Learning for Feasible Region on Coal Mine Virtual Power Plants with Imperfect Information
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866929738367369216 |
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| author | Huang, Hongxu Lyu, Ruike Feng, Cheng Zhong, Haiwang Gooi, H. B. Li, Bo Liang, Rui |
| author_facet | Huang, Hongxu Lyu, Ruike Feng, Cheng Zhong, Haiwang Gooi, H. B. Li, Bo Liang, Rui |
| contents | The feasible region assessment (FRA) in industrial virtual power plants (VPPs) is driven by the need to activate large-scale latent industrial loads for demand response, making it essential to aggregate these flexible resources for peak regulation. However, the large number of devices and the need for privacy preservation in coal mines pose challenges to accurately aggregating these resources into a cohesive coal mine VPP. In this paper, we propose an efficient and reliable data-driven approach for FRA in the coal mine VPP that can manage incomplete information. Our data-driven FRA algorithm approximates equipment and FRA parameters based on historical energy dispatch data, effectively addressing the challenges of imperfect information. Simulation results illustrate that our method approximates the accurate feasible operational boundaries under dynamic and imperfect information conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_00701 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning for Feasible Region on Coal Mine Virtual Power Plants with Imperfect Information Huang, Hongxu Lyu, Ruike Feng, Cheng Zhong, Haiwang Gooi, H. B. Li, Bo Liang, Rui Systems and Control The feasible region assessment (FRA) in industrial virtual power plants (VPPs) is driven by the need to activate large-scale latent industrial loads for demand response, making it essential to aggregate these flexible resources for peak regulation. However, the large number of devices and the need for privacy preservation in coal mines pose challenges to accurately aggregating these resources into a cohesive coal mine VPP. In this paper, we propose an efficient and reliable data-driven approach for FRA in the coal mine VPP that can manage incomplete information. Our data-driven FRA algorithm approximates equipment and FRA parameters based on historical energy dispatch data, effectively addressing the challenges of imperfect information. Simulation results illustrate that our method approximates the accurate feasible operational boundaries under dynamic and imperfect information conditions. |
| title | Learning for Feasible Region on Coal Mine Virtual Power Plants with Imperfect Information |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2503.00701 |