Document Type
Thesis - Open Access
Award Date
2022
Degree Name
Master of Science (MS)
Department / School
Mathematics and Statistics
First Advisor
Jung-Han Kimn
Abstract
In this work, we present a parallel method for accelerating the multi-period dynamic optimal power flow (DOPF). Our approach involves a distributed-memory parallelization of DOPF time-steps, use of a newly developed parallel primal-dual interior point method, and an iterative Krylov subspace linear solver with a block-Jacobi preconditioning scheme. The parallel primal-dual interior point method has been implemented and distributed in the open-source PETSc library and is currently available. We present the formulation of the DOPF problem, the developed primal dual interior point method solver, the parallel implementation, and results on various multi-core machines. We demonstrate the effectiveness our proposed block-Jacobi preconditioner and various Krylov subspace methods at improving parallel performance.
Library of Congress Subject Headings
Electric power systems -- Mathematical models.
Electric power distribution.
Smart power grids.
Number of Pages
43
Publisher
South Dakota State University
Recommended Citation
Sundermann, Rylee, "Efficient Numerical Optimization for Parallel Dynamic Optimal Power Flow Simulation Using Network Geometry" (2022). Electronic Theses and Dissertations. 365.
https://openprairie.sdstate.edu/etd2/365
Included in
Applied Mathematics Commons, Mathematics Commons, Operational Research Commons, Power and Energy Commons
Rights Statement
