MPA-DNN: Projection-Aware Unsupervised Learning for Multi-period DC-OPF
Fuente:
arXiv
Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866908586299359232 |
|---|---|
| author | Kim, Yeomoon Kim, Minsoo Kim, Jip |
| author_facet | Kim, Yeomoon Kim, Minsoo Kim, Jip |
| contents | Ensuring both feasibility and efficiency in optimal power flow (OPF) operations has become increasingly important in modern power systems with high penetrations of renewable energy and energy storage. While deep neural networks (DNNs) have emerged as promising fast surrogates for OPF solvers, they often fail to satisfy critical operational constraints, especially those involving inter-temporal coupling, such as generator ramping limits and energy storage operations. To deal with these issues, we propose a Multi-Period Projection-Aware Deep Neural Network (MPA-DNN) that incorporates a projection layer for multi-period dispatch into the network. By doing so, our model enforces physical feasibility through the projection, enabling end-to-end learning of constraint-compliant dispatch trajectories without relying on labeled data. Experimental results demonstrate that the proposed method achieves near-optimal performance while strictly satisfying all constraints in varying load conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_09349 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MPA-DNN: Projection-Aware Unsupervised Learning for Multi-period DC-OPF Kim, Yeomoon Kim, Minsoo Kim, Jip Systems and Control Ensuring both feasibility and efficiency in optimal power flow (OPF) operations has become increasingly important in modern power systems with high penetrations of renewable energy and energy storage. While deep neural networks (DNNs) have emerged as promising fast surrogates for OPF solvers, they often fail to satisfy critical operational constraints, especially those involving inter-temporal coupling, such as generator ramping limits and energy storage operations. To deal with these issues, we propose a Multi-Period Projection-Aware Deep Neural Network (MPA-DNN) that incorporates a projection layer for multi-period dispatch into the network. By doing so, our model enforces physical feasibility through the projection, enabling end-to-end learning of constraint-compliant dispatch trajectories without relying on labeled data. Experimental results demonstrate that the proposed method achieves near-optimal performance while strictly satisfying all constraints in varying load conditions. |
| title | MPA-DNN: Projection-Aware Unsupervised Learning for Multi-period DC-OPF |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2510.09349 |