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

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

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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.

Original languageEnglish
Article number121631
JournalEnergy
Volume238
Number of pages12
ISSN0360-5442
DOIs
Publication statusPublished - 2022

Bibliographical note

Funding Information:
This work is supported by “EnergyLab Nordhavn-New Urban Energy Infrastructures and Smart Components” project grant by the Danish Energy Technology Development and Demonstration Program (No. 64015-0055 ).

Publisher Copyright:
© 2021 Elsevier Ltd

Keywords

  • 4GDH
  • Heat booster station
  • Model predictive control
  • Power to heat
  • Smart energy systems
  • Ultra low temperature district heating

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