An Uncertainty-Aware Data-Driven Predictive Controller for Hybrid Power Plants
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866913698252062720 |
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| author | Desai, Manavendra Sharma, Himanshu Mukherjee, Sayak Glavaski, Sonja |
| author_facet | Desai, Manavendra Sharma, Himanshu Mukherjee, Sayak Glavaski, Sonja |
| contents | Given the advancements in data-driven modeling for complex engineering and scientific applications, this work utilizes a data-driven predictive control method, namely subspace predictive control, to coordinate hybrid power plant components and meet a desired power demand despite the presence of weather uncertainties. An uncertainty-aware data-driven predictive controller is proposed, and its potential is analyzed using real-world electricity demand profiles. For the analysis, a hybrid power plant with wind, solar, and co-located energy storage capacity of 4 MW each is considered. The analysis shows that the predictive controller can track a real-world-inspired electricity demand profile despite the presence of weather-induced uncertainties and be an intelligent forecaster for HPP performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_13333 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Uncertainty-Aware Data-Driven Predictive Controller for Hybrid Power Plants Desai, Manavendra Sharma, Himanshu Mukherjee, Sayak Glavaski, Sonja Systems and Control Computational Engineering, Finance, and Science Optimization and Control Given the advancements in data-driven modeling for complex engineering and scientific applications, this work utilizes a data-driven predictive control method, namely subspace predictive control, to coordinate hybrid power plant components and meet a desired power demand despite the presence of weather uncertainties. An uncertainty-aware data-driven predictive controller is proposed, and its potential is analyzed using real-world electricity demand profiles. For the analysis, a hybrid power plant with wind, solar, and co-located energy storage capacity of 4 MW each is considered. The analysis shows that the predictive controller can track a real-world-inspired electricity demand profile despite the presence of weather-induced uncertainties and be an intelligent forecaster for HPP performance. |
| title | An Uncertainty-Aware Data-Driven Predictive Controller for Hybrid Power Plants |
| topic | Systems and Control Computational Engineering, Finance, and Science Optimization and Control |
| url | https://arxiv.org/abs/2502.13333 |