Pjm

Contents

Pjm#

Module Contents#

Classes Summary#

PJM

PJM

Contents#

class gridstatus.pjm.PJM(api_key: str | None = None, retries: int = 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

api_key

None

AS_MARKET_RESULTS_GRANULARITY_CHANGE_DATE

‘2022-09-01’

AS_MARKET_RESULTS_START_DATE

‘2013-06-14’

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_5_min_endpoint_name

‘very_short_load_frcst’

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’)

retries

3

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_actual_and_scheduled_interchange_summary(...)

Retrieves the actual and scheduled interchange summary data from:

get_actual_operational_statistics(→ pandas.DataFrame)

Retrieves the actual operational statistics data from:

get_area_control_error(→ pandas.DataFrame)

Retrieves the area control error data from:

get_as_market_results_real_time_hourly(date[, end, ...])

Retrieves hourly real-time ancillary service market results prior to 2022-09-01.

get_cleared_virtuals_daily(→ pandas.DataFrame)

Retrieves the daily cleared virtual transactions data from:

get_dam_as_market_results(date[, end, verbose])

Retrieves the day-ahead ancillary service market results from :

get_day_ahead_demand_bids(→ pandas.DataFrame)

Retrieves the day ahead demand bids data from:

get_dispatched_reserves_prelim(→ pandas.DataFrame)

Retrieves the dispatched reserves preliminary data from:

get_dispatched_reserves_verified(→ pandas.DataFrame)

Retrieves the dispatched reserves verified data from:

get_emergency_postings(→ pandas.DataFrame)

Retrieves PJM emergency procedure postings by triggering the public

get_forecasted_generation_outages(date[, end, verbose])

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

get_fuel_mix(→ pandas.DataFrame)

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

get_gen_outages_by_type(→ pandas.DataFrame)

Retrieves the generation outage data

get_generation_capacity_daily(→ pandas.DataFrame)

Retrieves the daily generation capacity data from:

get_hourly_net_exports_by_state(→ pandas.DataFrame)

Retrieves the hourly net exports by state data from:

get_hourly_transfer_limits_and_flows(→ pandas.DataFrame)

Retrieves the hourly transfer limits and flows data from:

get_inc_and_dec_bids_day_ahead_hourly(→ pandas.DataFrame)

Retrieves the hourly day-ahead increment and decrement bids data from:

get_instantaneous_dispatch_rates(→ pandas.DataFrame)

Retrieves the instantaneous dispatch rate data from:

get_interconnection_queue(→ pandas.DataFrame)

get_interface_flows_and_limits_day_ahead(...)

Retrieves the interface flows and limit day ahead data from:

get_it_sced_lmp_5_min(→ pandas.DataFrame)

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

get_lmp(→ pandas.DataFrame)

Returns LMP at a previous date.

get_lmp_real_time_unverified_5_min(→ pandas.DataFrame)

Get real-time unverified 5-minute LMPs for a date range.

get_lmp_real_time_unverified_hourly(→ pandas.DataFrame)

Get real-time unverified hourly LMPs

get_load(→ pandas.DataFrame)

Returns load at a previous date at 5 minute intervals.

get_load_forecast(→ pandas.DataFrame)

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

get_load_forecast_5_min(→ pandas.DataFrame)

Load forecast made today extending for 2 hours in 5 minute intervals.

get_load_forecast_historical(→ pandas.DataFrame)

Historical load forecast in hourly intervals. Historical forecasts include all

get_load_metered_hourly(date[, end, verbose])

Retrieves the hourly metered load data from:

get_marginal_emission_rates_5_min(→ pandas.DataFrame)

Retrieves the 5-minute marginal emission rates data from PJM.

get_marginal_value_day_ahead_hourly(→ pandas.DataFrame)

Retrieves the marginal value data from:

get_marginal_value_real_time_5_min(→ pandas.DataFrame)

Retrieves the marginal value data from:

get_operational_reserves(→ pandas.DataFrame)

Retrieves the reserve market quantities in Megawatts from:

get_pai_intervals_5_min(→ pandas.DataFrame)

Retrieves the 5-minute Performance Assessment Intervals (PAI) data from PJM.

get_pnode_ids(→ pandas.DataFrame)

get_pricing_nodes(→ pandas.DataFrame)

Retrieves the pricing nodes data from:

get_projected_area_statistics_at_peak(→ pandas.DataFrame)

Area projected data for the peak of the day

get_projected_peak_tie_flow(→ pandas.DataFrame)

Retrieves the projected peak tie flow data from:

get_projected_rto_statistics_at_peak(→ pandas.DataFrame)

RTO-wide projected data for the peak of the day

get_raw_interconnection_queue(→ BinaryIO)

get_real_time_as_market_results(date[, end, verbose])

Retrieves the real-time ancillary service market results from :

get_regulation_market_monthly(→ pandas.DataFrame)

Retrieves the PJM Regulation Market Monthly data from:

get_regulation_prices_5_min(→ pandas.DataFrame)

Retrieves 5-minute regulation pricing data from:

get_reserve_subzone_buses(→ pandas.DataFrame)

Retrieves the reserve subzone buses data from:

get_reserve_subzone_resources(→ pandas.DataFrame)

Retrieves the reserve subzone resources data from:

get_scheduled_interchange_real_time(→ pandas.DataFrame)

Retrieves the scheduled interchange real time data from:

get_settlements_verified_lmp_5_min(→ pandas.DataFrame)

get_settlements_verified_lmp_hourly(→ pandas.DataFrame)

get_solar_forecast_5_min(→ pandas.DataFrame)

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

get_solar_forecast_hourly(→ pandas.DataFrame)

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

get_solar_generation_5_min(→ pandas.DataFrame)

Retrieves the 5 min solar generation data from:

get_solar_generation_by_area(→ pandas.DataFrame)

Retrieves the current solar generation information from:

get_sync_reserve_events(→ pandas.DataFrame)

Retrieves the synchronized reserve events data from:

get_tie_flows_5_min(→ pandas.DataFrame)

Retrieves the PJM Tie Flows 5 Minute data from:

get_transfer_interface_information_5_min(...)

Retrieves the transfer interface information from:

get_transmission_constraints_day_ahead_hourly(...)

Retrieves the transmission constraints data from:

get_transmission_limits(→ pandas.DataFrame)

Retrieves the current transmission limit information from:

get_voltage_limits(→ str)

Downloads the raw voltage limits CSV file from EDART.

get_weight_average_aggregation_definition(...)

Retrieves the weight average aggregation definition data from:

get_wind_forecast_5_min(→ pandas.DataFrame)

Retrieves the 5-min wind forecast

get_wind_forecast_hourly(→ pandas.DataFrame)

Retrieves the hourly wind forecast

get_wind_generation_by_area(date[, end, verbose])

Retrieves the current wind generation information from:

get_wind_generation_instantaneous(→ pandas.DataFrame)

Retrieves the instantaneous wind generation data from:

to_local_datetime(→ pandas.Series)

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

Retrieves the actual and scheduled interchange summary data from: https://dataminer2.pjm.com/feed/actual_and_scheduled_interchange_summary/definition

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

Retrieves the actual operational statistics data from: https://dataminer2.pjm.com/feed/ops_sum_prev_period/definition

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

Retrieves the area control error data from: https://dataminer2.pjm.com/feed/area_control_error/definition

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

Retrieves hourly real-time ancillary service market results prior to 2022-09-01.

This method queries historical data before the granularity changed from hourly to 5-minute intervals. For data on or after September 1, 2022, use get_real_time_as_market_results().

Parameters:

  • date – Start date. Must be between 2013-06-14 and 2022-08-31.

  • end – End date. Must be before 2022-09-01.

  • verbose – Print verbose output.

Returns:

DataFrame with hourly AS market results.

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

Retrieves the daily cleared virtual transactions data from: https://dataminer2.pjm.com/feed/day_inc_dec_utc/definition

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

Retrieves the day ahead demand bids data from: https://dataminer2.pjm.com/feed/hrl_dmd_bids/definition

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

Retrieves the dispatched reserves preliminary data from: https://dataminer2.pjm.com/feed/dispatched_reserves/definition

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

Retrieves the dispatched reserves verified data from: https://dataminer2.pjm.com/feed/rt_dispatch_reserves/definition

get_emergency_postings(url: str | None = None) → pandas.DataFrame[source]#

Retrieves PJM emergency procedure postings by triggering the public “Export To XML” button on the guest dashboard.

Two-step flow (no credentials required):

  1. GET the guest dashboard to obtain a session cookie and JSF ViewState.

  2. POST frmButtons:lnkDownload to download the XML export.

The XML contains Publish Time, Canceled Time, proper UTC start/end timestamps, and individual Region elements (one DataFrame row per message-region pair).

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

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

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

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

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

Retrieves the daily generation capacity data from: https://dataminer2.pjm.com/feed/day_gen_capacity/definition

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

Retrieves the hourly net exports by state data from: https://dataminer2.pjm.com/feed/state_net_interchange/definition

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

Retrieves the hourly transfer limits and flows data from: https://dataminer2.pjm.com/feed/transfer_limits_and_flows/definition

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

Retrieves the hourly day-ahead increment and decrement bids data from: https://dataminer2.pjm.com/feed/hrl_da_incs_decs/definition

Note: This data has a 4-month publication delay.

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

Retrieves the instantaneous dispatch rate data from: https://dataminer2.pjm.com/feed/inst_dispatch_rate/definition

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

Retrieves the interface flows and limit day ahead data from: https://dataminer2.pjm.com/feed/da_interface_flows_and_limits/definition

get_it_sced_lmp_5_min(date: str | pandas.Timestamp, end: str | pandas.Timestamp | None = None, verbose: bool = False) → pandas.DataFrame[source]#

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

get_lmp(date: str | pandas.Timestamp, market: str, end: str | pandas.Timestamp | None = None, locations: str = 'hubs', location_type: str | None = None, verbose: bool = False) → pandas.DataFrame[source]#

Returns LMP at a previous date.

Note

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.

Returns Location Id, Location Name, Location Short Name.

Parameters:

  • date – Date to get LMPs for.

  • end – End date to get LMPs for.

  • market – Supported Markets: REAL_TIME_5_MIN, REAL_TIME_HOURLY, DAY_AHEAD_HOURLY.

  • locations – 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 of rows!).

  • location_type – 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_lmp_real_time_unverified_5_min(date: str | pandas.Timestamp, end: str | pandas.Timestamp | None = None, locations: str | list | None = 'hubs', location_type: str | None = None, verbose: bool = False) → pandas.DataFrame[source]#

Get real-time unverified 5-minute LMPs for a date range.

Mirrors the output shape of get_lmp(market=REAL_TIME_5_MIN) but always hits rt_unverified_fivemin_lmps so callers can pull the freshest data (verified is delayed ~25 minutes).

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

Get real-time unverified hourly LMPs

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

Returns load at a previous date at 5 minute intervals.

Parameters:

date – Date to get load for. Must be in last 30 days.

Returns:

Load data time series. Columns include 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.

get_load_forecast(date: str | pandas.Timestamp, end: str | pandas.Timestamp | None = None, verbose: bool = False) → pandas.DataFrame[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_5_min(date: str | pandas.Timestamp, end: str | pandas.Timestamp | None = None, verbose: bool = False) → pandas.DataFrame[source]#

Load forecast made today extending for 2 hours in 5 minute intervals.

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

Retrieves the hourly metered load data from:

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

get_marginal_emission_rates_5_min(date: str | pandas.Timestamp, end: str | pandas.Timestamp | None = None, verbose: bool = False) → pandas.DataFrame[source]#

Retrieves the 5-minute marginal emission rates data from PJM.

PJM estimates marginal emissions every five minutes for load zones across the grid. These estimates include CO₂, SO₂, and NOₓ, expressed in lbs/MWh. The calculation reflects the physical costs of power flows, capturing the impact of congestion on nodal emissions. When imports are marginal at a node, PJM assigns them zero emissions because the fuel source is unknown.

Source: https://dataminer2.pjm.com/feed/fivemin_marginal_emissions/definition

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 marginal emission rates data.

Return type:

pandas.DataFrame

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

Retrieves the marginal value data from: https://dataminer2.pjm.com/feed/da_marginal_value/definition

get_marginal_value_real_time_5_min(date: str | pandas.Timestamp, end: str | pandas.Timestamp = None, verbose: bool = False) → pandas.DataFrame[source]#

Retrieves the marginal value data from: https://dataminer2.pjm.com/feed/rt_marginal_value/definition

get_operational_reserves(date: str | pandas.Timestamp, end: str | pandas.Timestamp | None = None, verbose: bool = False) → pandas.DataFrame[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_pai_intervals_5_min(date: str | pandas.Timestamp, end: str | pandas.Timestamp | None = None, verbose: bool = False) → pandas.DataFrame[source]#

Retrieves the 5-minute Performance Assessment Intervals (PAI) data from PJM.

This dataset contains information on the status of Performance Assessment Intervals (PAIs) across PJM and subzones, updated every 5 minutes. Performance during these PAIs is used by PJM to determine potential penalties, or compensation, for capacity obligations. This dataset has 3 potential responses in the Performance Assessment Interval column: “No PAI”, “PAI in Active Subzone”, and “PAI in RTO and Active Subzone”.

Source: https://dataminer2.pjm.com/feed/fivemin_pai_interval/definition

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 PAI intervals data.

Return type:

pandas.DataFrame

get_pnode_ids() → pandas.DataFrame[source]#
get_pricing_nodes(as_of: str | pandas.Timestamp | None = None, verbose: bool = False) → pandas.DataFrame[source]#

Retrieves the pricing nodes data from: https://dataminer2.pjm.com/feed/pnode/definition

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

Area projected data for the peak of the day

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

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

Retrieves the projected peak tie flow data from: https://dataminer2.pjm.com/feed/ops_sum_prjctd_tie_flow/definition

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

Retrieves the PJM Regulation Market Monthly data from: https://dataminer2.pjm.com/feed/reg_market_results/definition

get_regulation_prices_5_min(date: str | pandas.Timestamp, end: str | pandas.Timestamp | None = None, verbose: bool = False) → pandas.DataFrame[source]#

Retrieves 5-minute regulation pricing data from: https://api.pjm.com/api/v1/reg_prices

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

Retrieves the reserve subzone buses data from: https://dataminer2.pjm.com/feed/sync_pri_reserves_buses_list/definition

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

Retrieves the reserve subzone resources data from: https://dataminer2.pjm.com/feed/sync_pri_reserves_resources_list/definition

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

Retrieves the scheduled interchange real time data from: https://dataminer2.pjm.com/feed/rt_scheduled_interchange/definition

get_settlements_verified_lmp_5_min(date: str | pandas.Timestamp, end: str | pandas.Timestamp | None = None, verbose: bool = False) → pandas.DataFrame[source]#
get_settlements_verified_lmp_hourly(date: str | pandas.Timestamp, end: str | pandas.Timestamp | None = None, verbose: bool = False) → pandas.DataFrame[source]#
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 = False) → pandas.DataFrame[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 = False) → pandas.DataFrame[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_sync_reserve_events(verbose: bool = False) → pandas.DataFrame[source]#

Retrieves the synchronized reserve events data from: https://dataminer2.pjm.com/feed/sync_reserve_events/definition

get_tie_flows_5_min(date: str | pandas.Timestamp, end: str | pandas.Timestamp | None = None, verbose: bool = False) → pandas.DataFrame[source]#

Retrieves the PJM Tie Flows 5 Minute data from: https://dataminer2.pjm.com/feed/tie_flows/definition

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

Retrieves the transmission constraints data from: https://dataminer2.pjm.com/feed/da_transconstraints/definition

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

Downloads the raw voltage limits CSV file from EDART.

The URL returns a ZIP file containing the CSV. This method extracts and returns the CSV content as a string.

Source: https://edart.pjm.com/reports/voltagelimits.csv

Parameters:

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

Returns:

The CSV content as a string.

Return type:

str

Raises:

NoDataFoundException – If the server returns a rate limit message.

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

Retrieves the weight average aggregation definition data from: https://dataminer2.pjm.com/feed/agg_definitions/definition

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 = 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 = False) → pandas.DataFrame[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: pandas.DataFrame, column_name: str) → pandas.Series[source]#
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