ams.routines.dcopf.DCOPF#

class ams.routines.dcopf.DCOPF(system, config, **kwargs)[source]#

DC optimal power flow (DCOPF) using B-theta formulation.

Notes

  • The nodal price is calculated as pi in pic.

  • Devices online status of StaticGen, StaticLoad, and Shunt are considered in the connectivity matrices Cft, Cg, Cl, and Csh.

References

  1. R. D. Zimmerman, C. E. Murillo-Sanchez, and R. J. Thomas, “MATPOWER: Steady-State Operations, Planning, and Analysis Tools for Power Systems Research and Education,” IEEE Trans. Power Syst., vol. 26, no. 1, pp. 12-19, Feb. 2011

  2. Y. Chen et al., "Security-Constrained Unit Commitment for Electricity Market: Modeling, Solution Methods, and Future Challenges," in IEEE Transactions on Power Systems, vol. 38, no. 5, pp. 4668-4681, Sept. 2023

__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 results using 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 routine.

set(src, idx[, attr, value])

Set the value of an attribute of a routine parameter.

solve(**kwargs)

Solve the routine optimization model.

summary(**kwargs)

Summary interface

unpack(res, **kwargs)

Unpack the results from CVXPY model.

update([params, build_mats])

Update the values of Parameters in the optimization model.

Attributes

class_name