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Multi-flow optimization of a greenhouse system: A hierarchical control approach

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  • Blaud, Pierre Clement
  • Haurant, Pierrick
  • Chevrel, Philippe
  • Claveau, Fabien
  • Mouraud, Anthony

Abstract

Greenhouses are implemented all over the world to increase agricultural production thanks to controlled environmental conditions (inside temperature, moisture and CO2 contents). However, such systems are energy-intensive. The presented work focuses on controlling a greenhouse’ onsite multi-energy system (gas, heat and electricity), extended to a multi-flow system as the CO2 produced by the energy units is used as a plant fertilizers. In this view, a three levels hierarchical control has been developed: a steady state economic MPC is combined with a dynamic Multi-energy MPC and low-level PID controllers. This new controller aims at determining the best synergies for economic flows management, subject to compliance with the required climatic conditions in the greenhouse. The proposed controller is applied to a greenhouse which energy system is based on a thermal energy storage fueled by a gas boiler and a combined heat and power unit. The results are confronted to a usual ruled-based controller performed by greenhouses owners, showing a more efficient dynamic functioning of the energy system. Consequently, less gas is consumed (80.79 t to 76.03 t), heat is produced when needed, and electricity from the CHP is economically optimized allowing less importation from the grid (from 17.80 MWh to 15.88 MWh), and profitable selling.

Suggested Citation

  • Blaud, Pierre Clement & Haurant, Pierrick & Chevrel, Philippe & Claveau, Fabien & Mouraud, Anthony, 2023. "Multi-flow optimization of a greenhouse system: A hierarchical control approach," Applied Energy, Elsevier, vol. 351(C).
  • Handle: RePEc:eee:appene:v:351:y:2023:i:c:s0306261923012047
    DOI: 10.1016/j.apenergy.2023.121840
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    References listed on IDEAS

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    1. van Beveren, P.J.M. & Bontsema, J. & van Straten, G. & van Henten, E.J., 2015. "Optimal control of greenhouse climate using minimal energy and grower defined bounds," Applied Energy, Elsevier, vol. 159(C), pages 509-519.
    2. Lin, Dong & Zhang, Lijun & Xia, Xiaohua, 2021. "Model predictive control of a Venlo-type greenhouse system considering electrical energy, water and carbon dioxide consumption," Applied Energy, Elsevier, vol. 298(C).
    3. Kelvin López-Aguilar & Adalberto Benavides-Mendoza & Susana González-Morales & Antonio Juárez-Maldonado & Pamela Chiñas-Sánchez & Alvaro Morelos-Moreno, 2020. "Artificial Neural Network Modeling of Greenhouse Tomato Yield and Aerial Dry Matter," Agriculture, MDPI, vol. 10(4), pages 1-14, April.
    4. de Araujo Passos, Luigi Antonio & Ceha, Thomas Joseph & Baldi, Simone & De Schutter, Bart, 2023. "Model predictive control of a thermal chimney and dynamic solar shades for an all-glass facades building," Energy, Elsevier, vol. 264(C).
    5. Van Beveren, P.J.M. & Bontsema, J. & Van Straten, G. & Van Henten, E.J., 2015. "Minimal heating and cooling in a modern rose greenhouse," Applied Energy, Elsevier, vol. 137(C), pages 97-109.
    6. Lijun Chen & Shangfeng Du & Dan Xu & Yaofeng He & Meihui Liang, 2018. "Sliding Mode Control Based on Disturbance Observer for Greenhouse Climate Systems," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-8, April.
    7. Golzar, Farzin & Heeren, Niko & Hellweg, Stefanie & Roshandel, Ramin, 2018. "A novel integrated framework to evaluate greenhouse energy demand and crop yield production," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 487-501.
    8. Chiara Bersani & Ahmed Ouammi & Roberto Sacile & Enrico Zero, 2020. "Model Predictive Control of Smart Greenhouses as the Path towards Near Zero Energy Consumption," Energies, MDPI, vol. 13(14), pages 1-17, July.
    9. Mahmood, Farhat & Govindan, Rajesh & Bermak, Amine & Yang, David & Al-Ansari, Tareq, 2023. "Data-driven robust model predictive control for greenhouse temperature control and energy utilisation assessment," Applied Energy, Elsevier, vol. 343(C).
    10. Chen, Wei-Han & Mattson, Neil S. & You, Fengqi, 2022. "Intelligent control and energy optimization in controlled environment agriculture via nonlinear model predictive control of semi-closed greenhouse," Applied Energy, Elsevier, vol. 320(C).
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