A Feasible Reduced Space Method for Real-Time Optimal Power Flow
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2021
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915992676859904 |
|---|---|
| author | Pacaud, François Maldonado, Daniel Adrian Shin, Sungho Schanen, Michel Anitescu, Mihai |
| author_facet | Pacaud, François Maldonado, Daniel Adrian Shin, Sungho Schanen, Michel Anitescu, Mihai |
| contents | We propose a novel feasible-path algorithm to solve the optimal power flow (OPF) problem for real-time use cases. The method augments the seminal work of Dommel and Tinney with second-order derivatives to work directly in the reduced space induced by the power flow equations. In the reduced space, the optimization problem includes only inequality constraints corresponding to the operational constraints. While the reduced formulation directly enforces the physical constraints, the operational constraints are softly enforced through Augmented Lagrangian penalty terms. In contrast to interior-point algorithms (state-of-the art for solving OPF), our algorithm maintains feasibility at each iteration, which makes it suitable for real-time application. By exploiting accelerator hardware (Graphic Processing Units) to compute the reduced Hessian, we show that the second-order method is numerically tractable and is effective to solve both static and real-time OPF problems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2110_02590 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | A Feasible Reduced Space Method for Real-Time Optimal Power Flow Pacaud, François Maldonado, Daniel Adrian Shin, Sungho Schanen, Michel Anitescu, Mihai Optimization and Control We propose a novel feasible-path algorithm to solve the optimal power flow (OPF) problem for real-time use cases. The method augments the seminal work of Dommel and Tinney with second-order derivatives to work directly in the reduced space induced by the power flow equations. In the reduced space, the optimization problem includes only inequality constraints corresponding to the operational constraints. While the reduced formulation directly enforces the physical constraints, the operational constraints are softly enforced through Augmented Lagrangian penalty terms. In contrast to interior-point algorithms (state-of-the art for solving OPF), our algorithm maintains feasibility at each iteration, which makes it suitable for real-time application. By exploiting accelerator hardware (Graphic Processing Units) to compute the reduced Hessian, we show that the second-order method is numerically tractable and is effective to solve both static and real-time OPF problems. |
| title | A Feasible Reduced Space Method for Real-Time Optimal Power Flow |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2110.02590 |