Time-series power flow#

gpf.TIME_SERIES_SCHEMA: str#
class OperatingPointProfile(*, region='main', element_class, element, active_multiplier=None, reactive_multiplier=None)#

Apply active and reactive multiplier series to one source device.

class OperatingPointSchedule(*, time, profiles=())#

Declare one ordered time axis and its device profiles.

class CompiledTimeSeries(system, schedule)#

Store one prepared system and schedule for repeated solves.

class TimeSeriesStep(time, converged, iterations, solution)#

Store one completed time coordinate and its optional full solution.

class TimeSeriesResult(steps)#

Store ordered time-series outcomes.

property converged#

Return whether every completed step converged.

compile_time_series(source, schedule, **compile_options)#

Compile one power-flow system and resolve its ordered schedule.

Parameters:

  • source -- Source model or compiled power-flow system.

  • schedule -- Validated operating-point schedule.

  • compile_options -- Options for source-system compilation.

Returns:

One prepared system and operating-point schedule.

load_operating_point_schedule(path)#

Load one strict operating-point schedule from TOML.

Parameters:

path -- TOML schedule path.

Returns:

Validated schedule with one common time axis.

Raises:

  • ValueError -- The schema identifier is missing or unsupported.

  • pydantic.ValidationError -- The schedule fields are invalid.

run_time_series(compiled, *, backend=None, options=None, retain='all', stop_on_failure=True)#

Run one prepared schedule through a power-flow session.

Parameters:

  • compiled -- Compiled system and resolved operating-point schedule.

  • backend -- Optional CPU or CUDA backend name.

  • options -- Common system solve options for every step.

  • retain -- Full result retention mode.

  • stop_on_failure -- Stop after the first failed operating point.

Returns:

Ordered status and optional full solutions.