ams.routines.grbopt.OPF#
- class ams.routines.grbopt.OPF(system, config, **kwargs)[source]#
Optimal Power Flow (OPF) routine using gurobi-optimods.
This class provides an interface for performing optimal power flow analysis with gurobi-optimods, supporting both AC and DC OPF formulations.
In addition to optimizing generator dispatch, this routine can also optimize transmission line statuses (branch switching), enabling topology optimization. Refer to the gurobi-optimods documentation for further details:
https://gurobi-optimods.readthedocs.io/en/stable/mods/opf/opf.html
Added in version 1.0.10.
- __init__(system, config, **kwargs)[source]#
Initialize the routine.
- Parameters:
- systemOptional[Type]
The system object associated with the routine.
- configOptional[dict]
Configuration dictionary for the routine.
Methods
addConstrs(name, e_str[, info])Add a Constraint to the routine at runtime.
addRParam(name[, tex_name, info, src, unit, ...])Add RParam to the routine.
addService(name, value[, tex_name, unit, ...])Add ValueService to the routine.
addVars(name[, model, shape, tex_name, ...])Add a variable to the routine.
dc2ac([kloss])Convert the DC-based results with ACOPF.
disable(name)Disable a constraint by name.
doc([max_width, export])Retrieve routine documentation as a string.
enable(name)Enable a constraint by name.
export_csv([path])Export scheduling results to a csv file.
export_json([path])Export scheduling results to a json file.
formulation_summary([return_rows])Print (or return) a per-item table of the live formulation source.
get(src, idx[, attr, horizon])Get the value of a variable or parameter.
init(**kwargs)Initialize the routine.
load_json(path)Load scheduling results from a json file.
run(**kwargs)Run the OPF routine using gurobi-optimods.
set(src, idx[, attr, value])Set the value of an attribute of a routine parameter.
solve(**kwargs)Solve by PYPOWER.
summary(**kwargs)Summary interface
unpack(res, **kwargs)Unpack the results from the gurobi-optimods.
update([params, build_mats])Update the values of Parameters in the optimization model.
Attributes