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Model predictive control for a university heat prosumer with data centre waste heat and thermal energy storage

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  • Hou, Juan
  • Li, Haoran
  • Nord, Natasa
  • Huang, Gongsheng

Abstract

Data centres (DCs) are energy-intensive facilities that convert most of their energy use into waste heat. Given the rapidly increasing energy and environmental impacts of DCs, and the need to optimize regional energy structures, there is an increasing effort to recover DC waste heat for district heating (DH) systems. However, previous research mainly focused on exploring the possibilities and proposing technical solutions for capturing DC waste heat for DH systems. They rarely investigated solutions on optimal control of the DH system after recovering DC waste heat, particularly for a DC waste heat-based heat prosumer with thermal energy storage (TES). Therefore, this study applied a model predictive control (MPC) scheme for a university heat prosumer with DC waste heat and water tank TES by simulation. In the framework, the objective function minimized the overall energy cost considering the dynamic heating and electricity prices simultaneously, and the incorporated model described system dynamics including DC waste heat recovery units, TES, and campus DH system. The MPC framework was demonstrated to be more effective than a traditional rule-based control approach in terms of 1) providing more stable chilled water for the DC cooling system and 2) cutting monthly energy costs by up to 3.2%.

Suggested Citation

  • Hou, Juan & Li, Haoran & Nord, Natasa & Huang, Gongsheng, 2023. "Model predictive control for a university heat prosumer with data centre waste heat and thermal energy storage," Energy, Elsevier, vol. 267(C).
  • Handle: RePEc:eee:energy:v:267:y:2023:i:c:s0360544222034661
    DOI: 10.1016/j.energy.2022.126579
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    as
    1. Hiltunen, Pauli & Syri, Sanna, 2021. "Low-temperature waste heat enabling abandoning coal in Espoo district heating system," Energy, Elsevier, vol. 231(C).
    2. Ahmad, Tanveer & Chen, Huanxin & Shair, Jan, 2018. "Water source heat pump energy demand prognosticate using disparate data-mining based approaches," Energy, Elsevier, vol. 152(C), pages 788-803.
    3. Li, Haoran & Hou, Juan & Hong, Tianzhen & Nord, Natasa, 2022. "Distinguish between the economic optimal and lowest distribution temperatures for heat-prosumer-based district heating systems with short-term thermal energy storage," Energy, Elsevier, vol. 248(C).
    4. Khosravi, A. & Laukkanen, T. & Vuorinen, V. & Syri, S., 2021. "Waste heat recovery from a data centre and 5G smart poles for low-temperature district heating network," Energy, Elsevier, vol. 218(C).
    5. Song, Jingjing & Wallin, Fredrik & Li, Hailong, 2017. "District heating cost fluctuation caused by price model shift," Applied Energy, Elsevier, vol. 194(C), pages 715-724.
    6. Aoun, Nadine & Bavière, Roland & Vallée, Mathieu & Aurousseau, Antoine & Sandou, Guillaume, 2019. "Modelling and flexible predictive control of buildings space-heating demand in district heating systems," Energy, Elsevier, vol. 188(C).
    7. Knudsen, Brage Rugstad & Rohde, Daniel & Kauko, Hanne, 2021. "Thermal energy storage sizing for industrial waste-heat utilization in district heating: A model predictive control approach," Energy, Elsevier, vol. 234(C).
    8. Wahlroos, Mikko & Pärssinen, Matti & Manner, Jukka & Syri, Sanna, 2017. "Utilizing data center waste heat in district heating – Impacts on energy efficiency and prospects for low-temperature district heating networks," Energy, Elsevier, vol. 140(P1), pages 1228-1238.
    9. Lund, Henrik & Østergaard, Poul Alberg & Chang, Miguel & Werner, Sven & Svendsen, Svend & Sorknæs, Peter & Thorsen, Jan Eric & Hvelplund, Frede & Mortensen, Bent Ole Gram & Mathiesen, Brian Vad & Boje, 2018. "The status of 4th generation district heating: Research and results," Energy, Elsevier, vol. 164(C), pages 147-159.
    10. Li, Haoran & Hou, Juan & Hong, Tianzhen & Ding, Yuemin & Nord, Natasa, 2021. "Energy, economic, and environmental analysis of integration of thermal energy storage into district heating systems using waste heat from data centres," Energy, Elsevier, vol. 219(C).
    11. Huang, Pei & Copertaro, Benedetta & Zhang, Xingxing & Shen, Jingchun & Löfgren, Isabelle & Rönnelid, Mats & Fahlen, Jan & Andersson, Dan & Svanfeldt, Mikael, 2020. "A review of data centers as prosumers in district energy systems: Renewable energy integration and waste heat reuse for district heating," Applied Energy, Elsevier, vol. 258(C).
    12. Hermansen, Rune & Smith, Kevin & Thorsen, Jan Eric & Wang, Jiawei & Zong, Yi, 2022. "Model predictive control for a heat booster substation in ultra low temperature district heating systems," Energy, Elsevier, vol. 238(PA).
    13. Wahlroos, Mikko & Pärssinen, Matti & Rinne, Samuli & Syri, Sanna & Manner, Jukka, 2018. "Future views on waste heat utilization – Case of data centers in Northern Europe," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P2), pages 1749-1764.
    14. Saletti, Costanza & Zimmerman, Nathan & Morini, Mirko & Kyprianidis, Konstantinos & Gambarotta, Agostino, 2021. "Enabling smart control by optimally managing the State of Charge of district heating networks," Applied Energy, Elsevier, vol. 283(C).
    15. Hou, Juan & Li, Haoran & Nord, Natasa, 2022. "Nonlinear model predictive control for the space heating system of a university building in Norway," Energy, Elsevier, vol. 253(C).
    16. Leitner, Benedikt & Widl, Edmund & Gawlik, Wolfgang & Hofmann, René, 2020. "Control assessment in coupled local district heating and electrical distribution grids: Model predictive control of electric booster heaters," Energy, Elsevier, vol. 210(C).
    17. Nord, Natasa & Shakerin, Mohammad & Tereshchenko, Tymofii & Verda, Vittorio & Borchiellini, Romano, 2021. "Data informed physical models for district heating grids with distributed heat sources to understand thermal and hydraulic aspects," Energy, Elsevier, vol. 222(C).
    18. Werner, Sven, 2017. "International review of district heating and cooling," Energy, Elsevier, vol. 137(C), pages 617-631.
    19. Lyons, Ben & O’Dwyer, Edward & Shah, Nilay, 2020. "Model reduction for Model Predictive Control of district and communal heating systems within cooperative energy systems," Energy, Elsevier, vol. 197(C).
    20. Li, Haoran & Hou, Juan & Tian, Zhiyong & Hong, Tianzhen & Nord, Natasa & Rohde, Daniel, 2022. "Optimize heat prosumers' economic performance under current heating price models by using water tank thermal energy storage," Energy, Elsevier, vol. 239(PB).
    21. 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.
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    2. Jiang, Qingfeng & Liang, Wenlong & Zhu, Ze & Li, Yiliang & Wang, Pengfei, 2023. "Multimodel generalized predictive control of a heat-pipe reactor coupled with an open-air Brayton cycle," Energy, Elsevier, vol. 279(C).

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