deflex.Cycles

class deflex.Cycles(results, storages=True, lines=True, digits=10)

Detect all simple cycles in the directed graph.

Furthermore, get the flows of each cycle as pandas.DataFrame. For a large number of cycles getting the values may take a while so check the simple_cycles attribute first and consider setting storages and lines to False.

Cycles are a list of nodes with a flow between one node and the following node in the list and a flow from the last node of the lsit to the first node. Therefore, the number of nodes equals the number of flows.

Parameters:

• results (dict) – A valid deflex results dictionary.
• storages (bool) – Storages are always cycles and you may want to exclude them from the results setting storages=False. Nevertheless, sometimes storages are charged and discharged in one time step, which indicates a modelling problem. To detect such behaviour storages should be True. (default: True)
• lines (bool) – Transmission lines will create multiple cycles especially in models with a high number of regions and line. Setting lines to False will exclude cycles that are caused by lines. Cycles with e.g. an electrolyses in one region and a H2 power plant in another will cause a hydrogen-electricity cycle. In this cycle is a transmission line include but this cycle will not(!) be excluded if lines=False. (default: True)
• digits (int) – To detect used or critical cycles the flows are rounded to avoid a detection for very small flow values. Use digits to define the number of digits to be rounded. A high number will make the detection very sensitive. (default: 10)

name

Name of the cycle object.

Type:str

simple_cycles

A list of all cycles. Each cycle is a list of nodes.

Type:list of lists

Examples

>>> from deflex import restore_results, fetch_test_files
>>> fn = fetch_test_files("de03_fictive.dflx")
>>> c = Cycles(restore_results(fn), storages=True, lines=True)
>>> len(list(c.simple_cycles))
9
>>> c = Cycles(restore_results(fn), storages=False, lines=True)
>>> len(list(c.simple_cycles))
7
>>> c = Cycles(restore_results(fn), storages=False, lines=False)
>>> len(list(c.simple_cycles))
2

__init__(results, storages=True, lines=True, digits=10)

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(results[, storages, lines, digits])	Initialize self.
details()	Print out a more detailed overview over the existing cycles.
get_suspicious_time_steps()	Detect the time steps of a cycle in which all flows are non-zero.
print()	Print an overview of the cycles.

Attributes

cycles	Get all cycles of the model.
suspicious_cycles	Get all cycles from a list of cycles that are suspicious.
used_cycles	Get all cycles from a list of cycles that are used.