Demand response potential of district heating and ventilation in an educational office building

Behrang Vand*, Kristian Martin, Juha Jokisalo, Risto Kosonen, Aira Hast

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

28 Citations (Scopus)
184 Downloads (Pure)

Abstract

This study examines the influence of demand-response control strategies on thermal conditions, indoor air CO2 concentration, and heating energy cost and consumption in an educational office building heated by a district heating system in a cold climate. The real-time pricing-based demand response is applied for space heating, heating of ventilation, and adjustment of airflow rates. The ventilation analysis covers both constant and variable air volumes systems. The applied demand-response algorithms regulate room air temperature set-points for space heating, temperature set-point for supply air, and CO2 set-point adjusted with the variable air volume ventilation system. The accepted room air temperature range was 20-24.5 degrees C and CO2 concentration within 800-1200 ppm. This study was conducted with the validated dynamic building simulation tool IDA ICE. The results illustrate that the maximum yearly savings by demand response of space heating and ventilation with the constant air volume ventilation system are around 3 and 6% for the heating energy consumption and heating energy cost, respectively. For the variable air volume system, the heating energy consumption, heating energy cost, electricity consumption, and electricity cost saved by demand-response control can be up to 8, 11, 9, and 2%, respectively.

Original languageEnglish
Pages (from-to)304-319
Number of pages16
JournalScience and Technology for the Built Environment
Volume26
Issue number3
Early online date27 Nov 2019
DOIs
Publication statusPublished - 15 Mar 2020
MoE publication typeA1 Journal article-refereed

Keywords

  • THERMAL-ENERGY STORAGE
  • SMART GRIDS
  • OPTIMIZATION
  • SYSTEM
  • FLEXIBILITY
  • MICROGRIDS
  • MANAGEMENT
  • MODEL
  • demand-response control
  • district heating
  • rule-based control
  • dynamic energy pricing

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