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Virtual testbed for model predictive control development in district cooling systems

Author

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  • Zabala, Laura
  • Febres, Jesus
  • Sterling, Raymond
  • López, Susana
  • Keane, Marcus

Abstract

Recently, with increasing cooling demands, district cooling has assumed an important role as it is more efficient than stand-alone cooling systems. District cooling reduces the environmental impact and promotes the use of renewable sources. Earlier studies to optimise the production plants of district cooling systems were focused primarily on plants with compressor chillers and thermal energy storage devices. Although absorption chillers are crucial for integrating renewable sources into these systems, very few studies have considered them from the cooling perspective. In this regard, this paper presents the progress and results of the implementation of a virtual testbed based on a digital twin of a district cooling production plant with both compressor and absorption chillers. The aim of this study, carried out within the framework of INDIGO, a European Union-funded project, was (i) to develop a reliable model that can be used in a model predictive controller and (ii) to simulate the plant using this controller. The production plant components, which included absorption and compressor chillers, as well as cooling towers, were built using the equation-based Modelica programming language, and were calibrated using information from the manufacturer, together with real operation data. The remainder of the plant was modelled in Python. To integrate the Modelica models into the Python environment, a combination of machine learning techniques and state-space representation models was used. With these techniques, models with a high computational speed were obtained, which were suitable for real-time applications. These models were then used to build a model predictive control for the production plant to minimise the primary energy usage. The improvements in the control and the resultant energy savings achieved were compared with a baseline case working on a standard cascade control. Energy savings up to 50% were obtained in the simulation-based experiments.

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  • Zabala, Laura & Febres, Jesus & Sterling, Raymond & López, Susana & Keane, Marcus, 2020. "Virtual testbed for model predictive control development in district cooling systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 129(C).
    • Handle: RePEc:eee:rensus:v:129:y:2020:i:c:s1364032120302112
    DOI: 10.1016/j.rser.2020.109920
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    References listed on IDEAS

    as
    1. Huang, Sen & Zuo, Wangda & Sohn, Michael D., 2016. "Amelioration of the cooling load based chiller sequencing control," Applied Energy, Elsevier, vol. 168(C), pages 204-215.
    2. Chiam, Zhonglin & Easwaran, Arvind & Mouquet, David & Fazlollahi, Samira & Millás, Jaume V., 2019. "A hierarchical framework for holistic optimization of the operations of district cooling systems," Applied Energy, Elsevier, vol. 239(C), pages 23-40.
    3. Inayat, Abrar & Raza, Mohsin, 2019. "District cooling system via renewable energy sources: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 107(C), pages 360-373.
    4. Facci, Andrea Luigi & Andreassi, Luca & Ubertini, Stefano, 2014. "Optimization of CHCP (combined heat power and cooling) systems operation strategy using dynamic programming," Energy, Elsevier, vol. 66(C), pages 387-400.
    5. Palomba, Valeria & Dino, Giuseppe E. & Frazzica, Andrea, 2020. "Coupling sorption and compression chillers in hybrid cascade layout for efficient exploitation of renewables: Sizing, design and optimization," Renewable Energy, Elsevier, vol. 154(C), pages 11-28.
    6. Gang, Wenjie & Augenbroe, Godfried & Wang, Shengwei & Fan, Cheng & Xiao, Fu, 2016. "An uncertainty-based design optimization method for district cooling systems," Energy, Elsevier, vol. 102(C), pages 516-527.
    7. Shaikh, Pervez Hameed & Nor, Nursyarizal Bin Mohd & Nallagownden, Perumal & Elamvazuthi, Irraivan & Ibrahim, Taib, 2014. "A review on optimized control systems for building energy and comfort management of smart sustainable buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 409-429.
    8. Lake, Andrew & Rezaie, Behanz & Beyerlein, Steven, 2017. "Review of district heating and cooling systems for a sustainable future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 417-425.
    9. Gang, Wenjie & Wang, Shengwei & Xiao, Fu & Gao, Dian-ce, 2016. "District cooling systems: Technology integration, system optimization, challenges and opportunities for applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 253-264.
    10. Valerie Eveloy & Dereje S. Ayou, 2019. "Sustainable District Cooling Systems: Status, Challenges, and Future Opportunities, with Emphasis on Cooling-Dominated Regions," Energies, MDPI, vol. 12(2), pages 1-64, January.
    11. Rezaie, Behnaz & Rosen, Marc A., 2012. "District heating and cooling: Review of technology and potential enhancements," Applied Energy, Elsevier, vol. 93(C), pages 2-10.
    12. Trygg, Louise & Amiri, Shahnaz, 2007. "European perspective on absorption cooling in a combined heat and power system - A case study of energy utility and industries in Sweden," Applied Energy, Elsevier, vol. 84(12), pages 1319-1337, December.
    13. Sanaye, Sepehr & Sarrafi, Ahmadreza, 2015. "Optimization of combined cooling, heating and power generation by a solar system," Renewable Energy, Elsevier, vol. 80(C), pages 699-712.
    14. Burer, M. & Tanaka, K. & Favrat, D. & Yamada, K., 2003. "Multi-criteria optimization of a district cogeneration plant integrating a solid oxide fuel cell–gas turbine combined cycle, heat pumps and chillers," Energy, Elsevier, vol. 28(6), pages 497-518.
    15. Dorotić, Hrvoje & Pukšec, Tomislav & Duić, Neven, 2019. "Multi-objective optimization of district heating and cooling systems for a one-year time horizon," Energy, Elsevier, vol. 169(C), pages 319-328.
    16. Jakubcionis, Mindaugas & Carlsson, Johan, 2017. "Estimation of European Union residential sector space cooling potential," Energy Policy, Elsevier, vol. 101(C), pages 225-235.
    17. Wei, Dajun & Chen, Alian & Sun, Bo & Zhang, Chenghui, 2016. "Multi-objective optimal operation and energy coupling analysis of combined cooling and heating system," Energy, Elsevier, vol. 98(C), pages 296-307.
    18. Vandermeulen, Annelies & van der Heijde, Bram & Helsen, Lieve, 2018. "Controlling district heating and cooling networks to unlock flexibility: A review," Energy, Elsevier, vol. 151(C), pages 103-115.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

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