Nyiso ========================== .. py:module:: gridstatus.nyiso Module Contents --------------- Classes Summary ~~~~~~~~~~~~~~~ .. autoapisummary:: gridstatus.nyiso.DatasetInterval gridstatus.nyiso.NYISO gridstatus.nyiso.NYISOLocationType Attributes Summary ~~~~~~~~~~~~~~~~~~~ .. autoapisummary:: gridstatus.nyiso.AS_PRICES_DAY_AHEAD_HOURLY_DATASET gridstatus.nyiso.AS_PRICES_REAL_TIME_5_MIN_DATASET gridstatus.nyiso.BTM_SOLAR_ACTUAL_DATASET gridstatus.nyiso.BTM_SOLAR_FORECAST_DATASET gridstatus.nyiso.DAM_LMP_DATASET gridstatus.nyiso.DATASET_INTERVAL_MAP gridstatus.nyiso.FUEL_MIX_DATASET gridstatus.nyiso.INTERFACE_LIMITS_AND_FLOWS_DATASET gridstatus.nyiso.LAKE_ERIE_CIRCULATION_DAY_AHEAD_DATASET gridstatus.nyiso.LAKE_ERIE_CIRCULATION_REAL_TIME_DATASET gridstatus.nyiso.LIMITING_CONSTRAINTS_DAY_AHEAD_DATASET gridstatus.nyiso.LIMITING_CONSTRAINTS_REAL_TIME_DATASET gridstatus.nyiso.LOAD_DATASET gridstatus.nyiso.LOAD_FORECAST_DATASET gridstatus.nyiso.REAL_TIME_EVENTS_DATASET gridstatus.nyiso.REAL_TIME_HOURLY_LMP_DATASET gridstatus.nyiso.REAL_TIME_LMP_DATASET gridstatus.nyiso.REFERENCE_BUS_LOCATION Contents ~~~~~~~~~~~~~~~~~~~ .. py:data:: AS_PRICES_DAY_AHEAD_HOURLY_DATASET :value: 'damasp' .. py:data:: AS_PRICES_REAL_TIME_5_MIN_DATASET :value: 'rtasp' .. py:data:: BTM_SOLAR_ACTUAL_DATASET :value: 'btmactualforecast' .. py:data:: BTM_SOLAR_FORECAST_DATASET :value: 'btmdaforecast' .. py:data:: DAM_LMP_DATASET :value: 'damlbmp' .. py:data:: DATASET_INTERVAL_MAP :type: Dict[str, DatasetInterval] .. py:class:: DatasetInterval Bases: :py:obj:`NamedTuple` **Attributes** .. list-table:: :widths: 15 85 :header-rows: 0 * - **interval_duration_minutes** - None * - **time_type** - None .. py:data:: FUEL_MIX_DATASET :value: 'rtfuelmix' .. py:data:: INTERFACE_LIMITS_AND_FLOWS_DATASET :value: 'ExternalLimitsFlows' .. py:data:: LAKE_ERIE_CIRCULATION_DAY_AHEAD_DATASET :value: 'eriecirculationda' .. py:data:: LAKE_ERIE_CIRCULATION_REAL_TIME_DATASET :value: 'eriecirculationrt' .. py:data:: LIMITING_CONSTRAINTS_DAY_AHEAD_DATASET :value: 'DAMLimitingConstraints' Pricing data: https://www.nyiso.com/en/energy-market-operational-data .. py:data:: LIMITING_CONSTRAINTS_REAL_TIME_DATASET :value: 'LimitingConstraints' .. py:data:: LOAD_DATASET :value: 'pal' .. py:data:: LOAD_FORECAST_DATASET :value: 'isolf' .. py:class:: NYISO Bases: :py:obj:`gridstatus.base.ISOBase` New York Independent System Operator (NYISO) **Attributes** .. list-table:: :widths: 15 85 :header-rows: 0 * - **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** .. autoapisummary:: :nosignatures: gridstatus.nyiso.NYISO.get_as_prices_day_ahead_hourly gridstatus.nyiso.NYISO.get_as_prices_real_time_5_min gridstatus.nyiso.NYISO.get_btm_solar gridstatus.nyiso.NYISO.get_btm_solar_forecast gridstatus.nyiso.NYISO.get_capacity_prices gridstatus.nyiso.NYISO.get_fuel_mix gridstatus.nyiso.NYISO.get_generators gridstatus.nyiso.NYISO.get_interconnection_queue gridstatus.nyiso.NYISO.get_interface_limits_and_flows_5_min gridstatus.nyiso.NYISO.get_lake_erie_circulation_day_ahead gridstatus.nyiso.NYISO.get_lake_erie_circulation_real_time gridstatus.nyiso.NYISO.get_limiting_constraints_day_ahead gridstatus.nyiso.NYISO.get_limiting_constraints_real_time gridstatus.nyiso.NYISO.get_lmp gridstatus.nyiso.NYISO.get_load gridstatus.nyiso.NYISO.get_load_forecast gridstatus.nyiso.NYISO.get_loads gridstatus.nyiso.NYISO.get_raw_interconnection_queue gridstatus.nyiso.NYISO.get_status gridstatus.nyiso.NYISO.get_zonal_load_forecast .. py:method:: get_as_prices_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 Pull the most recent ancillary service market report's market clearing prices :param date: date that will be used to pull latest capacity report (will refer to month and year) :type date: pandas.Timestamp .. py:method:: get_as_prices_real_time_5_min(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, end: str | pandas.Timestamp | None = None, verbose: bool = False) -> pandas.DataFrame Pull the most recent ancillary service market report's market clearing prices :param date: date that will be used to pull latest capacity report (will refer to month and year) :type date: pandas.Timestamp .. py:method:: get_btm_solar(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 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. :param date: Date to get load for. Can be "today", or a date :type date: str, pd.Timestamp, datetime.datetime :param end: End date for date range. Optional. :type end: str, pd.Timestamp, datetime.datetime :param verbose: Whether to print verbose output. Optional. :type verbose: bool :returns: BTM solar data for NYISO system and each zone :rtype: pandas.DataFrame .. py:method:: 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 .. py:method:: get_capacity_prices(date: pandas.Timestamp | None = None, verbose: bool = False) -> pandas.DataFrame Pull the most recent capacity market report's market clearing prices :param date: date that will be used to pull latest capacity report (will refer to month and year) :type date: pandas.Timestamp :returns: a DataFrame of monthly capacity prices (all three auctions) for each of the four capacity localities within NYISO .. py:method:: 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 .. py:method:: get_generators(verbose: bool = False) -> pandas.DataFrame 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 :rtype: pandas.DataFrame .. py:method:: get_interconnection_queue() -> pandas.DataFrame 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 :rtype: pandas.DataFrame .. py:method:: 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 Get interface limits and flows for a date .. py:method:: 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 .. py:method:: 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 .. py:method:: 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 .. py:method:: 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 .. py:method:: 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 Supported Markets: - ``REAL_TIME_5_MIN`` (RTC) - ``REAL_TIME_15_MIN`` (RTD) - ``REAL_TIME_HOURLY`` (Real-time hourly LMP) - ``DAY_AHEAD_HOURLY`` Supported Location Types: - ``zone`` - ``generator`` NOTE: the generator data contains the single Reference Bus location type. REAL_TIME_5_MIN is the Real Time Dispatch (RTD) market. REAL_TIME_15_MIN is the Real Time Commitment (RTC) market. REAL_TIME_HOURLY is the real-time hourly LMP market. For documentation on real time dispatch and real time commitment, see: https://www.nyiso.com/documents/20142/1404816/RTC-RTD%20Convergence%20Study.pdf/f3843982-dd30-4c66-6c21-e101c3cb85af .. py:method:: get_load(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 Returns load at a previous date in 5 minute intervals for each zone and total load :param date: Date to get load for. Can be "today", or a date in the format YYYY-MM-DD :type date: str :param end: End date for date range. Optional. :type end: str :param verbose: Whether to print verbose output. Optional. :type verbose: bool :returns: Load data for NYISO and each zone :rtype: pandas.DataFrame .. py:method:: 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 Get load forecast for a date in 1 hour intervals .. py:method:: get_loads() -> pandas.DataFrame Get a list of loads in NYISO :returns: a DataFrame of loads and locations :rtype: pandas.DataFrame .. py:method:: get_raw_interconnection_queue() -> BinaryIO .. py:method:: 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 .. py:method:: 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 Get zonal load forecast for a date in 1 hour intervals .. py:class:: NYISOLocationType Bases: :py:obj:`enum.StrEnum` Enum where members are also (and must be) strings Initialize self. See help(type(self)) for accurate signature. **Attributes** .. list-table:: :widths: 15 85 :header-rows: 0 * - **GENERATOR** - 'generator' * - **ZONE** - 'zone' .. py:data:: REAL_TIME_EVENTS_DATASET :value: 'RealTimeEvents' .. py:data:: REAL_TIME_HOURLY_LMP_DATASET :value: 'rtlbmp' .. py:data:: REAL_TIME_LMP_DATASET :value: 'realtime' .. py:data:: REFERENCE_BUS_LOCATION :value: 'NYISO_LBMP_REFERENCE'