Run NYgrid

nygrid.run_nygrid.get_last_shutdown_hour(results: Dict[str, DataFrame], hour_before_start: Timestamp) → ndarray[source]

Get the hour since the last generator shutdown

Parameters:

  • results (dict) – Dictionary of results

  • hour_before_start (pd.Timestamp) – Hour before the start of the simulation

Returns:

hour_since_last_shutdown – Hour since the last generator shutdown

Return type:

np.ndarray

nygrid.run_nygrid.get_last_startup_hour(results: Dict[str, DataFrame], hour_before_start: Timestamp) → ndarray[source]

Get the hour since the last generator startup

Parameters:

  • results (dict) – Dictionary of results

  • hour_before_start (pd.Timestamp) – Hour before the start of the simulation

Returns:

hour_since_last_startup – Hour since the last generator startup

Return type:

np.ndarray

nygrid.run_nygrid.read_com_building_elec_data(data_dir: str | PathLike, upgrade_id: int, county_attrs: DataFrame) → DataFrame[source]

Commercial building energy changes due to electrification.

Parameters:

  • data_dir (str) – Directory of commercial building data

  • upgrade_id (int) – Commercial building upgrade scenario ID

  • county_attrs (pd.DataFrame) – County attributes

Returns:

com_load_change_county – Commercial building load change by county

Return type:

pd.DataFrame

nygrid.run_nygrid.read_electrification_data(electrification_dict: Dict[str, Any], county_attrs: DataFrame, county_2_bus: DataFrame) → Dict[str, Any][source]

Read electrification data for different sectors (residential building, commercial building, electric vehicle)

Parameters:

  • electrification_dict (dict) – Dictionary of electrification data Keys: ‘res_building’, ‘com_building’, ‘electric_vehicle’ In each dictionary, the following keys are required: data_dir (str), upgrade_id (int)

  • county_attrs (pd.DataFrame) – County attributes

  • county_2_bus (pd.DataFrame) – County to bus mapping

Returns:

electrification_dict – Dictionary of electrification data Keys: ‘res_building’, ‘com_building’, ‘electric_vehicle’ In each dictionary, the following keys are required: data_dir (str), upgrade_id (int), load_change (pd.DataFrame)

Return type:

dict

nygrid.run_nygrid.read_ev_elec_data(data_dir: str | PathLike, upgrade_id: int, county_attrs: DataFrame) → DataFrame[source]

Electric vehicle energy changes due to electrification.

Parameters:

  • data_dir (str) – Directory of EV data

  • upgrade_id (int) – EV upgrade scenario ID

  • county_attrs (pd.DataFrame) – County attributes

Returns:

ev_load_change_county – EV load change by county

Return type:

pd.DataFrame

nygrid.run_nygrid.read_grid_profile(data_dir: str | PathLike, year: int) → Dict[str, DataFrame][source]

Read grid profile data from csv files.

Parameters:

  • data_dir (str) – Directory of grid data

  • year (int) – Year of grid data

Returns:

grid_data – Dictionary of grid data The following keys are accepted: load_profile, gen_profile, genmax_profile, genmin_profile, genramp30_profile, gencost0_profile, gencost1_profile The dictionary values are pandas.DataFrame

Return type:

dict

nygrid.run_nygrid.read_grid_prop(grid_data_dir: str | PathLike, bus_prop_file: str = 'bus_prop.csv', gen_prop_file: str = 'gen_prop.csv', genfuel_prop_file: str = 'genfuel_prop.csv', gencost_prop_file: str = 'gencost_prop.csv', branch_prop_file: str = 'branch_prop.csv', if_lims_prop_file: str = 'if_lims_prop.csv', if_map_prop_file: str = 'if_map_prop.csv', esr_prop_file: str = 'esr_prop.csv', dcline_prop_file: str = 'dcline_prop.csv') → Dict[str, DataFrame][source]

Read grid data from csv files.

Parameters:

  • grid_data_dir (str) – Path to the grid data directory.

  • bus_prop_file (str, optional) – Bus properties file name, by default ‘bus_prop.csv’

  • gen_prop_file (str, optional) – Generator properties file name, by default ‘gen_prop.csv’

  • genfuel_prop_file (str, optional) – Generator fuel type file name, by default ‘genfuel_prop.csv’

  • gencost_prop_file (str, optional) – Generator cost properties file name, by default ‘gencost_prop.csv’

  • branch_prop_file (str, optional) – AC line properties file name, by default ‘branch_prop.csv’

  • if_lims_prop_file (str, optional) – Interface limit properties file name, by default ‘if_lims_prop.csv’

  • if_map_prop_file (str, optional) – Interface mapping file name, by default ‘if_map_prop.csv’

  • esr_prop_file (str, optional) – Energy storage properties file name, by default ‘esr_prop.csv’

  • dcline_prop_file (str, optional) – DC line properties file name, by default ‘dcline_prop.csv’

Returns:

grid_data – Dictionary of grid data.

Keys: bus_prop, gen_prop, gen_fuel, gencost_prop, branch_prop, if_lim_prop, if_map_prop, esr_prop, dcline_prop

Values: pandas.DataFrame

Return type:

dict

nygrid.run_nygrid.read_res_building_elec_data(data_dir: str | PathLike, upgrade_id: int, county_attrs: DataFrame) → DataFrame[source]

Residential building energy changes due to electrification.

Parameters:

  • data_dir (str) – Directory of residential building data

  • upgrade_id (int) – Residential building upgrade scenario ID

  • county_attrs (pd.DataFrame) – County attributes

Returns:

res_load_change_county – Residential building load change by county

Return type:

pd.DataFrame

nygrid.run_nygrid.read_vre_data(solar_data_dir: str | PathLike, onshore_wind_data_dir: str | PathLike, offshore_wind_data_dir: str | PathLike) → Tuple[DataFrame, DataFrame][source]
Parameters:

  • solar_data_dir (str) – Directory of solar data

  • onshore_wind_data_dir (str) – Directory of onshore wind data

  • offshore_wind_data_dir (str) – Directory of offshore wind data

Returns:

  • vre_prop (pandas.DataFrame) – VRE properties

  • genmax_profile_vre (pandas.DataFrame) – VRE generation profiles

nygrid.run_nygrid.run_nygrid_sim(grid_prop: Dict[str, DataFrame], grid_profile: Dict[str, DataFrame], start_datetime: Timestamp, end_datetime: Timestamp, options: Dict[str, Any], solver_options: Dict[str, Any] | None, gen_init: ndarray | None, gen_init_cmt: ndarray | None, soc_init: ndarray | None, gen_last_startup_hour: ndarray | None = None, gen_last_shutdown_hour: ndarray | None = None, verbose: bool = False) → Dict[str, DataFrame][source]

Run NYGrid simulation for one day

Parameters:

  • grid_prop (dict) –

    Dictionary of grid properties

    Keys: bus_prop, gen_prop, gen_fuel, gencost_prop, branch_prop, if_lim_prop, if_map_prop

    Values: pandas.DataFrame

  • grid_profile (dict) –

    Dictionary of grid profiles

    Keys: load_profile, gen_profile, genmax_profile, genmin_profile, genramp30_profile, gencost0_profile, gencost1_profile

    Values: pandas.DataFrame

  • start_datetime (pd.Timestamp) – Start datetime of simulation

  • end_datetime (pd.Timestamp) – End datetime of simulation

  • options (dict) – Dictionary of options

  • init_gen (np.ndarray) – Initial generator status

  • init_soc (np.ndarray) – Initial ESR SOC

  • gen_last_startup_hour (np.ndarray, optional) – Hour since the last generator startup

  • gen_last_shutdown_hour (np.ndarray, optional) – Hour since the last generator shutdown

  • verbose (bool, optional) – Print verbose output, by default

Returns:

results – Dictionary of results

Return type:

dict