_PLAN_RTID = 12316[source]#

gridstatus.ercot.DAM_AND_SCED_ANCILLARY_SERVICE_DEMAND_CURVES_RTID = 24893[source]#

gridstatus.ercot.DAM_CLEARING_PRICES_FOR_CAPACITY_RTID = 12329[source]#

gridstatus.ercot.DAM_LMPS_BY_ELECTRICAL_BUS_RTID = 12328[source]#

gridstatus.ercot.DAM_PRICE_CORRECTIONS_RTID = 13044[source]#

gridstatus.ercot.DAM_SETTLEMENT_POINT_PRICES_RTID = 12331[source]#

gridstatus.ercot.DAM_SHADOW_PRICES_RTID = 12332[source]#

gridstatus.ercot.DAM_SYSTEM_LAMBDA_RTID = 13113[source]#

gridstatus.ercot.DAM_TOTAL_AS_SOLD_RTID = 24888[source]#

gridstatus.ercot.DAM_TOTAL_ENERGY_PURCHASED_RTID = 12333[source]#

gridstatus.ercot.DAM_TOTAL_ENERGY_SOLD_RTID = 12334[source]#

class gridstatus.ercot.Document[source]#

Attributes

constructed_name	None
friendly_name	None
friendly_name_timestamp	None
publish_date	None
url	None

gridstatus.ercot.ELECTRICAL_BUS_LOCATION_TYPE = 'Electrical Bus'[source]#

class gridstatus.ercot.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’
ESR_CORRECTION_DATES	None
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’
OPERATIONS_MESSAGES_URL	‘https://www.ercot.com/services/comm/mkt_notices/opsmessages’
SCED_SUPPLEMENTAL_CORRECTION_END	None
SCED_SUPPLEMENTAL_CORRECTION_START	None
status_homepage	‘https://www.ercot.com/gridmktinfo/dashboards/gridconditions’
WAYBACK_CDX_URL	‘https://web.archive.org/cdx/search/cdx’
WAYBACK_SNAP_URL	‘https://web.archive.org/web/{timestamp}/{url}’

Methods

`ambiguous_based_on_dstflag`(→ pandas.Series)
`get_3_day_highest_price_sced`(→ pandas.DataFrame)	Get the bid price and name of the Load Resource submitting the
`get_60_day_dam_disclosure`(→ dict)	Get 60 day DAM Disclosure data. Returns a dict with keys
`get_60_day_sced_disclosure`(→ dict)	Get 60 day SCED Disclosure data
`get_as_demand_curves_daily_ruc`(→ pandas.DataFrame)	Get Daily RUC Ancillary Service Demand Curves
`get_as_demand_curves_dam_and_sced`(→ pandas.DataFrame)	Get Ancillary Service Demand Curves
`get_as_demand_curves_hourly_ruc`(→ pandas.DataFrame)	Get Hourly RUC Ancillary Service Demand Curves
`get_as_demand_curves_weekly_ruc`(→ pandas.DataFrame)	Get Weekly RUC Ancillary Service Demand Curves
`get_as_deployment_factors_daily_ruc`(→ pandas.DataFrame)	Get Daily RUC Ancillary Service Deployment Factors
`get_as_deployment_factors_hourly_ruc`(→ pandas.DataFrame)	Get Hourly RUC Ancillary Service Deployment Factors
`get_as_deployment_factors_projected`(→ pandas.DataFrame)	Get Projected Ancillary Service Deployment Factors
`get_as_deployment_factors_weekly_ruc`(→ pandas.DataFrame)	Get Weekly RUC Ancillary Service Deployment Factors
`get_as_monitor`(→ pandas.DataFrame)	Get Ancillary Service Capacity Monitor.
`get_as_plan`(→ pandas.DataFrame)	Ancillary Service requirements by type and quantity for each hour of the
`get_as_prices`(→ pandas.DataFrame)	Get ancillary service clearing prices in hourly intervals in Day Ahead Market
`get_as_reports`(→ pandas.DataFrame)	Get Ancillary Services Reports.
`get_as_reports_dam`(→ pandas.DataFrame)	Get Day-Ahead Market Ancillary Services Reports.
`get_as_reports_sced`(→ pandas.DataFrame)	Get 2-Day SCED Ancillary Service Disclosure Reports.
`get_as_total_capability`(→ pandas.DataFrame)	Get Total Capability of Resources Available to Provide Ancillary Service
`get_available_seasonal_capacity_forecast`(...)	Retrieves the forecasted demand (Load Forecast) and the forecasted available
`get_capacity_committed`(→ pandas.DataFrame)	Retrieves the actual committed capacity (the amount of power available from
`get_capacity_forecast`(→ pandas.DataFrame)	Retrieves the forecasted committed capacity (Committed Capacity) and the
`get_ccp_resource_names`(→ pandas.DataFrame)
`get_cop_adjustment_period_snapshot_60_day`(...)
`get_dam_asdc_aggregated`(→ pandas.DataFrame)	Get DAM Aggregated Ancillary Service Offer Curve (NP4-19-CD).
`get_dam_price_corrections`(→ pandas.DataFrame)	Get DAM Price Corrections
`get_dam_spp`(→ pandas.DataFrame)	Get Historical DAM Settlement Point Prices(SPPs)
`get_dam_system_lambda`(→ pandas.DataFrame)	Get Day-Ahead Market System Lambda
`get_dam_total_as_sold`(→ pandas.DataFrame)	Get DAM Total Ancillary Services Sold
`get_dam_total_energy_purchased`(→ pandas.DataFrame)	Get DAM Total Energy Purchased
`get_dam_total_energy_sold`(→ pandas.DataFrame)	Get DAM Total Energy Sold
`get_energy_storage_resources`(→ pandas.DataFrame)	Get energy storage resources.
`get_fuel_mix`(→ pandas.DataFrame)	Get fuel mix 5 minute intervals
`get_fuel_mix_detailed`(→ pandas.DataFrame)	The fuel mix with gen, hsl, and seasonal capacity for each fuel type.
`get_highest_price_as_offer_selected`(→ pandas.DataFrame)	Get the offer price and the name of the Entity submitting
`get_highest_price_as_offer_selected_dam`(→ pandas.DataFrame)	Get the offer price and the name of the Entity submitting
`get_highest_price_as_offer_selected_sced`(...)	Get the offer price and the name of the Entity submitting
`get_hourly_load_post_settlements`(→ pandas.DataFrame)	Get historical hourly load data from ERCOT's load archives.
`get_hourly_resource_outage_capacity`(→ pandas.DataFrame)	Hourly Resource Outage Capacity report sourced
`get_hub_name_dc_ties`(→ pandas.DataFrame)
`get_indicative_lmp_by_settlement_point`(→ pandas.DataFrame)
`get_indicative_mcpc_rtd`(→ pandas.DataFrame)	Get RTD Indicative Real-Time Market Clearing Prices for Capacity
`get_interconnection_queue`(→ pandas.DataFrame)	Get interconnection queue for ERCOT
`get_lmp`(→ pandas.DataFrame)	Get LMP data for ERCOT normally produced by SCED every five minutes
`get_lmp_by_bus_dam`(→ pandas.DataFrame)	Get Day-Ahead Market (DAM) LMPs by Electrical Bus
`get_load`(→ pandas.DataFrame)	Get load for a date
`get_load_by_forecast_zone`(→ pandas.DataFrame)	Get hourly load for ERCOT forecast zones
`get_load_by_weather_zone`(→ pandas.DataFrame)	Get hourly load for ERCOT weather zones
`get_load_forecast`(→ pandas.DataFrame)	Returns load forecast of specified forecast type.
`get_load_forecast_by_model`(→ pandas.DataFrame)	Get Seven-Day Load Forecast by Model and Weather Zone.
`get_mcpc_dam`(→ pandas.DataFrame)	Get Market Clearing Prices for Capacity (MCPC) from the Day-Ahead Market
`get_mcpc_dam_price_corrections`(→ pandas.DataFrame)	Get Market Clearing Price for Capacity (MCPC) corrections for DAM.
`get_mcpc_real_time_15_min`(→ pandas.DataFrame)	Get Market Clearing Prices for Capacity by 15-minute interval
`get_mcpc_sced`(→ pandas.DataFrame)	Get Market Clearing Prices for Capacity by SCED interval
`get_noie_mapping`(→ pandas.DataFrame)
`get_operations_messages`(→ pandas.DataFrame)	Get operations messages from the ERCOT control room.
`get_raw_interconnection_queue`(→ BinaryIO)
`get_real_time_adders`(→ pandas.DataFrame)	Get Real-Time ORDC and Reliability Deployment
`get_real_time_adders_and_reserves`(→ pandas.DataFrame)	Get Real-Time ORDC and Reliability Deployment Price Adders and
`get_real_time_system_conditions`(→ pandas.DataFrame)	Get Real-Time System Conditions.
`get_reported_outages`(→ pandas.DataFrame)	Retrieves the 5-minute data behind this dashboard:
`get_resource_node_to_unit`(→ pandas.DataFrame)
`get_rtm_price_corrections`(→ pandas.DataFrame)	Get RTM Price Corrections
`get_rtm_spp`(→ pandas.DataFrame)	Get Historical RTM Settlement Point Prices(SPPs)
`get_sara`(→ pandas.DataFrame)	Parse SARA data from url.
`get_sced_system_lambda`(→ pandas.DataFrame)	Get System lambda of each successful SCED
`get_settlement_points_electrical_bus_mapping`(...)
`get_shadow_prices_dam`(→ pandas.DataFrame)	Get Day-Ahead Market Shadow Prices
`get_short_term_system_adequacy`(→ pandas.DataFrame)	Get Short Term System Adequacy published between date and end.
`get_solar_actual_and_forecast_by_geographical_region_hourly`(date)	Get Hourly Solar Report by geographical region
`get_solar_actual_and_forecast_hourly`(date[, end, verbose])	Get Hourly Solar Report.
`get_spp`(→ pandas.DataFrame)	Get SPP data for ERCOT
`get_status`(→ pandas.DataFrame)	Returns status of grid
`get_system_as_capacity_monitor`(→ pandas.DataFrame)	Get System Ancillary Service Capacity Monitor.
`get_system_wide_actual_load`(→ pandas.DataFrame)	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`(→ pandas.DataFrame)	Get Unplanned Resource Outages.
`get_wind_actual_and_forecast_by_geographical_region_hourly`(date)	Get Hourly Wind Report by geographical region
`get_wind_actual_and_forecast_hourly`(date[, end, verbose])	Get Hourly Wind Report.
`parse_doc`(→ pandas.DataFrame)
`read_doc`(→ pandas.DataFrame)
`read_docs`(→ pandas.DataFrame)

ambiguous_based_on_dstflag(df: pandas.DataFrame) → pandas.Series[source]#

Get the bid price and name of the Load Resource submitting the highest-priced bid selected or dispatched by SCED for the given operating day.

Published daily, three days after the applicable operating day.

https://www.ercot.com/mp/data-products/data-product-details?id=np3-257-ex

Parameters:

date – operating day to fetch data for.
end – optional end operating day for date range queries.
verbose – print verbose output.

Returns:

Interval Start, Interval End, SCED Timestamp, QSE, DME, Load Resource, Highest Price Dispatched by SCED, Proxy Extension.

Return type:

pandas.DataFrame with columns

get_60_day_dam_disclosure(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, process: bool = False, verbose: bool = False, output_format: gridstatus.ercot_60d_utils.CurveOutputFormat | str = CurveOutputFormat.LIST) → dict[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_only_offer_awards”
“dam_energy_only_offers”
“dam_ptp_obligation_bid_awards”
“dam_ptp_obligation_bids”
“dam_energy_bid_awards”
“dam_energy_bids”
“dam_ptp_obligation_option”
“dam_ptp_obligation_option_awards”
“dam_esr” (when available, starting 2025-12-06)
“dam_esr_as_offers” (when available, starting 2025-12-06)

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.

Parameters:: output_format – CurveOutputFormat.LIST (default) returns Python list-of-lists per curve cell. CurveOutputFormat.PG_ARRAY_AS_STRING returns PG array strings, using ~3x less peak memory.

get_60_day_sced_disclosure(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, process: bool = False, verbose: bool = False, output_format: gridstatus.ercot_60d_utils.CurveOutputFormat | str = CurveOutputFormat.LIST) → dict[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.
output_format – CurveOutputFormat.LIST (default) returns Python list-of-lists per curve cell. CurveOutputFormat.PG_ARRAY_AS_STRING returns PG array strings, using ~3x less peak memory.

Returns:

dictionary with keys “sced_load_resource”, “sced_gen_resource”,: ”sced_smne”, and (when available) “sced_esr”, “sced_eoc_updates”, “sced_resource_as_offers”, mapping to pandas.DataFrame objects

Return type:

dict

get_as_demand_curves_daily_ruc(date: str | pandas.Timestamp, end: str | pandas.Timestamp | None = None, verbose: bool = False) → pandas.DataFrame[source]#: Get Daily RUC Ancillary Service Demand Curves

get_as_demand_curves_dam_and_sced(date: str | pandas.Timestamp, end: str | pandas.Timestamp | None = None, verbose: bool = False) → pandas.DataFrame[source]#: Get Ancillary Service Demand Curves

get_as_demand_curves_hourly_ruc(date: str | pandas.Timestamp, end: str | pandas.Timestamp | None = None, verbose: bool = False) → pandas.DataFrame[source]#: Get Hourly RUC Ancillary Service Demand Curves

get_as_demand_curves_weekly_ruc(date: str | pandas.Timestamp, end: str | pandas.Timestamp | None = None, verbose: bool = False) → pandas.DataFrame[source]#: Get Weekly RUC Ancillary Service Demand Curves

get_as_deployment_factors_daily_ruc(date: str | pandas.Timestamp, end: str | pandas.Timestamp | None = None, verbose: bool = False) → pandas.DataFrame[source]#: Get Daily RUC Ancillary Service Deployment Factors

get_as_deployment_factors_hourly_ruc(date: str | pandas.Timestamp, end: str | pandas.Timestamp | None = None, verbose: bool = False) → pandas.DataFrame[source]#: Get Hourly RUC Ancillary Service Deployment Factors

get_as_deployment_factors_projected(date: str | pandas.Timestamp, end: str | pandas.Timestamp | None = None, verbose: bool = False) → pandas.DataFrame[source]#: Get Projected Ancillary Service Deployment Factors

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

Get Weekly RUC Ancillary Service Deployment Factors

Retrieves ancillary service deployment factors used by the Weekly Reliability Unit Commitment (WRUC) process for each hour in the RUC Study Period.

Parameters:

date – Date to retrieve data for. Can be a string or pandas Timestamp.
end – Optional end date for date range queries.
verbose – If True, print verbose output.

Returns:

Interval Start, Interval End, RUC Timestamp, AS Type, and AS Deployment Factors.

Return type:

DataFrame with columns

get_as_monitor(date: str = 'latest', verbose: bool = False) → pandas.DataFrame[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

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

Get Ancillary Services Reports.

Published with a 2 day delay around 3am central

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

Get Day-Ahead Market Ancillary Services Reports.

Published with a 2 day delay around 3am central.

Contains cleared, self-arranged, and bid curve data for each AS product.

Parameters:

date – date to fetch reports for
verbose – print verbose output

Returns:

A DataFrame with DAM ancillary services reports

Return type:

pandas.DataFrame

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

Get 2-Day SCED Ancillary Service Disclosure Reports.

Published with a 2 day delay around 3am central.

Contains offer curves (MW offered and price) for each AS product at each SCED timestamp.

Output columns: SCED Timestamp, AS Type, Offer Curve

Parameters:

date – date to fetch reports for
verbose – print verbose output

Returns:

A DataFrame with SCED ancillary services offers

Return type:

pandas.DataFrame

get_as_total_capability(date: str | pandas.Timestamp, end: str | pandas.Timestamp | None = None, verbose: bool = False) → pandas.DataFrame[source]#: Get Total Capability of Resources Available to Provide Ancillary Service

get_available_seasonal_capacity_forecast(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = 'latest', verbose: bool = False) → pandas.DataFrame[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: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = 'latest', verbose: bool = False) → pandas.DataFrame[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: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = 'latest', verbose: bool = False) → pandas.DataFrame[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_ccp_resource_names(date: str | pandas.Timestamp | None = None, end: str | pandas.Timestamp | None = None, verbose: bool = False) → pandas.DataFrame[source]#

get_cop_adjustment_period_snapshot_60_day(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, verbose: bool = False) → pandas.DataFrame[source]#

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

Get DAM Aggregated Ancillary Service Offer Curve (NP4-19-CD).

The DAM Aggregated Ancillary Service Demand/Offer Curve contains the aggregated offer curve (price/quantity pairs) per ancillary service type for each hour of the next day’s delivery, published once per DAM run. date and end are interpreted as delivery dates; the DAM runs the day before, so the underlying file is located by shifting the posted-date filter back by one day.

https://www.ercot.com/mp/data-products/data-product-details?id=np4-19-cd

get_dam_price_corrections(dam_type: str, verbose: bool = False) → pandas.DataFrame[source]#

Get DAM Price Corrections

Parameters:: dam_type (str) – ‘DAM_SPP’, ‘DAM_EBLMP’

get_dam_spp(year: int, verbose: bool = False) → pandas.DataFrame[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 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_dam_total_as_sold(date: str | pandas.Timestamp, end: str | pandas.Timestamp | None = None, verbose: bool = False) → pandas.DataFrame[source]#: Get DAM Total Ancillary Services Sold

Get DAM Total Energy Purchased

Parameters:

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

Returns:

A DataFrame with DAM total energy purchased data

Return type:

pandas.DataFrame

Get DAM Total Energy Sold

Parameters:

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

Returns:

A DataFrame with DAM total energy sold data

Return type:

pandas.DataFrame

get_energy_storage_resources(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = 'latest', verbose: bool = False) → pandas.DataFrame[source]#: Get energy storage resources. Always returns data from previous and current day

get_fuel_mix(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], verbose: bool = False) → pandas.DataFrame[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_fuel_mix_detailed(date: str | datetime.datetime | pandas.Timestamp, verbose: bool = False) → pandas.DataFrame[source]#: The fuel mix with gen, hsl, and seasonal capacity for each fuel type.

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

Get the offer price and the name of the Entity submitting the offer for the highest-priced Ancillary Service (AS) Offer selected in the Day-Ahead Market (DAM).

Published with 3 day delay

Parameters:

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

Returns:

A DataFrame with columns:

Interval Start
Interval End
QSE
DME
Resource Name
AS Type
Block Indicator
Offered Price
Total Offered Quantity
Offered Quantities

Return type:

pandas.DataFrame

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

Get the offer price and the name of the Entity submitting the offer for the highest-priced Ancillary Service (AS) Offer selected in the Real-Time Market (SCED).

Published with 3 day delay

Parameters:

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

Returns:

A DataFrame with columns:

SCED Timestamp
QSE
DME
Resource Name
AS Type
Offered Price
Total Offered Quantity
Offered Quantities

Return type:

pandas.DataFrame

Get historical hourly load data from ERCOT’s load archives.

Downloads zip files from https://www.ercot.com/gridinfo/load/load_hist and parses the historical load data by weather zones.

Parameters:

date (str, datetime) – Year to download data for, or “latest” for most recent data
end (str, datetime) – End date for range, or None for single date
verbose (bool, optional) – print verbose output. Defaults to False.

Returns:

pandas.DataFrame

get_hourly_resource_outage_capacity(date: str | pandas.Timestamp, end: str | pandas.Timestamp | None = None, verbose: bool = False) → pandas.DataFrame[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, pd.Timestamp) – time to download. Returns last hourly report before this time. Supports “latest”
end (str, pd.Timestamp, 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_hub_name_dc_ties(date: str | pandas.Timestamp | None = None, end: str | pandas.Timestamp | None = None, verbose: bool = False) → pandas.DataFrame[source]#

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

get_indicative_mcpc_rtd(date: str | pandas.Timestamp, end: str | pandas.Timestamp | None = None, verbose: bool = False) → pandas.DataFrame[source]#: Get RTD Indicative Real-Time Market Clearing Prices for Capacity

get_interconnection_queue(verbose: bool = False) → pandas.DataFrame[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: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, location_type: str = SETTLEMENT_POINT_LOCATION_TYPE, verbose: bool = False) → pandas.DataFrame[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 Day-Ahead Market (DAM) LMPs by Electrical Bus

Returns hourly Locational Marginal Prices per electrical bus from the Day-Ahead Market.

https://www.ercot.com/mp/data-products/data-product-details?id=NP4-183-CD

Parameters:

date (str, datetime) – date to get data for. Supports “latest”, “today”, or a specific date.
end (str, datetime, optional) – end date for a date range query. If None, returns 1 day of data. Defaults to None.
verbose (bool, optional) – print verbose output. Defaults to False.

Returns:

A DataFrame with day-ahead LMPs by electrical bus

Return type:

pandas.DataFrame

Get load for a date

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

get_load_by_forecast_zone(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], verbose: bool = False) → pandas.DataFrame[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: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], verbose: bool = False) → pandas.DataFrame[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: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, forecast_type: ERCOTSevenDayLoadForecastReport = ERCOTSevenDayLoadForecastReport.BY_FORECAST_ZONE, verbose: bool = False) → pandas.DataFrame[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 Seven-Day Load Forecast by Model and Weather Zone.

Forecasted hourly demand by Model and Weather Zone as reported by ERCOT. Released every hour for the current day and the next 7.

Parameters:

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

Returns:

A DataFrame with load forecast by model data

Return type:

pandas.DataFrame

Source:: https://www.ercot.com/mp/data-products/data-product-details?id=NP3-565-CD

Get Market Clearing Prices for Capacity (MCPC) from the Day-Ahead Market

Returns hourly MCPC per ancillary service type in long format.

https://www.ercot.com/mp/data-products/data-product-details?id=NP4-188-CD

Parameters:

date (str, datetime) – date to get data for. Supports “latest”, “today”, or a specific date.
end (str, datetime, optional) – end date for a date range query. If None, returns 1 day of data. Defaults to None.
verbose (bool, optional) – print verbose output. Defaults to False.

Returns:

A DataFrame with columns: Interval Start,: Interval End, AS Type, MCPC

Return type:

pandas.DataFrame

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

Get Market Clearing Price for Capacity (MCPC) corrections for DAM.

MCPC (Market Clearing Price for Capacity) corrections contain ancillary service prices at the system level.

Returns:

DataFrame with columns:

Price Correction Time
Interval Start
Interval End
AS Type
MCPC Original
MCPC Corrected

Return type:

pd.DataFrame

get_mcpc_real_time_15_min(date: str | pandas.Timestamp, end: str | pandas.Timestamp | None = None, verbose: bool = False) → pandas.DataFrame[source]#: Get Market Clearing Prices for Capacity by 15-minute interval

get_mcpc_sced(date: str | pandas.Timestamp, end: str | pandas.Timestamp | None = None, verbose: bool = False) → pandas.DataFrame[source]#: Get Market Clearing Prices for Capacity by SCED interval

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

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

Get operations messages from the ERCOT control room.

When called without date arguments, scrapes the live page at https://www.ercot.com/services/comm/mkt_notices/opsmessages which shows a rolling window of recent messages (~one month).

When called with a date (and optional end), fetches historical snapshots from the Wayback Machine covering the requested range, deduplicates, and filters to the requested window.

Parameters:

date – Start date for historical query, or None for latest.
end – End date for historical query. Defaults to date + 1 month.
verbose – Print verbose output.

Returns:

pandas.DataFrame with columns Time, Notice, Type, Status.

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

Get Real-Time ORDC and Reliability Deployment Price Adders and Reserves by SCED Interval produced by SCED every five minutes.

Parameters:

date – date to get data for
end – end date to get data for. If None, defaults to date + 1 day
verbose – print verbose output. Defaults to False.

Returns:

A DataFrame with ORDC price adders data

Return type:

pandas.DataFrame

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: str = 'latest', verbose: bool = False) → pandas.DataFrame[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

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

get_rtm_price_corrections(rtm_type: str, verbose: bool = False) → pandas.DataFrame[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: int, verbose: bool = False) → pandas.DataFrame[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: str = 'https://www.ercot.com/files/docs/2023/05/05/SARA_Summer2023_Revised.xlsx', verbose: bool = False) → pandas.DataFrame[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 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_settlement_points_electrical_bus_mapping(date: str | pandas.Timestamp | None = None, end: str | pandas.Timestamp | None = None, verbose: bool = False) → pandas.DataFrame[source]#

Get Day-Ahead Market Shadow Prices

Returns shadow prices for binding transmission constraints from the Day-Ahead Market.

https://www.ercot.com/mp/data-products/data-product-details?id=NP4-191-CD

Parameters:

date (str, datetime) – date to get data for. Supports “latest”, “today”, or a specific date.
end (str, datetime, optional) – end date for a date range query. If None, returns 1 day of data. Defaults to None.
verbose (bool, optional) – print verbose output. Defaults to False.

Returns:

A DataFrame with day-ahead market shadow prices

Return type:

pandas.DataFrame

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_solar_actual_and_forecast_by_geographical_region_hourly(date: str | datetime.date, end: str | datetime.date = None, verbose: bool = False)[source]#

Get Hourly Solar Report by geographical region

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_solar_actual_and_forecast_hourly(date: str | datetime.date, end: str | datetime.date = None, verbose: bool = False)[source]#

Get Hourly Solar Report.

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_spp(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, market: str = None, locations: list = 'ALL', location_type: str = 'ALL', verbose: bool = False) → pandas.DataFrame[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: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], verbose: bool = False) → pandas.DataFrame[source]#: Returns status of grid

get_system_as_capacity_monitor(date: str | None = None, verbose: bool = False) → pandas.DataFrame[source]#

Get System Ancillary Service Capacity Monitor.

Fetches real-time ancillary service capacity data from https://www.ercot.com/api/1/services/read/dashboards/ancillary-service-capacity-monitor.json

Parameters:

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

Returns:

A DataFrame with system AS capacity monitor data

Return type:

pandas.DataFrame

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: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, verbose: bool = False) → pandas.DataFrame[source]#

Get 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: str | pandas.Timestamp, end: str | pandas.Timestamp | None = None, verbose: bool = False) → pandas.DataFrame[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

get_wind_actual_and_forecast_by_geographical_region_hourly(date: str | datetime.date, end: str | datetime.date = None, verbose: bool = False)[source]#

Get Hourly Wind Report by geographical region

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_wind_actual_and_forecast_hourly(date: str | datetime.date, end: str | datetime.date = None, verbose: bool = 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

parse_doc(doc: pandas.DataFrame, dst_ambiguous_default: str = 'infer', verbose: bool = False, nonexistent: str = 'raise') → pandas.DataFrame[source]#

read_doc(doc: Document, parse: bool = True, verbose: bool = False, request_kwargs: dict | None = None, read_csv_kwargs: dict | None = None) → pandas.DataFrame[source]#

read_docs(docs: list[Document], parse: bool = True, empty_df: pandas.DataFrame | None = None, verbose: bool = False, request_kwargs: dict | None = None) → pandas.DataFrame[source]#

gridstatus.ercot.ERCOT_INDICATIVE_LMP_BY_SETTLEMENT_POINT_RTID = 13073[source]#

class gridstatus.ercot.ERCOTSevenDayLoadForecastReport(*args, **kwds)[source]#

Bases: enum.Enum

Enum class for the Medium Term (Seven Day) Load Forecasts. The values are the report IDs.

Attributes

BY_FORECAST_ZONE	12311
BY_MODEL_AND_STUDY_AREA	15953
BY_MODEL_AND_WEATHER_ZONE	14837
BY_WEATHER_ZONE	12312

gridstatus.ercot.GIS_REPORT_RTID = 15933[source]#

gridstatus.ercot.HISTORICAL_DAM_LOAD_ZONE_AND_HUB_PRICES_RTID = 13060[source]#

gridstatus.ercot.HISTORICAL_RTM_LOAD_ZONE_AND_HUB_PRICES_RTID = 13061[source]#

gridstatus.ercot.HOURLY_RESOURCE_OUTAGE_CAPACITY_RTID = 13103[source]#

gridstatus.ercot.HOURLY_RUC_AS_DEMAND_CURVES_RTID = 26382[source]#

gridstatus.ercot.HOURLY_RUC_AS_DEPLOYMENT_FACTORS_RTID = 24896[source]#

gridstatus.ercot.HUB_SETTLEMENT_TYPES = ['HU', 'SH', 'AH'][source]#

gridstatus.ercot.LMPS_BY_ELECTRICAL_BUS_RTID = 11485[source]#

gridstatus.ercot.LMPS_BY_SETTLEMENT_POINT_RTID = 12300[source]#

gridstatus.ercot.LOAD_ZONE_SETTLEMENT_TYPES = ['LZ', 'LZ_DC'][source]#

gridstatus.ercot.LOCATION_TYPE_HUB = 'Trading Hub'[source]#

gridstatus.ercot.LOCATION_TYPE_RESOURCE_NODE = 'Resource Node'[source]#

gridstatus.ercot.LOCATION_TYPE_ZONE = 'Load Zone'[source]#

gridstatus.ercot.LOCATION_TYPE_ZONE_DC = 'Load Zone DC Tie'[source]#

gridstatus.ercot.LOCATION_TYPE_ZONE_DC_EW = 'Load Zone DC Tie Energy Weighted'[source]#

gridstatus.ercot.LOCATION_TYPE_ZONE_EW = 'Load Zone Energy Weighted'[source]#

gridstatus.ercot.parse_timestamp_from_friendly_name(friendly_name: str) → pandas.Timestamp[source]#

gridstatus.ercot.PROJECTED_ANCILLARY_SERVICE_DEPLOYMENTS_FACTORS_RTID = 24886[source]#

gridstatus.ercot.REAL_TIME_ADDERS_AND_RESERVES_RTID = 13221[source]#

gridstatus.ercot.REAL_TIME_ADDERS_RTID = 13221[source]#

gridstatus.ercot.REAL_TIME_CLEARING_LMPS_BY_RESOURCE_NODES_LOAD_ZONES_AND_TRADING_HUBS_RTD = 4104[source]#

gridstatus.ercot.REAL_TIME_CLEARING_PRICES_FOR_CAPACITY_15_MIN_RTID = 24898[source]#

gridstatus.ercot.REAL_TIME_CLEARING_PRICES_FOR_CAPACITY_BY_SCED_INTERVAL_RTID = 24891[source]#

gridstatus.ercot.RESOURCE_NODE_SETTLEMENT_TYPES = ['RN', 'PCCRN', 'LCCRN', 'PUN'][source]#

gridstatus.ercot.RTD_INDICATIVE_REAL_TIME_MCPC_RTID = 24889[source]#

gridstatus.ercot.RTM_PRICE_CORRECTIONS_RTID = 13045[source]#

gridstatus.ercot.SCED_SYSTEM_LAMBDA_RTID = 13114[source]#

gridstatus.ercot.SETTLEMENT_POINT_LOCATION_TYPE = 'Settlement Point'[source]#: Report Type IDs

gridstatus.ercot.SETTLEMENT_POINT_PRICES_AT_RESOURCE_NODES_HUBS_AND_LOAD_ZONES_RTID = 12301[source]#

gridstatus.ercot.SETTLEMENT_POINTS_LIST_AND_ELECTRICAL_BUSES_MAPPING_RTID = 10008[source]#

gridstatus.ercot.SHORT_TERM_SYSTEM_ADEQUACY_REPORT_RTID = 12315[source]#

gridstatus.ercot.SIXTY_DAY_DAM_DISCLOSURE_REPORTS_RTID = 13051[source]#

gridstatus.ercot.SIXTY_DAY_SCED_DISCLOSURE_REPORTS_RTID = 13052[source]#

gridstatus.ercot.SOLAR_POWER_PRODUCTION_HOURLY_AVERAGED_ACTUAL_AND_FORECASTED_VALUES_BY_GEOGRAPHICAL_REGION_RTID = 21809[source]#

gridstatus.ercot.SOLAR_POWER_PRODUCTION_HOURLY_AVERAGED_ACTUAL_AND_FORECASTED_VALUES_RTID = 13483[source]#: Settlement Point Type Description ========== ========== =========== Resource Node RN Resource Node for normal resource Resource Node PCCRN Physical Resource Node for combined cycle units Resource Node LCCRN Logical Resource Node for combined cycle plant Resource Node PUN Private Area Network Resource Node Load Zone LZ Congestion Load Zone Load Zone LZ_DC DCTIE Load Zone Hub HU Hub Hub SH ERCOT_345KV_HUBBUSES_AVG Hub AH ERCOT_HUB_AVG ============================================================ Source: https://www.ercot.com/files/docs/2009/10/26/07_tests_for_rsnable_lmps_overview_of_price_valid_tool_09102.ppt

gridstatus.ercot.SYSTEM_WIDE_ACTUALS_RTID = 12340[source]#

gridstatus.ercot.TEMPERATURE_FORECAST_BY_WEATHER_ZONE_RTID = 12325[source]#

gridstatus.ercot.THREE_DAY_DAM_HIGHEST_PRICE_AS_OFFER_SELECTED_RTID = 13018[source]#

gridstatus.ercot.THREE_DAY_HIGHEST_PRICE_BIDS_SCED_RTID = 13230[source]#

gridstatus.ercot.THREE_DAY_SCED_HIGHEST_PRICE_AS_OFFER_SELECTED_RTID = 25812[source]#

gridstatus.ercot.TOTAL_CAPABILITY_OF_RESOURCES_AS_RTID = 24887[source]#

gridstatus.ercot.TWO_DAY_ANCILLARY_SERVICES_REPORTS_RTID = 13057[source]#

gridstatus.ercot.TWO_DAY_SCED_ANCILLARY_SERVICES_REPORTS_RTID = 25814[source]#

gridstatus.ercot.UNPLANNED_RESOURCE_OUTAGES_REPORT_RTID = 22912[source]#

gridstatus.ercot.WEEKLY_RUC_AS_DEMAND_CURVES_RTID = 26384[source]#

gridstatus.ercot.WEEKLY_RUC_AS_DEPLOYMENT_FACTORS_RTID = 24897[source]#

gridstatus.ercot.WIND_POWER_PRODUCTION_HOURLY_AVERAGED_ACTUAL_AND_FORECASTED_VALUES_BY_GEOGRAPHICAL_REGION_RTID = 14787[source]#

gridstatus.ercot.WIND_POWER_PRODUCTION_HOURLY_AVERAGED_ACTUAL_AND_FORECASTED_VALUES_RTID = 13028[source]#