Market-Oriented Flow Allocation for Thermal Solar Plants: An Auction-Based Methodology with Artificial Intelligence
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866912306465603584 |
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| author | Ruiz-Moreno, Sara Gallego, Antonio J. Macías, Manuel Camacho, Eduardo F. |
| author_facet | Ruiz-Moreno, Sara Gallego, Antonio J. Macías, Manuel Camacho, Eduardo F. |
| contents | This paper presents a novel method to optimize thermal balance in parabolic trough collector (PTC) plants. It uses a market-based system to distribute flow among loops combined with an artificial neural network (ANN) to reduce computation and data requirements. This auction-based approach balances loop temperatures, accommodating varying thermal losses and collector efficiencies. Validation across different thermal losses, optical efficiencies, and irradiance conditions-sunny, partially cloudy, and cloudy-show improved thermal power output and intercept factors compared to a no-allocation system. It demonstrates scalability and practicality for large solar thermal plants, enhancing overall performance. The method was first validated through simulations on a realistic solar plant model, then adapted and successfully tested in a 50 MW solar trough plant, demonstrating its advantages. Furthermore, the algorithms have been implemented, commissioned, and are currently operating in 13 commercial solar trough plants. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_01652 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Market-Oriented Flow Allocation for Thermal Solar Plants: An Auction-Based Methodology with Artificial Intelligence Ruiz-Moreno, Sara Gallego, Antonio J. Macías, Manuel Camacho, Eduardo F. Systems and Control Artificial Intelligence This paper presents a novel method to optimize thermal balance in parabolic trough collector (PTC) plants. It uses a market-based system to distribute flow among loops combined with an artificial neural network (ANN) to reduce computation and data requirements. This auction-based approach balances loop temperatures, accommodating varying thermal losses and collector efficiencies. Validation across different thermal losses, optical efficiencies, and irradiance conditions-sunny, partially cloudy, and cloudy-show improved thermal power output and intercept factors compared to a no-allocation system. It demonstrates scalability and practicality for large solar thermal plants, enhancing overall performance. The method was first validated through simulations on a realistic solar plant model, then adapted and successfully tested in a 50 MW solar trough plant, demonstrating its advantages. Furthermore, the algorithms have been implemented, commissioned, and are currently operating in 13 commercial solar trough plants. |
| title | Market-Oriented Flow Allocation for Thermal Solar Plants: An Auction-Based Methodology with Artificial Intelligence |
| topic | Systems and Control Artificial Intelligence |
| url | https://arxiv.org/abs/2504.01652 |