edisgo.network.dsm

Classes

DSM

Data container for demand side management potential data.

Module Contents

class edisgo.network.dsm.DSM(**kwargs)[source]

Data container for demand side management potential data.

property p_min

Maximum load decrease in MW.

Parameters:

df (pandas.DataFrame) – Maximum load decrease in MW. Index of the dataframe is a time index and column names are names of DSM loads as in loads_df.

Returns:

Maximum load decrease in MW. For more information on the dataframe see input parameter df.

Return type:

pandas.DataFrame

property p_max

Maximum load increase in MW.

Parameters:

df (pandas.DataFrame) – Maximum load increase in MW. Index of the dataframe is a time index and column names are names of DSM loads as in loads_df.

Returns:

Maximum load decrease in MW. For more information on the dataframe see input parameter df.

Return type:

pandas.DataFrame

property e_min

Maximum energy preponing in MWh.

Parameters:

df (pandas.DataFrame) – Maximum energy preponing in MWh. Index of the dataframe is a time index and column names are names of DSM loads as in loads_df.

Returns:

Maximum energy preponing in MWh. For more information on the dataframe see input parameter df.

Return type:

pandas.DataFrame

property e_max

Maximum energy postponing in MWh.

Parameters:

df (pandas.DataFrame) – Maximum energy postponing in MWh. Index of the dataframe is a time index and column names are names of DSM loads as in loads_df.

Returns:

Maximum energy postponing in MWh. For more information on the dataframe see input parameter df.

Return type:

pandas.DataFrame

reduce_memory(attr_to_reduce=None, to_type='float32')[source]

Reduces size of dataframes to save memory.

See reduce_memory for more information.

Parameters:

  • attr_to_reduce (list(str), optional) – List of attributes to reduce size for. Per default, all active and reactive power time series of generators, loads, and storage units are reduced.

  • to_type (str, optional) – Data type to convert time series data to. This is a tradeoff between precision and memory. Default: “float32”.

to_csv(directory: str | pathlib.Path, reduce_memory=False, **kwargs)[source]

Exports DSM data to csv files.

The following attributes are exported:

  • ‘p_min’ : Attribute p_min is saved to p_min.csv.

  • ‘p_max’ : Attribute p_max is saved to p_max.csv.

  • ‘e_min’ : Attribute e_min is saved to e_min.csv.

  • ‘e_max’ : Attribute e_max is saved to e_max.csv.

Parameters:

  • directory (str) – Path to save DSM data to.

  • reduce_memory (bool, optional) – If True, size of dataframes is reduced using reduce_memory. Optional parameters of reduce_memory can be passed as kwargs to this function. Default: False.

  • kwargs – Kwargs may contain arguments of reduce_memory.

from_csv(data_path: str | pathlib.Path, from_zip_archive: bool = False)[source]

Restores DSM data from csv files.

Parameters:

  • data_path (str) – Path to DSM csv files or zip archive.

  • from_zip_archive (bool) – Set to True if data is archived in a zip archive. Default: False.

check_integrity()[source]

Check data integrity.

Checks for duplicated and missing labels as well as implausible values.