pypower.ipoptopf

Module ipoptopf_solver

Solves AC optimal power flow using IPOPT.

Functions

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ipoptopf_solver(om, ppopt)
Solves AC optimal power flow using IPOPT.

source code

eval_f(x, user_data=None)
Calculates the objective value.

source code

eval_grad_f(x, user_data=None)
Calculates gradient for objective function.

source code

eval_g(x, user_data=None)
Calculates the constraint values and returns an array.

source code

eval_jac_g(x, flag, user_data=None)
Calculates the Jacobi matrix.

source code

eval_h(x, lagrange, obj_factor, flag, user_data=None)
Calculates the Hessian matrix (optional).

source code

Variables

[hide private]

__package__ = 'pypower'

Function Details

[hide private]

ipoptopf_solver(om, ppopt)

source code

Solves AC optimal power flow using IPOPT.

Inputs are an OPF model object and a PYPOWER options vector.

Outputs are a results dict, success flag and raw output dict.

results is a PYPOWER case dict (ppc) with the usual baseMVA, bus branch, gen, gencost fields, along with the following additional fields:

order see 'help ext2int' for details of this field
x final value of optimization variables (internal order)
f final objective function value
mu shadow prices on ...
- var
  - l lower bounds on variables
  - u upper bounds on variables
- nln
  - l lower bounds on nonlinear constraints
  - u upper bounds on nonlinear constraints
- lin
  - l lower bounds on linear constraints
  - u upper bounds on linear constraints

success is True if solver converged successfully, False otherwise

raw is a raw output dict in form returned by MINOS

xr final value of optimization variables
pimul constraint multipliers
info solver specific termination code
output solver specific output information

See Also: opf, pips

Authors:: Ray Zimmerman (PSERC Cornell), Carlos E. Murillo-Sanchez (PSERC Cornell & Universidad Autonoma de Manizales), Richard Lincoln

eval_f(x, user_data=None)

source code

Calculates the objective value.

Parameters:

x - input vector

eval_jac_g(x, flag, user_data=None)

source code

Calculates the Jacobi matrix.

If the flag is true, returns a tuple (row, col) to indicate the sparse Jacobi matrix's structure. If the flag is false, returns the values of the Jacobi matrix with length nnzj.

eval_h(x, lagrange, obj_factor, flag, user_data=None)

source code

Calculates the Hessian matrix (optional).

If omitted, set nnzh to 0 and Ipopt will use approximated Hessian which will make the convergence slower.