Nyiso

Nyiso#

Module Contents#

Classes Summary#

`DatasetInterval`
`NYISO`	New York Independent System Operator (NYISO)
`NYISOLocationType`	Enum where members are also (and must be) strings

Attributes Summary#

Contents#

gridstatus.nyiso.AS_PRICES_DAY_AHEAD_HOURLY_DATASET = 'damasp'[source]#

gridstatus.nyiso.AS_PRICES_REAL_TIME_5_MIN_DATASET = 'rtasp'[source]#

gridstatus.nyiso.BTM_INSTALLED_CAPACITY_DATASET = 'btminstalledcapacitytracking'[source]#

gridstatus.nyiso.BTM_SOLAR_ACTUAL_DATASET = 'btmactualforecast'[source]#

gridstatus.nyiso.BTM_SOLAR_FORECAST_DATASET = 'btmdaforecast'[source]#

gridstatus.nyiso.DAM_LMP_DATASET = 'damlbmp'[source]#

gridstatus.nyiso.DATASET_INTERVAL_MAP: Dict[str, DatasetInterval][source]#

class gridstatus.nyiso.DatasetInterval[source]#

Bases: NamedTuple

Attributes

interval_duration_minutes	None
time_type	None

gridstatus.nyiso.FUEL_MIX_DATASET = 'rtfuelmix'[source]#

gridstatus.nyiso.GENERATION_OUTAGES_FORECAST_URL = 'http://mis.nyiso.com/public/csv/genmaint/gen_maint_report.csv'[source]#

gridstatus.nyiso.INTERFACE_LIMITS_AND_FLOWS_DATASET = 'ExternalLimitsFlows'[source]#

gridstatus.nyiso.LAKE_ERIE_CIRCULATION_DAY_AHEAD_DATASET = 'eriecirculationda'[source]#

gridstatus.nyiso.LAKE_ERIE_CIRCULATION_REAL_TIME_DATASET = 'eriecirculationrt'[source]#

gridstatus.nyiso.LIMITING_CONSTRAINTS_DAY_AHEAD_DATASET = 'DAMLimitingConstraints'[source]#: Pricing data: https://www.nyiso.com/en/energy-market-operational-data

gridstatus.nyiso.LIMITING_CONSTRAINTS_REAL_TIME_DATASET = 'LimitingConstraints'[source]#

gridstatus.nyiso.LOAD_DATASET = 'pal'[source]#

gridstatus.nyiso.LOAD_FORECAST_DATASET = 'isolf'[source]#

class gridstatus.nyiso.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_as_prices_day_ahead_hourly`(→ pandas.DataFrame)	Pull the most recent ancillary service market report's market clearing prices
`get_as_prices_real_time_5_min`(→ pandas.DataFrame)	Pull the most recent ancillary service market report's market clearing prices
`get_btm_installed_capacity`(→ pandas.DataFrame)	Returns NYISO's daily estimate of installed behind-the-meter solar capacity
`get_btm_solar`(→ pandas.DataFrame)	Returns estimated BTM solar generation at a previous date in hourly
`get_btm_solar_forecast`(→ pandas.DataFrame)
`get_capacity_prices`(→ pandas.DataFrame)	Pull the most recent capacity market report's market clearing prices
`get_fuel_mix`(→ pandas.DataFrame)
`get_generation_outages_forecast`(→ pandas.DataFrame)	Get forecasted generation outage capacity for the next 30 days.
`get_generators`(→ pandas.DataFrame)	Get a list of generators in NYISO. When possible return capacity and fuel type information
`get_interconnection_queue`(→ pandas.DataFrame)	Return NYISO interconnection queue
`get_interface_limits_and_flows_5_min`(→ pandas.DataFrame)	Get interface limits and flows for a date
`get_lake_erie_circulation_day_ahead`(→ pandas.DataFrame)
`get_lake_erie_circulation_real_time`(→ pandas.DataFrame)
`get_limiting_constraints_day_ahead`(→ pandas.DataFrame)
`get_limiting_constraints_real_time`(→ pandas.DataFrame)
`get_lmp`(→ pandas.DataFrame)	Deprecated. Use the per-dataset methods instead:
`get_lmp_day_ahead_hourly`(→ pandas.DataFrame)	Get day-ahead hourly LMPs for all zone and generator locations.
`get_lmp_real_time_15_min`(→ pandas.DataFrame)	Get real-time 15-minute (RTC) LMPs for all zone and generator locations.
`get_lmp_real_time_5_min`(→ pandas.DataFrame)	Get real-time 5-minute (RTD) LMPs for all zone and generator locations.
`get_lmp_real_time_hourly`(→ pandas.DataFrame)	Get real-time hourly LMPs for all zone and generator locations.
`get_load`(→ pandas.DataFrame)	Returns load at a previous date in 5 minute intervals for
`get_load_forecast`(→ pandas.DataFrame)	Get load forecast for a date in 1 hour intervals
`get_loads`(→ pandas.DataFrame)	Get a list of loads in NYISO
`get_raw_interconnection_queue`(→ BinaryIO)
`get_status`(→ pandas.DataFrame)
`get_zonal_load_forecast`(→ pandas.DataFrame)	Get zonal load forecast for a date in 1 hour intervals
`get_zonal_load_hourly`(→ pandas.DataFrame)	Get hourly integrated real-time load by zone.

Pull the most recent ancillary service 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)

Pull the most recent ancillary service 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)

Returns NYISO’s daily estimate of installed behind-the-meter solar capacity by zone.

Source: https://mis.nyiso.com/public/P-70Clist.htm

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 “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: 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_capacity_prices(date: pandas.Timestamp | None = None, verbose: bool = False) → pandas.DataFrame[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)
Returns:: a DataFrame of monthly capacity prices (all three auctions) for each of the four capacity localities within NYISO

get_fuel_mix(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_generation_outages_forecast(date: str | pandas.Timestamp | Literal['latest'] = 'latest', end: str | pandas.Timestamp | None = None, verbose: bool = False) → pandas.DataFrame[source]#: Get forecasted generation outage capacity for the next 30 days.

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

Get a list of generators in NYISO. When possible return capacity and fuel type information

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
2024 CRIS MW Summer
2024 CRIS MW Winter
2024 Capability MW Summer
2024 Capability MW Winter
Is Dual Fuel
Unit Type
Fuel Type 1
Fuel Type 2
2023 Net Energy GWh
Notes
Generator Type

Return type:

pandas.DataFrame

get_interconnection_queue() → pandas.DataFrame[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_interface_limits_and_flows_5_min(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 interface limits and flows for a date

get_lake_erie_circulation_day_ahead(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_lake_erie_circulation_real_time(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_limiting_constraints_day_ahead(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_limiting_constraints_real_time(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_lmp(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, market: gridstatus.base.Markets | None = None, locations: list | None = None, location_type: NYISOLocationType | None = None, verbose: bool = False) → pandas.DataFrame[source]#: Deprecated. Use the per-dataset methods instead: get_lmp_real_time_5_min(), get_lmp_real_time_15_min(), get_lmp_real_time_hourly(), get_lmp_day_ahead_hourly().

get_lmp_day_ahead_hourly(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 day-ahead hourly LMPs for all zone and generator locations.

get_lmp_real_time_15_min(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 real-time 15-minute (RTC) LMPs for all zone and generator locations.

get_lmp_real_time_5_min(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 real-time 5-minute (RTD) LMPs for all zone and generator locations.

get_lmp_real_time_hourly(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 real-time hourly LMPs for all zone and generator locations.

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 “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: 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 load forecast for a date in 1 hour intervals

get_loads() → pandas.DataFrame[source]#

Get a list of loads in NYISO

Returns:: a DataFrame of loads and locations
Return type:: pandas.DataFrame

get_raw_interconnection_queue() → BinaryIO[source]#

get_status(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_zonal_load_forecast(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 zonal load forecast for a date in 1 hour intervals

get_zonal_load_hourly(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 hourly integrated real-time load by zone.

class gridstatus.nyiso.NYISOLocationType[source]#

Bases: enum.StrEnum

Enum where members are also (and must be) strings

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

Attributes

GENERATOR	‘generator’
ZONE	‘zone’

gridstatus.nyiso.REAL_TIME_EVENTS_DATASET = 'RealTimeEvents'[source]#

gridstatus.nyiso.REAL_TIME_HOURLY_LMP_DATASET = 'rtlbmp'[source]#

gridstatus.nyiso.REAL_TIME_LMP_DATASET = 'realtime'[source]#

gridstatus.nyiso.REFERENCE_BUS_LOCATION = 'NYISO_LBMP_REFERENCE'[source]#

gridstatus.nyiso.ZONAL_LOAD_HOURLY_DATASET = 'palIntegrated'[source]#

`AS_PRICES_DAY_AHEAD_HOURLY_DATASET`
`AS_PRICES_REAL_TIME_5_MIN_DATASET`
`BTM_INSTALLED_CAPACITY_DATASET`
`BTM_SOLAR_ACTUAL_DATASET`
`BTM_SOLAR_FORECAST_DATASET`
`DAM_LMP_DATASET`
`DATASET_INTERVAL_MAP`
`FUEL_MIX_DATASET`
`GENERATION_OUTAGES_FORECAST_URL`
`INTERFACE_LIMITS_AND_FLOWS_DATASET`
`LAKE_ERIE_CIRCULATION_DAY_AHEAD_DATASET`
`LAKE_ERIE_CIRCULATION_REAL_TIME_DATASET`
`LIMITING_CONSTRAINTS_DAY_AHEAD_DATASET`	Pricing data:
`LIMITING_CONSTRAINTS_REAL_TIME_DATASET`
`LOAD_DATASET`
`LOAD_FORECAST_DATASET`
`REAL_TIME_EVENTS_DATASET`
`REAL_TIME_HOURLY_LMP_DATASET`
`REAL_TIME_LMP_DATASET`
`REFERENCE_BUS_LOCATION`
`ZONAL_LOAD_HOURLY_DATASET`