Economic model predictive control for demand flexibility of a residential building

Christian Finck (Corresponding author), Rongling Li, Wim Zeiler

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)
1 Downloads (Pure)

Abstract

Future building energy management systems will have to be capable of adapting to variation in the rate of production of energy from renewable sources. Controllers employing a model predictive control (MPC) framework can optimise and schedule energy usage based on the availability of renewably generated energy. In this paper, an MPC using artificial neural networks (ANNs) was implemented in a residential building. The ANN-MPC was successfully tested and demonstrated good performance predicting the building's energy consumption. The controller was then modified to function as an economic MPC (EMPC) to optimise demand flexibility (i.e., the ability to adapt energy demands to fluctuations in supply). The operational costs of energy usage were associated with this demand flexibility, which was represented by three flexibility indicators: flexibility factor, supply cover factor, and load cover factor. The results from a day-long test showed that these flexibility indicators were maximised (flexibility factor ranged from −0.88 to 0.67, supply cover factor from 0.04 to 0.13, and load cover factor from 0.07 to 0.16) when the EMPC controller's demand flexibility was compared to that of a conventional proportional-integral (PI) controller. The EMPC framework for demand flexibility can be used to regulate on-site energy generation, grid consumption, and grid feed-in and can thus serve as a basis for overall optimisation of the operation of heating systems to achieve greater demand flexibility.

Original languageEnglish
Pages (from-to)365-379
Number of pages15
JournalEnergy
Volume176
DOIs
Publication statusPublished - 1 Jun 2019

Fingerprint

Model predictive control
Controllers
Economics
Neural networks
Energy management systems
Energy utilization
Availability
Heating
Costs

Keywords

  • Artificial neural network
  • Demand flexibility
  • Economic model predictive control
  • Experimental case study
  • Optimal control
  • Residential building

Cite this

@article{be50531371ed41b081978cff281820d0,
title = "Economic model predictive control for demand flexibility of a residential building",
abstract = "Future building energy management systems will have to be capable of adapting to variation in the rate of production of energy from renewable sources. Controllers employing a model predictive control (MPC) framework can optimise and schedule energy usage based on the availability of renewably generated energy. In this paper, an MPC using artificial neural networks (ANNs) was implemented in a residential building. The ANN-MPC was successfully tested and demonstrated good performance predicting the building's energy consumption. The controller was then modified to function as an economic MPC (EMPC) to optimise demand flexibility (i.e., the ability to adapt energy demands to fluctuations in supply). The operational costs of energy usage were associated with this demand flexibility, which was represented by three flexibility indicators: flexibility factor, supply cover factor, and load cover factor. The results from a day-long test showed that these flexibility indicators were maximised (flexibility factor ranged from −0.88 to 0.67, supply cover factor from 0.04 to 0.13, and load cover factor from 0.07 to 0.16) when the EMPC controller's demand flexibility was compared to that of a conventional proportional-integral (PI) controller. The EMPC framework for demand flexibility can be used to regulate on-site energy generation, grid consumption, and grid feed-in and can thus serve as a basis for overall optimisation of the operation of heating systems to achieve greater demand flexibility.",
keywords = "Artificial neural network, Demand flexibility, Economic model predictive control, Experimental case study, Optimal control, Residential building",
author = "Christian Finck and Rongling Li and Wim Zeiler",
year = "2019",
month = "6",
day = "1",
doi = "10.1016/j.energy.2019.03.171",
language = "English",
volume = "176",
pages = "365--379",
journal = "Energy",
issn = "0360-5442",
publisher = "Elsevier",

}

Economic model predictive control for demand flexibility of a residential building. / Finck, Christian (Corresponding author); Li, Rongling; Zeiler, Wim.

In: Energy, Vol. 176, 01.06.2019, p. 365-379.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Economic model predictive control for demand flexibility of a residential building

AU - Finck, Christian

AU - Li, Rongling

AU - Zeiler, Wim

PY - 2019/6/1

Y1 - 2019/6/1

N2 - Future building energy management systems will have to be capable of adapting to variation in the rate of production of energy from renewable sources. Controllers employing a model predictive control (MPC) framework can optimise and schedule energy usage based on the availability of renewably generated energy. In this paper, an MPC using artificial neural networks (ANNs) was implemented in a residential building. The ANN-MPC was successfully tested and demonstrated good performance predicting the building's energy consumption. The controller was then modified to function as an economic MPC (EMPC) to optimise demand flexibility (i.e., the ability to adapt energy demands to fluctuations in supply). The operational costs of energy usage were associated with this demand flexibility, which was represented by three flexibility indicators: flexibility factor, supply cover factor, and load cover factor. The results from a day-long test showed that these flexibility indicators were maximised (flexibility factor ranged from −0.88 to 0.67, supply cover factor from 0.04 to 0.13, and load cover factor from 0.07 to 0.16) when the EMPC controller's demand flexibility was compared to that of a conventional proportional-integral (PI) controller. The EMPC framework for demand flexibility can be used to regulate on-site energy generation, grid consumption, and grid feed-in and can thus serve as a basis for overall optimisation of the operation of heating systems to achieve greater demand flexibility.

AB - Future building energy management systems will have to be capable of adapting to variation in the rate of production of energy from renewable sources. Controllers employing a model predictive control (MPC) framework can optimise and schedule energy usage based on the availability of renewably generated energy. In this paper, an MPC using artificial neural networks (ANNs) was implemented in a residential building. The ANN-MPC was successfully tested and demonstrated good performance predicting the building's energy consumption. The controller was then modified to function as an economic MPC (EMPC) to optimise demand flexibility (i.e., the ability to adapt energy demands to fluctuations in supply). The operational costs of energy usage were associated with this demand flexibility, which was represented by three flexibility indicators: flexibility factor, supply cover factor, and load cover factor. The results from a day-long test showed that these flexibility indicators were maximised (flexibility factor ranged from −0.88 to 0.67, supply cover factor from 0.04 to 0.13, and load cover factor from 0.07 to 0.16) when the EMPC controller's demand flexibility was compared to that of a conventional proportional-integral (PI) controller. The EMPC framework for demand flexibility can be used to regulate on-site energy generation, grid consumption, and grid feed-in and can thus serve as a basis for overall optimisation of the operation of heating systems to achieve greater demand flexibility.

KW - Artificial neural network

KW - Demand flexibility

KW - Economic model predictive control

KW - Experimental case study

KW - Optimal control

KW - Residential building

UR - http://www.scopus.com/inward/record.url?scp=85064194349&partnerID=8YFLogxK

U2 - 10.1016/j.energy.2019.03.171

DO - 10.1016/j.energy.2019.03.171

M3 - Article

AN - SCOPUS:85064194349

VL - 176

SP - 365

EP - 379

JO - Energy

JF - Energy

SN - 0360-5442

ER -