edisgo.network.timeseries.TimeSeriesRaw

class edisgo.network.timeseries.TimeSeriesRaw

Holds raw time series data, e.g. sector-specific demand and standing times of EV.

Normalised time series are e.g. sector-specific demand time series or technology-specific feed-in time series. Time series needed for flexibilities are e.g. heat time series or curtailment time series.

q_control

Dataframe with information on applied reactive power control or in case of conventional loads assumed reactive power behavior. Index of the dataframe are the component names as in index of generators_df, loads_df, and storage_units_df. Columns are “type” with the type of Q-control applied (can be “fixed_cosphi”, “cosphi(P)”, or “Q(V)”), “power_factor” with the (maximum) power factor, “q_sign” giving the sign of the reactive power (only applicable to “fixed_cosphi”), “parametrisation” with the parametrisation of the respective Q-control (only applicable to “cosphi(P)” and “Q(V)”).

Type:

pandas.DataFrame

fluctuating_generators_active_power_by_technology

DataFrame with feed-in time series per technology or technology and weather cell ID normalized to a nominal capacity of 1. Columns can either just contain the technology type as string or be a pandas.MultiIndex with the first level containing the technology as string and the second level the weather cell ID as integer. Index is a pandas.DatetimeIndex.

Type:

pandas.DataFrame

dispatchable_generators_active_power_by_technology

DataFrame with feed-in time series per technology normalized to a nominal capacity of 1. Columns contain the technology type as string. Index is a pandas.DatetimeIndex.

Type:

pandas.DataFrame

conventional_loads_active_power_by_sector

DataFrame with load time series of each type of conventional load normalized to an annual consumption of 1. Index needs to be a pandas.DatetimeIndex. Columns represent load type. In ding0 grids the differentiated sectors are ‘residential’, ‘cts’, ‘industrial’, and ‘agricultural’.

Type:

pandas.DataFrame

charging_points_active_power_by_use_case

DataFrame with charging demand time series per use case normalized to a nominal capacity of 1. Columns contain the use case as string. Index is a pandas.DatetimeIndex.

Type:

pandas.DataFrame

q_control

fluctuating_generators_active_power_by_technology

dispatchable_generators_active_power_by_technology

conventional_loads_active_power_by_sector

charging_points_active_power_by_use_case

reduce_memory(attr_to_reduce=None, to_type='float32')

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. Attributes need to be dataframes containing only time series. Per default the following attributes are reduced if they exist: q_control, fluctuating_generators_active_power_by_technology, dispatchable_generators_active_power_by_technology, conventional_loads_active_power_by_sector, charging_points_active_power_by_use_case.

• 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, reduce_memory=False, **kwargs)

Saves time series to csv.

Saves all attributes that are set to csv files with the same file name. See class definition for possible attributes.

Parameters:

• directory (str) – Directory to save time series in.

• 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 optional arguments of reduce_memory.

from_csv(directory)

Restores time series from csv files.

See to_csv() for more information on which time series are saved.

Parameters:

directory (str) – Directory time series are saved in.