A fast implementation of coalitional model predictive controllers based on machine learning: Application to solar power plants
This article proposes a real-time implementation of distributed model predictive controllers to maximize the thermal energy generated by parabolic trough collector fields. For this control strategy, we consider that each loop of the solar collector field is individually managed by a controller, whic...
| Authors: | ; ; ; ; |
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| Format: | Article |
| Status: | Published version |
| Publication date: | 2023 |
| Country: | España |
| Institution: | Universidad de Sevilla (US) |
| Repository: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/141085 |
| Online Access: | https://hdl.handle.net/11441/141085 https://doi.org/10.1016/j.engappai.2022.105666 |
| Access Level: | Open access |
| Keyword: | Neural networks Artificial intelligence Non-linear model predictive control Coalitional control Multi-agent systems Solar thermal applications |
| _version_ | 1878735452924542976 |
|---|---|
| author | Masero Rubio, Eva |
| author2 | Ruiz-Moreno, Sara Domínguez Frejo, José Ramón Maestre Torreblanca, José María Camacho, Eduardo F. |
| author2_role | author author author author |
| author_browse | Camacho, Eduardo F. Domínguez Frejo, José Ramón Maestre Torreblanca, José María Masero Rubio, Eva Ruiz-Moreno, Sara |
| author_facet | Masero Rubio, Eva Ruiz-Moreno, Sara Domínguez Frejo, José Ramón Maestre Torreblanca, José María Camacho, Eduardo F. |
| author_role | author |
| collection | idUS. Depósito de Investigación de la Universidad de Sevilla |
| description | This article proposes a real-time implementation of distributed model predictive controllers to maximize the thermal energy generated by parabolic trough collector fields. For this control strategy, we consider that each loop of the solar collector field is individually managed by a controller, which can form coalition with other controllers to attain its local goals while contributing to the overall objective. The formation of coalitions is based on a market-based mechanism in which the heat transfer fluid is traded. To relieve the computational burden online, we propose a learning-based approach that approximates optimization problems so that the controller can be applied in real time. Finally, simulations in a -loop solar collector field are used to assess the coalitional strategy based on neural networks in comparison with the coalitional model predictive control. The results show that the coalitional strategy based on neural networks provides a reduction in computing time of up to and a minimal reduction in performance compared to the coalitional model predictive controller used as the baseline. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | ES_7cfdca9d3c4f723ffbf90d242dc207c2 |
| institution | Universidad de Sevilla (US) |
| instname_str | Universidad de Sevilla (US) |
| network_acronym_str | ES |
| network_name_str | España |
| oai_identifier_str | oai:idus.us.es:11441/141085 |
| publishDate | 2023 |
| publishDateSort | 2023 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| reponame_str | idUS. Depósito de Investigación de la Universidad de Sevilla |
| spelling | A fast implementation of coalitional model predictive controllers based on machine learning: Application to solar power plants Masero Rubio, Eva Ruiz-Moreno, Sara Domínguez Frejo, José Ramón Maestre Torreblanca, José María Camacho, Eduardo F. Neural networks Artificial intelligence Non-linear model predictive control Coalitional control Multi-agent systems Solar thermal applications This article proposes a real-time implementation of distributed model predictive controllers to maximize the thermal energy generated by parabolic trough collector fields. For this control strategy, we consider that each loop of the solar collector field is individually managed by a controller, which can form coalition with other controllers to attain its local goals while contributing to the overall objective. The formation of coalitions is based on a market-based mechanism in which the heat transfer fluid is traded. To relieve the computational burden online, we propose a learning-based approach that approximates optimization problems so that the controller can be applied in real time. Finally, simulations in a -loop solar collector field are used to assess the coalitional strategy based on neural networks in comparison with the coalitional model predictive control. The results show that the coalitional strategy based on neural networks provides a reduction in computing time of up to and a minimal reduction in performance compared to the coalitional model predictive controller used as the baseline. Unión Europea 78905 Ministerio de Ciencia e Innovación PID2020-119476RB-I00 Ministerio de Ciencia e Innovación IJC2018-035395-I Ministerio de Ciencia e Innovación FPU18/04476 Ministerio de Ciencia e Innovación FPU20/01958 Elsevier https://hdl.handle.net/11441/141085 https://doi.org/10.1016/j.engappai.2022.105666 |
| spellingShingle | A fast implementation of coalitional model predictive controllers based on machine learning: Application to solar power plants Masero Rubio, Eva Neural networks Artificial intelligence Non-linear model predictive control Coalitional control Multi-agent systems Solar thermal applications |
| status_str | publishedVersion |
| title | A fast implementation of coalitional model predictive controllers based on machine learning: Application to solar power plants |
| title_full | A fast implementation of coalitional model predictive controllers based on machine learning: Application to solar power plants |
| title_fullStr | A fast implementation of coalitional model predictive controllers based on machine learning: Application to solar power plants |
| title_full_unstemmed | A fast implementation of coalitional model predictive controllers based on machine learning: Application to solar power plants |
| title_short | A fast implementation of coalitional model predictive controllers based on machine learning: Application to solar power plants |
| title_sort | A fast implementation of coalitional model predictive controllers based on machine learning: Application to solar power plants |
| topic | Neural networks Artificial intelligence Non-linear model predictive control Coalitional control Multi-agent systems Solar thermal applications |
| topic_facet | Neural networks Artificial intelligence Non-linear model predictive control Coalitional control Multi-agent systems Solar thermal applications |
| url | https://hdl.handle.net/11441/141085 https://doi.org/10.1016/j.engappai.2022.105666 |