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Model predictive control for thermal energy storage assisted large central cooling systems

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  • Shan, Kui
  • Fan, Cheng
  • Wang, Jiayuan

Abstract

Variable speed drivers (VSDs) are commonly used for enhancing energy efficiency in building central cooling systems. However, VSDs often consume about 4–8% of the converted energy. Moreover, the initial and maintenance costs of VSDs for extremely large and high voltage chillers could be extremely high. This study proposes to use thermal energy storage (TES) to enhance energy efficiency of extremely large constant speed chillers. A new model predictive control method is proposed to control the charging/discharging of TES and on/off of chillers to achieve high efficiency. The proposed method partially decouples the demand side and the supply side, so that the large chillers are either operated in high efficiency or turned off. The method can also solve the problem of frequent chiller tripping due to too low load in winter conditions. The proposed optimal control strategy has been validated on a dynamic platform built based on the existing chiller plant in a high-rise commercial building. Validation tests were conducted in both summer and winter conditions based on real operation data. Results show that the proposed method could improve the efficiency of chillers by 3.10% and 22.94% in summer and winter conditions, respectively.

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  • Shan, Kui & Fan, Cheng & Wang, Jiayuan, 2019. "Model predictive control for thermal energy storage assisted large central cooling systems," Energy, Elsevier, vol. 179(C), pages 916-927.
    • Handle: RePEc:eee:energy:v:179:y:2019:i:c:p:916-927
    DOI: 10.1016/j.energy.2019.04.178
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    References listed on IDEAS

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    1. Baeten, Brecht & Confrey, Thomas & Pecceu, Sébastien & Rogiers, Frederik & Helsen, Lieve, 2016. "A validated model for mixing and buoyancy in stratified hot water storage tanks for use in building energy simulations," Applied Energy, Elsevier, vol. 172(C), pages 217-229.
    2. Zhang, Yin & Wang, Xin & Zhuo, Siwen & Zhang, Yinping, 2016. "Pre-feasibility of building cooling heating and power system with thermal energy storage considering energy supply–demand mismatch," Applied Energy, Elsevier, vol. 167(C), pages 125-134.
    3. Arteconi, Alessia & Ciarrocchi, Eleonora & Pan, Quanwen & Carducci, Francesco & Comodi, Gabriele & Polonara, Fabio & Wang, Ruzhu, 2017. "Thermal energy storage coupled with PV panels for demand side management of industrial building cooling loads," Applied Energy, Elsevier, vol. 185(P2), pages 1984-1993.
    4. Crespo Del Granado, Pedro & Pang, Zhan & Wallace, Stein W., 2016. "Synergy of smart grids and hybrid distributed generation on the value of energy storage," Applied Energy, Elsevier, vol. 170(C), pages 476-488.
    5. Cui, Borui & Gao, Dian-ce & Wang, Shengwei & Xue, Xue, 2015. "Effectiveness and life-cycle cost-benefit analysis of active cold storages for building demand management for smart grid applications," Applied Energy, Elsevier, vol. 147(C), pages 523-535.
    6. Cui, Borui & Gao, Dian-ce & Xiao, Fu & Wang, Shengwei, 2017. "Model-based optimal design of active cool thermal energy storage for maximal life-cycle cost saving from demand management in commercial buildings," Applied Energy, Elsevier, vol. 201(C), pages 382-396.
    7. Alva, Guruprasad & Lin, Yaxue & Fang, Guiyin, 2018. "An overview of thermal energy storage systems," Energy, Elsevier, vol. 144(C), pages 341-378.
    8. Luo, Na & Hong, Tianzhen & Li, Hui & Jia, Ruoxi & Weng, Wenguo, 2017. "Data analytics and optimization of an ice-based energy storage system for commercial buildings," Applied Energy, Elsevier, vol. 204(C), pages 459-475.
    9. Chan, Apple L.S. & Chow, Tin-Tai & Fong, Square K.F. & Lin, John Z., 2006. "Performance evaluation of district cooling plant with ice storage," Energy, Elsevier, vol. 31(14), pages 2750-2762.
    10. Guo, Shaopeng & Liu, Qibin & Sun, Jie & Jin, Hongguang, 2018. "A review on the utilization of hybrid renewable energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 1121-1147.
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    Cited by:

    1. Ragosebo Kgaugelo Modise & Khumbulani Mpofu & Olukorede Tijani Adenuga, 2021. "Energy and Carbon Emission Efficiency Prediction: Applications in Future Transport Manufacturing," Energies, MDPI, vol. 14(24), pages 1-15, December.
    2. Shan, Kui & Wang, Shengwei & Zhuang, Chaoqun, 2021. "Controlling a large constant speed centrifugal chiller to provide grid frequency regulation: A validation based on onsite tests," Applied Energy, Elsevier, vol. 300(C).
    3. Yang Yuan & Neng Zhu & Haizhu Zhou & Hai Wang, 2021. "A New Model Predictive Control Method for Eliminating Hydraulic Oscillation and Dynamic Hydraulic Imbalance in a Complex Chilled Water System," Energies, MDPI, vol. 14(12), pages 1-23, June.
    4. Zou, Wenke & Sun, Yongjun & Gao, Dian-ce & Zhang, Xu, 2023. "Globally optimal control of hybrid chilled water plants integrated with small-scale thermal energy storage for energy-efficient operation," Energy, Elsevier, vol. 262(PA).
    5. Wan, Xin & Luo, Xiong-Lin, 2020. "Economic optimization of chemical processes based on zone predictive control with redundancy variables," Energy, Elsevier, vol. 212(C).
    6. Cao, Hui & Lin, Jiajing & Li, Nan, 2023. "Optimal control and energy efficiency evaluation of district ice storage system," Energy, Elsevier, vol. 276(C).
    7. Oravec, Juraj & Horváthová, Michaela & Bakošová, Monika, 2020. "Energy efficient convex-lifting-based robust control of a heat exchanger," Energy, Elsevier, vol. 201(C).
    8. Tarragona, Joan & Pisello, Anna Laura & Fernández, Cèsar & de Gracia, Alvaro & Cabeza, Luisa F., 2021. "Systematic review on model predictive control strategies applied to active thermal energy storage systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    9. Haji Haji, Vahab & Fekih, Afef & Monje, Concepción Alicia & Fakhri Asfestani, Ramin, 2020. "Adaptive model predictive control design for the speed and temperature control of a V94.2 gas turbine unit in a combined cycle power plant," Energy, Elsevier, vol. 207(C).

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