pycity_scheduling.classes.grids package

Submodules

pycity_scheduling.classes.grids.district_heating_grid module

class pycity_scheduling.classes.grids.district_heating_grid.TwoPipeDHGrid(e_dh: Environment, *args, **kwargs)

Bases: EntityContainer

Methods

get_lower_entities()

Yield all lower-level entities.

populate_model([mode])

Add entity block and lower entities blocks to pyomo ConcreteModel.

update_model()

Update block parameters and bounds.

add_edge

create_endpoint

nodes

volume
Parameters:

e_dh (Environment) –

add_edge(node_a, node_b, line)
create_endpoint(e: Environment, location=None, ts=None)
Parameters:

e (Environment) –

get_lower_entities()

Yield all lower-level entities.

Yields:

All contained entities.

nodes()
populate_model(mode='power_flow')

Add entity block and lower entities blocks to pyomo ConcreteModel.

Call both parent’s populate_model methods and set variables lower bounds to None. Then call populate_model method of all contained entities and add constraints that the sum of their variables for each period equals the corresponding own variable.

Parameters:

  • model (pyomo.ConcreteModel) –

  • mode (str, optional) –

    Specifies which set of constraints to use.

    • convex : Use linear constraints

    • integer : Use same constraints as convex mode

update_model()

Update block parameters and bounds.

Set parameters and bounds according to the current situation of the device according to the previous schedule and the current forecasts.

Parameters:

mode (str, optional) –

Specifies which set of constraints to use.

  • convex : Use linear constraints

  • integer : Use integer variables representing discrete control decisions

volume()

pycity_scheduling.classes.grids.electrical_grid module

class pycity_scheduling.classes.grids.electrical_grid.ElectricalGrid(e_e: Environment, subs: dict = {}, ts: TimeSampler | None = None)

Bases: Grid

Methods

populate_model([mode])

Add entity block to pyomo ConcreteModel.

add_edge

consensus_variables

create_endpoint

exchange_variables

nodes
Parameters:
add_edge(node_a, node_b, resistance=0, inductance=0)
consensus_variables(m)
create_endpoint(e: Environment, location=None, ts=None)
Parameters:

e (Environment) –

exchange_variables(m)
nodes()
populate_model(mode='energy')

Add entity block to pyomo ConcreteModel.

Places the block with the name of the entity in the ConcreteModel.

Parameters:

  • model (pyomo.ConcreteModel) –

  • mode (str, optional) –

    Specifies which set of constraints to use.

    • convex : Use linear constraints

    • integer : May use integer variables

pycity_scheduling.classes.grids.endpoint module

class pycity_scheduling.classes.grids.endpoint.Endpoint(environment, location=None, *args, **kwargs)

Bases: Node

pycity_scheduling.classes.grids.fluid_grid module

class pycity_scheduling.classes.grids.fluid_grid.FluidGrid(environment, subs: dict = {}, ts: TimeSampler | None = None)

Bases: Grid

Methods

populate_model(model[, mode])

Add entity block to pyomo ConcreteModel.

add_edge

consensus_variables

exchange_variables

volume
Parameters:
add_edge(node_a, node_b, length, diameter)
consensus_variables(m)
exchange_variables(m)
populate_model(model, mode='flow')

Add entity block to pyomo ConcreteModel.

Places the block with the name of the entity in the ConcreteModel.

Parameters:

  • model (pyomo.ConcreteModel) –

  • mode (str, optional) –

    Specifies which set of constraints to use.

    • convex : Use linear constraints

    • integer : May use integer variables

volume()

pycity_scheduling.classes.grids.grid module

class pycity_scheduling.classes.grids.grid.Grid(environment, subs: dict = {}, ts: TimeSampler | None = None)

Bases: OptimizationEntity

Attributes:
resource
Parameters:

Methods

add_edge

add_endpoint

consensus_variables

exchange_variables
add_edge(node_a, node_b, line)
add_endpoint()
consensus_variables(m)
exchange_variables(m)
property resource

pycity_scheduling.classes.grids.junction module

class pycity_scheduling.classes.grids.junction.Junction(environment, location, *args, **kwargs)

Bases: Node

Methods

efficiency
efficiency()

pycity_scheduling.classes.grids.node module

class pycity_scheduling.classes.grids.node.Node(environment, location, *args, **kwargs)

Bases: OptimizationEntity

pycity_scheduling.classes.grids.valve module

Module contents

The pycity_scheduling framework

Copyright (C) 2022, Institute for Automation of Complex Power Systems (ACS), E.ON Energy Research Center (E.ON ERC), RWTH Aachen University

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