Price-responsive model predictive control of floor heating systems for demand response using building thermal mass

Maomao Hu, Fu Xiao*, John Bagterp Jørgensen, Rongling Li

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Floor heating (FH) system is a widely-used thermally active building system, which can take advantage of building thermal mass to shift energy demands to off-peak hours. Its ability of effective utilization of low-temperature energy resources also helps to increase energy efficiency and reduce greenhouse gas emissions. However, control of FH systems remains a challenge due to the large thermal inertia of the pipe-embedded concrete floor. In the context of smart grids, the control issue becomes more complicated because the dynamic electricity prices need to be taken into consideration to achieve economic benefits and encourage demand response participation. In this study, an advanced optimal control method, i.e., model predictive control (MPC), is developed for FH systems, which can simultaneously consider all the influential variables including weather conditions, occupancy and dynamic electricity prices. Considering the on-line computational efficiency, a control-oriented dynamic thermal model for a room integrated with FH system is developed and represented in a stochastic state-space form. An economic MPC controller, formulated as a mixed integer linear programming problem, is designed for FH systems. A TRNSYS-MATLAB co-simulation testbed is developed to test and compare different control methods under various operating conditions in terms of energy consumption, thermal comfort and operating costs. Test results show that, compared to the conventional on-off controller, the MPC controller is able to use building thermal mass to optimally shift energy consumption to low-price periods, improve thermal comfort at the beginning of occupancy, reduce energy demand during peak periods, and save electricity costs for residential end-users. The weather conditions and electricity prices have influences on the start-up time and duration of preheating, energy flexibility potential and electricity cost savings of FH systems.
Original languageEnglish
JournalApplied Thermal Engineering
Volume153
Pages (from-to)316-329
ISSN1359-4311
DOIs
Publication statusPublished - 2019

Keywords

  • Floor heating
  • Model predictive control
  • Dynamic electricity prices
  • RC model
  • Energy flexibility
  • Demand response

Cite this

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title = "Price-responsive model predictive control of floor heating systems for demand response using building thermal mass",
abstract = "Floor heating (FH) system is a widely-used thermally active building system, which can take advantage of building thermal mass to shift energy demands to off-peak hours. Its ability of effective utilization of low-temperature energy resources also helps to increase energy efficiency and reduce greenhouse gas emissions. However, control of FH systems remains a challenge due to the large thermal inertia of the pipe-embedded concrete floor. In the context of smart grids, the control issue becomes more complicated because the dynamic electricity prices need to be taken into consideration to achieve economic benefits and encourage demand response participation. In this study, an advanced optimal control method, i.e., model predictive control (MPC), is developed for FH systems, which can simultaneously consider all the influential variables including weather conditions, occupancy and dynamic electricity prices. Considering the on-line computational efficiency, a control-oriented dynamic thermal model for a room integrated with FH system is developed and represented in a stochastic state-space form. An economic MPC controller, formulated as a mixed integer linear programming problem, is designed for FH systems. A TRNSYS-MATLAB co-simulation testbed is developed to test and compare different control methods under various operating conditions in terms of energy consumption, thermal comfort and operating costs. Test results show that, compared to the conventional on-off controller, the MPC controller is able to use building thermal mass to optimally shift energy consumption to low-price periods, improve thermal comfort at the beginning of occupancy, reduce energy demand during peak periods, and save electricity costs for residential end-users. The weather conditions and electricity prices have influences on the start-up time and duration of preheating, energy flexibility potential and electricity cost savings of FH systems.",
keywords = "Floor heating, Model predictive control, Dynamic electricity prices, RC model, Energy flexibility, Demand response",
author = "Maomao Hu and Fu Xiao and J{\o}rgensen, {John Bagterp} and Rongling Li",
year = "2019",
doi = "10.1016/j.applthermaleng.2019.02.107",
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Price-responsive model predictive control of floor heating systems for demand response using building thermal mass. / Hu, Maomao; Xiao, Fu; Jørgensen, John Bagterp; Li, Rongling.

In: Applied Thermal Engineering, Vol. 153, 2019, p. 316-329.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - Price-responsive model predictive control of floor heating systems for demand response using building thermal mass

AU - Hu, Maomao

AU - Xiao, Fu

AU - Jørgensen, John Bagterp

AU - Li, Rongling

PY - 2019

Y1 - 2019

N2 - Floor heating (FH) system is a widely-used thermally active building system, which can take advantage of building thermal mass to shift energy demands to off-peak hours. Its ability of effective utilization of low-temperature energy resources also helps to increase energy efficiency and reduce greenhouse gas emissions. However, control of FH systems remains a challenge due to the large thermal inertia of the pipe-embedded concrete floor. In the context of smart grids, the control issue becomes more complicated because the dynamic electricity prices need to be taken into consideration to achieve economic benefits and encourage demand response participation. In this study, an advanced optimal control method, i.e., model predictive control (MPC), is developed for FH systems, which can simultaneously consider all the influential variables including weather conditions, occupancy and dynamic electricity prices. Considering the on-line computational efficiency, a control-oriented dynamic thermal model for a room integrated with FH system is developed and represented in a stochastic state-space form. An economic MPC controller, formulated as a mixed integer linear programming problem, is designed for FH systems. A TRNSYS-MATLAB co-simulation testbed is developed to test and compare different control methods under various operating conditions in terms of energy consumption, thermal comfort and operating costs. Test results show that, compared to the conventional on-off controller, the MPC controller is able to use building thermal mass to optimally shift energy consumption to low-price periods, improve thermal comfort at the beginning of occupancy, reduce energy demand during peak periods, and save electricity costs for residential end-users. The weather conditions and electricity prices have influences on the start-up time and duration of preheating, energy flexibility potential and electricity cost savings of FH systems.

AB - Floor heating (FH) system is a widely-used thermally active building system, which can take advantage of building thermal mass to shift energy demands to off-peak hours. Its ability of effective utilization of low-temperature energy resources also helps to increase energy efficiency and reduce greenhouse gas emissions. However, control of FH systems remains a challenge due to the large thermal inertia of the pipe-embedded concrete floor. In the context of smart grids, the control issue becomes more complicated because the dynamic electricity prices need to be taken into consideration to achieve economic benefits and encourage demand response participation. In this study, an advanced optimal control method, i.e., model predictive control (MPC), is developed for FH systems, which can simultaneously consider all the influential variables including weather conditions, occupancy and dynamic electricity prices. Considering the on-line computational efficiency, a control-oriented dynamic thermal model for a room integrated with FH system is developed and represented in a stochastic state-space form. An economic MPC controller, formulated as a mixed integer linear programming problem, is designed for FH systems. A TRNSYS-MATLAB co-simulation testbed is developed to test and compare different control methods under various operating conditions in terms of energy consumption, thermal comfort and operating costs. Test results show that, compared to the conventional on-off controller, the MPC controller is able to use building thermal mass to optimally shift energy consumption to low-price periods, improve thermal comfort at the beginning of occupancy, reduce energy demand during peak periods, and save electricity costs for residential end-users. The weather conditions and electricity prices have influences on the start-up time and duration of preheating, energy flexibility potential and electricity cost savings of FH systems.

KW - Floor heating

KW - Model predictive control

KW - Dynamic electricity prices

KW - RC model

KW - Energy flexibility

KW - Demand response

U2 - 10.1016/j.applthermaleng.2019.02.107

DO - 10.1016/j.applthermaleng.2019.02.107

M3 - Journal article

VL - 153

SP - 316

EP - 329

JO - Applied Thermal Engineering

JF - Applied Thermal Engineering

SN - 1359-4311

ER -