Parallel primal‐dual interior point method for the solution of dynamic optimal power flow - Rylee Sundermann; Shrirang G. Abhyankar; Hong Zhang; Jung‐Han Kimn; Timothy M. Hansen (IET Generation, Transmission & Distribution, 2023)
Abstract This work presents a novel solution for accelerating the dynamic optimal power flow using a distributed‐memory parallelization approach. Unlike other two‐stage relaxation‐based approaches (such as ADMM), the proposed approach constructs the entire dynamic optimal power flow problem in parallel and solves it using a parallel primal‐dual interior point method with an iterative Krylov subspace linear solver with a block‐Jacobi preconditioning scheme. The parallel primal‐dual interior point method has been implemented in the open‐source portable, extensible toolkit for scientific computation (PETSc) library. The formulation, implementation, and numerical results on multicore computers to demonstrate the performance of the proposed approach on medium‐ to large‐scale networks with varying time horizons are presented. The results show that a significant speedup is achieved by using a block‐Jacobi preconditioner with an iterative Krylov subspace method for solving the dynamic optimal power flow problems.