Parallel primal‐dual interior point method for the solution of dynamic optimal power flow
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 paral...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-02-01
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| Series: | IET Generation, Transmission & Distribution |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/gtd2.12708 |
| _version_ | 1797905421655080960 |
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| author | Rylee Sundermann Shrirang G. Abhyankar Hong Zhang Jung‐Han Kimn Timothy M. Hansen |
| author_facet | Rylee Sundermann Shrirang G. Abhyankar Hong Zhang Jung‐Han Kimn Timothy M. Hansen |
| author_sort | Rylee Sundermann |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-10T10:05:02Z |
| format | Article |
| id | doaj.art-d8f40bf0b2ea413ab5e09810cbb7278a |
| institution | Directory Open Access Journal |
| issn | 1751-8687 1751-8695 |
| language | English |
| last_indexed | 2024-04-10T10:05:02Z |
| publishDate | 2023-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Generation, Transmission & Distribution |
| spelling | doaj.art-d8f40bf0b2ea413ab5e09810cbb7278a2023-02-16T03:26:08ZengWileyIET Generation, Transmission & Distribution1751-86871751-86952023-02-0117481182010.1049/gtd2.12708Parallel primal‐dual interior point method for the solution of dynamic optimal power flowRylee Sundermann0Shrirang G. Abhyankar1Hong Zhang2Jung‐Han Kimn3Timothy M. Hansen4Department of Mathematics and Statistics South Dakota State University Brookings South Dakota USAElectricity Infrastructure and Buildings Division Pacific Northwest National Laboratory (PNNL) Richland Washington USAComputer Science Department Illinois Institute of Technology Chicago Illinois USADepartment of Mathematics and Statistics South Dakota State University Brookings South Dakota USADepartment of Electrical Engineering and Computer Science South Dakota State University Brookings South Dakota USAAbstract 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.https://doi.org/10.1049/gtd2.12708power controlpower generation dispatchparallel programmingpower system analysis computingpower system computationpower system economics |
| spellingShingle | Rylee Sundermann Shrirang G. Abhyankar Hong Zhang Jung‐Han Kimn Timothy M. Hansen Parallel primal‐dual interior point method for the solution of dynamic optimal power flow IET Generation, Transmission & Distribution power control power generation dispatch parallel programming power system analysis computing power system computation power system economics |
| title | Parallel primal‐dual interior point method for the solution of dynamic optimal power flow |
| title_full | Parallel primal‐dual interior point method for the solution of dynamic optimal power flow |
| title_fullStr | Parallel primal‐dual interior point method for the solution of dynamic optimal power flow |
| title_full_unstemmed | Parallel primal‐dual interior point method for the solution of dynamic optimal power flow |
| title_short | Parallel primal‐dual interior point method for the solution of dynamic optimal power flow |
| title_sort | parallel primal dual interior point method for the solution of dynamic optimal power flow |
| topic | power control power generation dispatch parallel programming power system analysis computing power system computation power system economics |
| url | https://doi.org/10.1049/gtd2.12708 |
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