flixOpt.aggregation

This module contains the Aggregation functionality for the flixOpt framework. Through this, aggregating TimeSeriesData is possible.

Classes

Aggregation

Aggregation(original_data: DataFrame, hours_per_time_step: Skalar, hours_per_period: Skalar, nr_of_periods: int = 8, weights: Dict[str, float] = None, time_series_for_high_peaks: List[str] = None, time_series_for_low_peaks: List[str] = None)

aggregation organizing class

Write a docstring please

Parameters:

Name	Type	Description	Default
`timeseries`		timeseries of the data with a datetime index	required

Functions

cluster

cluster() -> None

Durchführung der Zeitreihenaggregation

get_cluster_indices

get_cluster_indices() -> Dict[str, List[np.ndarray]]

Generates a dictionary that maps each cluster to a list of index vectors representing the time steps assigned to that cluster for each period.

Returns: dict: {cluster_0: [index_vector_3, index_vector_7, ...], cluster_1: [index_vector_1], ...}

get_equation_indices

get_equation_indices(skip_first_index_of_period: bool = True) -> Tuple[np.ndarray, np.ndarray]

Generates pairs of indices for the equations by comparing index vectors of the same cluster. If skip_first_index_of_period is True, the first index of each period is skipped.

Args: skip_first_index_of_period (bool): Whether to include or skip the first index of each period.

Returns: Tuple[np.ndarray, np.ndarray]: Two arrays of indices.

TimeSeriesCollection

TimeSeriesCollection(time_series_list: List[TimeSeries])

AggregationParameters

AggregationParameters(hours_per_period: float, nr_of_periods: int, fix_storage_flows: bool, aggregate_data_and_fix_non_binary_vars: bool, percentage_of_period_freedom: float = 0, penalty_of_period_freedom: float = 0, time_series_for_high_peaks: List[TimeSeriesData] = None, time_series_for_low_peaks: List[TimeSeriesData] = None)

Initializes aggregation parameters for time series data

Parameters:

Name	Type	Description	Default
`hours_per_period`	`float`	Duration of each period in hours.	required
`nr_of_periods`	`int`	Number of typical periods to use in the aggregation.	required
`fix_storage_flows`	`bool`	Whether to aggregate storage flows (load/unload); if other flows are fixed, fixing storage flows is usually not required.	required
`aggregate_data_and_fix_non_binary_vars`	`bool`	Whether to aggregate all time series data, which allows to fix all time series variables (like flow_rate), or only fix binary variables. If False non time_series data is changed!! If True, the mathematical Problem is simplified even further.	required
`percentage_of_period_freedom`	`float`	Specifies the maximum percentage (0–100) of binary values within each period that can deviate as "free variables", chosen by the solver (default is 0). This allows binary variables to be 'partly equated' between aggregated periods.	`0`
`penalty_of_period_freedom`	`float`	The penalty associated with each "free variable"; defaults to 0. Added to Penalty	`0`
`time_series_for_high_peaks`	`list of TimeSeriesData`	List of time series to use for explicitly selecting periods with high values.	`None`
`time_series_for_low_peaks`	`list of TimeSeriesData`	List of time series to use for explicitly selecting periods with low values.	`None`

Functions

AggregationModel

AggregationModel(aggregation_parameters: AggregationParameters, flow_system: FlowSystem, aggregation_data: Aggregation, components_to_clusterize: Optional[List[Component]])

Bases: ElementModel

The AggregationModel holds equations and variables related to the Aggregation of a FLowSystem. It creates Equations that equates indices of variables, and introduces penalties related to binary variables, that escape the equation to their related binaries in other periods

Modeling-Element for "index-equating"-equations