casadi-ipopt-nlp

To solve nonlinear optimization problems for power systems using CasADi and IPOPT, define symbolic variables, add nonlinear constraints, configure solver options, handle multiple initializations, and extract per-unit scaled AC power-flow solutions.

Per-unit scaling in AC power-flow optimization maps physical grid quantities to solver variables using complex-number formulations. It is needed to normalize system values, ensuring numerical stability when the solver calculates optimal operating points.

Yes, you can extract generator setpoints and voltage magnitudes from the NLP solver output. The solution maps variables to per-bus quantities, outputting voltage magnitude (Vm), voltage angle (Va), active generation (Pg), and reactive generation (Qg).

You can configure IPOPT solver options and handle multiple initializations by setting parameters within the symbolic modeling environment. This approach applies robust initialization strategies across multiple startup points for grid contingency analysis.

Yes, this nonlinear optimization toolkit supports complex-number formulations for AC power systems. It incorporates power-system patterns that map physical quantities to solver variables, enabling accurate per-unit scaled modeling.