Vectorial dynamic optimal power flow calculation including wind farms based on step-controlled primal-dual interior point method

A vectorial implementation of dynamic optimal power flow (DOPF) including wind farms was presented. The vectorization of DOPF was established by arranging the control variables and state variables according to the variable types and time intervals. The asynchronous generators in wind farms were modeled in Q-V formulation. A step-controlled primal-dual interior point framework (SCIPM) with upper and lower inequality constrains was adopted to solve this DOPF model. The gradient and Hessian matrices of each time interval had relative non-zeros position with the admittance matrix, which was constant during iterations. Hence a sparse data structure and memory allocation strategy was utilized to accelerate the construction of KKT system. The effect of ramping rates and generation contract constrains on solving KKT system was analyzed in detail. Through computation statistics, it is confirmed that approximate minimum degree (AMD) reordering algorithm is most efficient with only ramping rate constrains, and column approximate minimum degree (COLAMD) reordering algorithm is most efficient with both ramping rate and generation contract constrains. Numerical simulations on test systems ranging in size from 14 to 1040 buses over 12~96 time intervals validate the correctness and efficiency of the proposed method. Vectorization technique with step-controlled primal-dual interior point method improves the calculation speed and convergence performance of DOPF.