Diffusion-assisted Model Predictive Control Optimization for Power System Real-Time Operation
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913838092255232 |
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| author | Xu, Linna Zhu, Yongli |
| author_facet | Xu, Linna Zhu, Yongli |
| contents | This paper presents a modified model predictive control (MPC) framework for real-time power system operation. The framework incorporates a diffusion model tailored for time series generation to enhance the accuracy of the load forecasting module used in the system operation. In the absence of explicit state transition law, a model-identification procedure is leveraged to derive the system dynamics, thereby eliminating a barrier when applying MPC to a renewables-dominated power system. Case study results on an industry park system and the IEEE 30-bus system demonstrate that using the diffusion model to augment the training dataset significantly improves load-forecasting accuracy, and the inferred system dynamics are applicable to the real-time grid operation with solar and wind. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_08535 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Diffusion-assisted Model Predictive Control Optimization for Power System Real-Time Operation Xu, Linna Zhu, Yongli Systems and Control Machine Learning This paper presents a modified model predictive control (MPC) framework for real-time power system operation. The framework incorporates a diffusion model tailored for time series generation to enhance the accuracy of the load forecasting module used in the system operation. In the absence of explicit state transition law, a model-identification procedure is leveraged to derive the system dynamics, thereby eliminating a barrier when applying MPC to a renewables-dominated power system. Case study results on an industry park system and the IEEE 30-bus system demonstrate that using the diffusion model to augment the training dataset significantly improves load-forecasting accuracy, and the inferred system dynamics are applicable to the real-time grid operation with solar and wind. |
| title | Diffusion-assisted Model Predictive Control Optimization for Power System Real-Time Operation |
| topic | Systems and Control Machine Learning |
| url | https://arxiv.org/abs/2505.08535 |