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Cryo-polygeneration plant - a novel operation algorithm leveraging adaptive AI energy systems models for urban microgrids

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  • Thangavelu, Sundar Raj
  • Tafone, Alessio
  • Gunasekhara, Imantha
  • Morita, Shigenori
  • Romagnoli, Alessandro

Abstract

Cryo-Polygeneration is a quite sophisticated system that accounts for different energy generations comprising electricity, steam, hot and cold energy and more. However, the true challenge lies in operating these diverse energy systems in harmony to achieve the best performance and overall process objectives, mainly operation cost and energy efficiency. This study proposes a novel algorithm for optimizing operations of cryo-polygeneration systems, enabling precise scheduling and dispatch based on anticipated time-varying energy requirements or loads. By deriving optimal setpoints that minimize operating expenses, the algorithm addresses key operational objectives within this emerging field of cryo-polygeneration systems. Leveraging the power of AI, this study exploited the use of adaptive AI model of the distributed energy systems that offers high prediction accuracy due to minimal plant-model mismatch and also uncovers inherent benefits and limitations. The developed algorithm is applicable to both island and grid-connected polygeneration systems with power trading attributes. The workability of the proposed operation algorithm was demonstrated using a district-scale case study, which confirms the integration of multi-energy technologies and process flexibility helped to improve operational objectives greatly, resulting in a 32 to 51 % reduction in operation costs compared to the baseline. Ultimately, this novel operational algorithm presents a transformative solution for cryo-polygeneration plants, offering a reliable framework to promote sustainable energy resource management. The proposed operation algorithm can be easily implemented in the energy management system of cryo-polygeneration plants, facilitating more efficient and sustainable energy resource utilization.

Suggested Citation

  • Thangavelu, Sundar Raj & Tafone, Alessio & Gunasekhara, Imantha & Morita, Shigenori & Romagnoli, Alessandro, 2025. "Cryo-polygeneration plant - a novel operation algorithm leveraging adaptive AI energy systems models for urban microgrids," Applied Energy, Elsevier, vol. 383(C).
    • Handle: RePEc:eee:appene:v:383:y:2025:i:c:s0306261925000911
    DOI: 10.1016/j.apenergy.2025.125361
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    References listed on IDEAS

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    1. Jana, Kuntal & Ray, Avishek & Majoumerd, Mohammad Mansouri & Assadi, Mohsen & De, Sudipta, 2017. "Polygeneration as a future sustainable energy solution – A comprehensive review," Applied Energy, Elsevier, vol. 202(C), pages 88-111.
    2. Murugan, S. & Horák, Bohumil, 2016. "Tri and polygeneration systems - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1032-1051.
    3. Thangavelu, Sundar Raj & Myat, Aung & Khambadkone, Ashwin, 2017. "Energy optimization methodology of multi-chiller plant in commercial buildings," Energy, Elsevier, vol. 123(C), pages 64-76.
    4. Piacentino, Antonio & Barbaro, Chiara, 2013. "A comprehensive tool for efficient design and operation of polygeneration-based energy μgrids serving a cluster of buildings. Part II: Analysis of the applicative potential," Applied Energy, Elsevier, vol. 111(C), pages 1222-1238.
    5. Lei, Ming & C. Alves, José & A. Alotaibi, Majed & Chen, Jinwei, 2024. "A novel polygeneration process integrated into a gas turbine cycle toward an environmentally friendly framework; Application of a comprehensive 4E study," Energy, Elsevier, vol. 307(C).
    6. Lu, Yakai & Tian, Zhe & Zhou, Ruoyu & Liu, Wenjing, 2021. "A general transfer learning-based framework for thermal load prediction in regional energy system," Energy, Elsevier, vol. 217(C).
    7. Tafone, Alessio & Raj Thangavelu, Sundar & Morita, Shigenori & Romagnoli, Alessandro, 2023. "Design optimization of a novel cryo-polygeneration demonstrator developed in Singapore – Techno-economic feasibility study for a cooling dominated tropical climate," Applied Energy, Elsevier, vol. 330(PB).
    8. Cimmino, Luca & Barco Burgos, Jimmy & Eicker, Ursula, 2024. "Exergy and thermoeconomic analysis of a novel polygeneration system based on gasification and power-to-x strategy," Renewable Energy, Elsevier, vol. 236(C).
    9. Sundar Raj Thangavelu & Inam Ullah Nutkani & Chia Meng Hwee & Aung Myat & Ashwin Khambadkone, 2015. "Integrated Electrical and Thermal Grid Facility - Testing of Future Microgrid Technologies," Energies, MDPI, vol. 8(9), pages 1-24, September.
    10. Piacentino, Antonio & Barbaro, Chiara & Cardona, Fabio & Gallea, Roberto & Cardona, Ennio, 2013. "A comprehensive tool for efficient design and operation of polygeneration-based energy μgrids serving a cluster of buildings. Part I: Description of the method," Applied Energy, Elsevier, vol. 111(C), pages 1204-1221.
    11. Kasaeian, Alibakhsh & Bellos, Evangelos & Shamaeizadeh, Armin & Tzivanidis, Christos, 2020. "Solar-driven polygeneration systems: Recent progress and outlook," Applied Energy, Elsevier, vol. 264(C).
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