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Model predictive control for a heat booster substation in ultra low temperature district heating systems

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  • Hermansen, Rune
  • Smith, Kevin
  • Thorsen, Jan Eric
  • Wang, Jiawei
  • Zong, Yi

Abstract

District heating systems may support an increased penetration of stochastic renewable energy technologies and a reduction in centralized combined heat and power plants to reduce carbon dioxide emissions. Ultra low temperature district heating minimizes transport heat losses while enabling the utilization of low-grade surplus heat. Local heat booster substations can heat water to useable temperatures using a heat pump and a hot water tank for storage and flexible operation. This paper proposes a hybrid model predictive control strategy in which an existing heat booster substation is modelled and its charging schedule optimized in real-time over a 24-h forecasted prediction horizon. This enables load shifting whereby scheduling of the heat pump minimizes operation costs. The realisation of energy flexibility can support greater utilization of renewable energy sources and surplus heat in energy supply systems to reduce primary energy consumption. The linear hybrid model predictive controller was successfully implemented in a real 22-flat multifamily building in Copenhagen to verify the control strategy. A comparison of the proposed model predictive control scheduling to the standard rule-based control showed average savings of 23 % on the electricity costs.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:238:y:2022:i:pa:s036054422101879x
    DOI: 10.1016/j.energy.2021.121631
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    2. Quirosa, Gonzalo & Torres, Miguel & Becerra, José A. & Jiménez-Espadafor, Francisco J. & Chacartegui, Ricardo, 2023. "Energy analysis of an ultra-low temperature district heating and cooling system with coaxial borehole heat exchangers," Energy, Elsevier, vol. 278(PA).
    3. Tomc, Urban & Nosan, Simon & Vidrih, Boris & Bogić, Simon & Navickaite, Kristina & Vozel, Katja & Bobič, Miha & Kitanovski, Andrej, 2024. "Small demonstrator of a thermoelectric heat-pump booster for an ultra-low-temperature district-heating substation," Applied Energy, Elsevier, vol. 361(C).
    4. Zinsmeister, Daniel & Tzscheutschler, Peter & Perić, Vedran S. & Goebel, Christoph, 2023. "Stratified thermal energy storage model with constant layer volume for predictive control — Formulation, comparison, and empirical validation," Renewable Energy, Elsevier, vol. 219(P2).
    5. 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).
    6. Mengting Jiang & Camilo Rindt & David M. J. Smeulders, 2022. "Optimal Planning of Future District Heating Systems—A Review," Energies, MDPI, vol. 15(19), pages 1-38, September.
    7. Jan Lorenz Svensen, 2022. "Peak Shaving in District Heating Utilizing Adaptive Predictive Control," Energies, MDPI, vol. 15(22), pages 1-15, November.
    8. Behzadi, Amirmohammad & Holmberg, Sture & Duwig, Christophe & Haghighat, Fariborz & Ooka, Ryozo & Sadrizadeh, Sasan, 2022. "Smart design and control of thermal energy storage in low-temperature heating and high-temperature cooling systems: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 166(C).
    9. Selva Calixto & Marco Cozzini & Roberto Fedrizzi & Giampaolo Manzolini, 2024. "A New Method for the Techno-Economic Analysis and the Identification of Expansion Strategies of Neutral-Temperature District Heating and Cooling Systems," Energies, MDPI, vol. 17(9), pages 1-20, April.
    10. Tahiri, Abdelkarim & Smith, Kevin Michael & Thorsen, Jan Eric & Hviid, Christian Anker & Svendsen, Svend, 2023. "Staged control of domestic hot water storage tanks to support district heating efficiency," Energy, Elsevier, vol. 263(PB).
    11. Gianni Martinazzoli & Daniele Pasinelli & Adriano Maria Lezzi & Mariagrazia Pilotelli, 2023. "Design of a 5th Generation District Heating Substation Prototype for a Real Case Study," Sustainability, MDPI, vol. 15(4), pages 1-21, February.
    12. Chen, Minghao & Xie, Zhiyuan & Sun, Yi & Zheng, Shunlin, 2023. "The predictive management in campus heating system based on deep reinforcement learning and probabilistic heat demands forecasting," Applied Energy, Elsevier, vol. 350(C).

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