Miso Api ============================= .. py:module:: gridstatus.miso_api Module Contents --------------- Classes Summary ~~~~~~~~~~~~~~~ .. autoapisummary:: gridstatus.miso_api.MISOAPI Attributes Summary ~~~~~~~~~~~~~~~~~~~ .. autoapisummary:: gridstatus.miso_api.BASE_LOAD_GENERATION_AND_INTERCHANGE_URL gridstatus.miso_api.BASE_PRICING_URL gridstatus.miso_api.CERTIFICATES_CHAIN_FILE gridstatus.miso_api.CURRENT_DIR gridstatus.miso_api.DAILY_RESOLUTION gridstatus.miso_api.EX_ANTE gridstatus.miso_api.EX_POST gridstatus.miso_api.FINAL_STRING gridstatus.miso_api.FIVE_MINUTE_RESOLUTION gridstatus.miso_api.HOURLY_RESOLUTION gridstatus.miso_api.LOAD_GENERATION_AND_INTERCHANGE_PRODUCT gridstatus.miso_api.logger gridstatus.miso_api.PRELIMINARY_STRING gridstatus.miso_api.PRICING_PRODUCT Contents ~~~~~~~~~~~~~~~~~~~ .. py:data:: BASE_LOAD_GENERATION_AND_INTERCHANGE_URL :value: 'https://apim.misoenergy.org/lgi/v1' .. py:data:: BASE_PRICING_URL :value: 'https://apim.misoenergy.org/pricing/v1' .. py:data:: CERTIFICATES_CHAIN_FILE .. py:data:: CURRENT_DIR .. py:data:: DAILY_RESOLUTION :value: 'daily' .. py:data:: EX_ANTE :value: 'exante' .. py:data:: EX_POST :value: 'expost' .. py:data:: FINAL_STRING :value: 'Final' .. py:data:: FIVE_MINUTE_RESOLUTION :value: '5min' .. py:data:: HOURLY_RESOLUTION :value: 'hourly' .. py:data:: LOAD_GENERATION_AND_INTERCHANGE_PRODUCT :value: 'load_generation_and_interchange' .. py:data:: logger .. py:class:: MISOAPI(pricing_api_key: str | None = None, load_generation_and_interchange_api_key: str | None = None, initial_sleep_seconds: int = 1, max_retries: int = 3, exponential_base: int = 2) Class for querying the MISO API. Currently supports only pricing data. :param pricing_api_key: The API key for the pricing API. Can be a comma-separated list of keys if you have multiple keys. :param load_generation_and_interchange_api_key: The API key for the load, generation, and interchange API. Can be a comma-separated list of keys if you have multiple keys. :param initial_sleep_seconds: The number of seconds to wait between each request. Used to address rate limiting (429 responses). :param max_retries: The maximum number of retries for failed requests. Uses exponential backoff between retries. Used to address the common 503 errors from the MISO API. :param exponential_base: The base for exponential backoff calculation. Sleep time is exponential_base^(attempt+1) seconds. Default is 2, which gives delays of 2, 4, 8 seconds for attempts 0, 1, 2. **Attributes** .. list-table:: :widths: 15 85 :header-rows: 0 * - **current_load_generation_and_interchange_key_index** - 0 * - **current_pricing_key_index** - 0 * - **default_timezone** - 'EST' * - **exponential_base** - 2 * - **initial_sleep_seconds** - 1 * - **load_generation_and_interchange_api_key** - None * - **load_generation_and_interchange_api_keys** - None * - **max_retries** - 3 * - **pricing_api_key** - None * - **pricing_api_keys** - None **Methods** .. autoapisummary:: :nosignatures: gridstatus.miso_api.MISOAPI.get_actual_load_daily gridstatus.miso_api.MISOAPI.get_actual_load_hourly gridstatus.miso_api.MISOAPI.get_actual_load_hourly_pivoted gridstatus.miso_api.MISOAPI.get_as_mcp_day_ahead_ex_ante gridstatus.miso_api.MISOAPI.get_as_mcp_day_ahead_ex_post gridstatus.miso_api.MISOAPI.get_as_mcp_real_time_5_min_ex_ante gridstatus.miso_api.MISOAPI.get_as_mcp_real_time_5_min_ex_post_final gridstatus.miso_api.MISOAPI.get_as_mcp_real_time_5_min_ex_post_prelim gridstatus.miso_api.MISOAPI.get_as_mcp_real_time_hourly_ex_post_final gridstatus.miso_api.MISOAPI.get_as_mcp_real_time_hourly_ex_post_prelim gridstatus.miso_api.MISOAPI.get_day_ahead_cleared_demand_daily gridstatus.miso_api.MISOAPI.get_day_ahead_cleared_demand_hourly gridstatus.miso_api.MISOAPI.get_day_ahead_cleared_generation_physical_hourly gridstatus.miso_api.MISOAPI.get_day_ahead_cleared_generation_virtual_hourly gridstatus.miso_api.MISOAPI.get_day_ahead_generation_fuel_type_hourly gridstatus.miso_api.MISOAPI.get_day_ahead_net_scheduled_interchange_hourly gridstatus.miso_api.MISOAPI.get_day_ahead_offered_generation_ecomax_hourly gridstatus.miso_api.MISOAPI.get_day_ahead_offered_generation_ecomin_hourly gridstatus.miso_api.MISOAPI.get_interchange_hourly gridstatus.miso_api.MISOAPI.get_lmp_day_ahead_hourly_ex_ante gridstatus.miso_api.MISOAPI.get_lmp_day_ahead_hourly_ex_post gridstatus.miso_api.MISOAPI.get_lmp_real_time_5_min_ex_ante gridstatus.miso_api.MISOAPI.get_lmp_real_time_5_min_ex_post_final gridstatus.miso_api.MISOAPI.get_lmp_real_time_5_min_ex_post_prelim gridstatus.miso_api.MISOAPI.get_lmp_real_time_hourly_ex_post_final gridstatus.miso_api.MISOAPI.get_lmp_real_time_hourly_ex_post_prelim gridstatus.miso_api.MISOAPI.get_look_ahead_hourly gridstatus.miso_api.MISOAPI.get_medium_term_load_forecast_daily gridstatus.miso_api.MISOAPI.get_medium_term_load_forecast_hourly gridstatus.miso_api.MISOAPI.get_medium_term_load_forecast_hourly_aggregated gridstatus.miso_api.MISOAPI.get_outage_forecast gridstatus.miso_api.MISOAPI.get_pricing_nodes gridstatus.miso_api.MISOAPI.get_real_time_cleared_demand_daily gridstatus.miso_api.MISOAPI.get_real_time_cleared_demand_hourly gridstatus.miso_api.MISOAPI.get_real_time_cleared_generation_hourly gridstatus.miso_api.MISOAPI.get_real_time_committed_generation_ecomax_hourly gridstatus.miso_api.MISOAPI.get_real_time_generation_fuel_type_hourly gridstatus.miso_api.MISOAPI.get_real_time_offered_generation_ecomax_hourly .. py:method:: get_actual_load_daily(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, verbose: bool = False, geo_resolution: Literal['region', 'localResourceZone'] = 'region') -> pandas.DataFrame .. py:method:: get_actual_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, geo_resolution: Literal['region', 'localResourceZone'] = 'region') -> pandas.DataFrame .. py:method:: get_actual_load_hourly_pivoted(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 actual load by local resource zone in hourly intervals, pivoted with zones as columns. Returns columns: Interval Start, Interval End, LRZ1, LRZ2 7, LRZ3 5, LRZ4, LRZ6, LRZ8 9 10, MISO .. py:method:: get_as_mcp_day_ahead_ex_ante(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, verbose: bool = False, use_daily_requests: bool = False) -> pandas.DataFrame .. py:method:: get_as_mcp_day_ahead_ex_post(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, verbose: bool = False, use_daily_requests: bool = False) -> pandas.DataFrame .. py:method:: get_as_mcp_real_time_5_min_ex_ante(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, verbose: bool = False, use_daily_requests: bool = False) -> pandas.DataFrame .. py:method:: get_as_mcp_real_time_5_min_ex_post_final(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, verbose: bool = False, use_daily_requests: bool = False) -> pandas.DataFrame .. py:method:: get_as_mcp_real_time_5_min_ex_post_prelim(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, verbose: bool = False, use_daily_requests: bool = False) -> pandas.DataFrame .. py:method:: get_as_mcp_real_time_hourly_ex_post_final(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, verbose: bool = False, use_daily_requests: bool = False) -> pandas.DataFrame .. py:method:: get_as_mcp_real_time_hourly_ex_post_prelim(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, verbose: bool = False, use_daily_requests: bool = False) -> pandas.DataFrame .. py:method:: get_day_ahead_cleared_demand_daily(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_day_ahead_cleared_demand_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 .. py:method:: get_day_ahead_cleared_generation_physical_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 .. py:method:: get_day_ahead_cleared_generation_virtual_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 .. py:method:: get_day_ahead_generation_fuel_type_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 .. py:method:: get_day_ahead_net_scheduled_interchange_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 .. py:method:: get_day_ahead_offered_generation_ecomax_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 .. py:method:: get_day_ahead_offered_generation_ecomin_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 .. py:method:: get_interchange_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 .. py:method:: get_lmp_day_ahead_hourly_ex_ante(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, verbose: bool = False, **kwargs) -> pandas.DataFrame .. py:method:: get_lmp_day_ahead_hourly_ex_post(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, verbose: bool = False, **kwargs) -> pandas.DataFrame .. py:method:: get_lmp_real_time_5_min_ex_ante(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, verbose: bool = False, **kwargs) -> pandas.DataFrame .. py:method:: get_lmp_real_time_5_min_ex_post_final(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, verbose: bool = False, **kwargs) -> pandas.DataFrame .. py:method:: get_lmp_real_time_5_min_ex_post_prelim(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, verbose: bool = False, **kwargs) -> pandas.DataFrame .. py:method:: get_lmp_real_time_hourly_ex_post_final(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, verbose: bool = False, **kwargs) -> pandas.DataFrame .. py:method:: get_lmp_real_time_hourly_ex_post_prelim(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, verbose: bool = False, **kwargs) -> pandas.DataFrame .. py:method:: get_look_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, publish_time: str | pandas.Timestamp | None = None) -> pandas.DataFrame Get look-ahead hourly data combining medium-term load forecast and outage forecast. Look-ahead data is only available for future dates (today and beyond). Historical look-ahead data is not supported. Returns DataFrame with columns: Interval Start, Interval End, Publish Time, Region, MTLF, Outage This matches the output of MISO().get_look_ahead_hourly(). .. py:method:: get_medium_term_load_forecast_daily(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, verbose: bool = False, publish_time: str | pandas.Timestamp | None = None) -> pandas.DataFrame .. py:method:: get_medium_term_load_forecast_hourly(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, verbose: bool = False, publish_time: str | pandas.Timestamp | None = None) -> pandas.DataFrame .. py:method:: get_medium_term_load_forecast_hourly_aggregated(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, verbose: bool = False, publish_time: str | pandas.Timestamp | None = None) -> pandas.DataFrame Get medium term load forecast aggregated from individual zones (Z1-Z10) to LRZ aggregates and pivoted with zones as columns. Returns columns: Interval Start, Interval End, Publish Time, LRZ1 MTLF, LRZ2_7 MTLF, LRZ3_5 MTLF, LRZ4 MTLF, LRZ6 MTLF, LRZ8_9_10 MTLF, MISO MTLF .. py:method:: get_outage_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 hourly outage forecast. The API only returns hourly data for today and future days. Historical outage forecast data is not supported. Note: Outage forecast is only available for future dates (today and beyond). .. py:method:: get_pricing_nodes(date: str | pandas.Timestamp | None = 'latest', end: pandas.Timestamp | None = None, verbose: bool = False) -> pandas.DataFrame Retrieve MISO pricing nodes for a specific date or date range. MISO pricing nodes change quarterly on March 1st, June 1st, September 1st, and December 1st. New pricing nodes become effective on these dates, some pricing nodes are retired/removed and some nodes change names/node ids. :param date: The date for which to retrieve pricing nodes. If None, defaults to "latest". Can be a pd.Timestamp or "latest". :param end: Optional end date for a date range. If provided, retrieves pricing nodes for all quarterly updates between date and end. :param verbose: If True, prints additional information during data retrieval. .. py:method:: get_real_time_cleared_demand_daily(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_real_time_cleared_demand_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 .. py:method:: get_real_time_cleared_generation_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 :abstractmethod: NOTE: This function is not ready for use yet. MISO Real-Time Cleared Generation API returns wrong timestamp. The timestamps are off by 5 hours, seems to be a timezone issue, UTC instead of EST. .. py:method:: get_real_time_committed_generation_ecomax_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 .. py:method:: get_real_time_generation_fuel_type_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 .. py:method:: get_real_time_offered_generation_ecomax_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 .. py:data:: PRELIMINARY_STRING :value: 'Preliminary' .. py:data:: PRICING_PRODUCT :value: 'pricing'