A Methodology for the Enhancement of the Energy Flexibility and Contingency Response of a Building Through Predictive Control of Passive and Active Storage
Optimal management of thermal energy storage in a building is essential to provide predictable energy flexibility to a smart grid. Active technologies such as Electric Thermal Storage (ETS) can assist in building heating load management and can complement the building’s passive thermal storage capac...
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doaj-395b312d678b417aa226f5b822daaeaf2021-03-04T00:05:28ZengMDPI AGEnergies1996-10732021-03-01141387138710.3390/en14051387A Methodology for the Enhancement of the Energy Flexibility and Contingency Response of a Building Through Predictive Control of Passive and Active StorageJennifer Date0José A. Candanedo1Andreas K. Athienitis2Department of Building, Civil and Environmental Engineering, Concordia University, Montreal, QC H3G 1M8, CanadaDepartment of Building, Civil and Environmental Engineering, Concordia University, Montreal, QC H3G 1M8, CanadaDepartment of Building, Civil and Environmental Engineering, Concordia University, Montreal, QC H3G 1M8, CanadaOptimal management of thermal energy storage in a building is essential to provide predictable energy flexibility to a smart grid. Active technologies such as Electric Thermal Storage (ETS) can assist in building heating load management and can complement the building’s passive thermal storage capacity. The presented paper outlines a methodology that utilizes the concept of Building Energy Flexibility Index (BEFI) and shows that implementing Model Predictive Control (MPC) with dedicated thermal storage can provide predictable energy flexibility to the grid during critical times. When the utility notifies the customer 12 h before a Demand Response (DR) event, a BEFI up to 65 kW (100% reduction) can be achieved. A dynamic rate structure as the objective function is shown to be successful in reducing the peak demand, while a greater reduction in energy consumption in a 24-hour period is seen with a rate structure with a demand charge. Contingency reserve participation was also studied and strategies included reducing the zone temperature setpoint by 2<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mo>∘</mo></msup></semantics></math></inline-formula>C for 3 h or using the stored thermal energy by discharging the device for 3 h. Favourable results were found for both options, where a BEFI of up to 47 kW (96%) is achieved. The proposed methodology for modeling and evaluation of control strategies is suitable for other similar convectively conditioned buildings equipped with active and passive storage.https://www.mdpi.com/1996-1073/14/5/1387energy flexibilitypredictive controlthermal storageactive storagepassive storagecontingency reserve |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jennifer Date José A. Candanedo Andreas K. Athienitis |
spellingShingle |
Jennifer Date José A. Candanedo Andreas K. Athienitis A Methodology for the Enhancement of the Energy Flexibility and Contingency Response of a Building Through Predictive Control of Passive and Active Storage Energies energy flexibility predictive control thermal storage active storage passive storage contingency reserve |
author_facet |
Jennifer Date José A. Candanedo Andreas K. Athienitis |
author_sort |
Jennifer Date |
title |
A Methodology for the Enhancement of the Energy Flexibility and Contingency Response of a Building Through Predictive Control of Passive and Active Storage |
title_short |
A Methodology for the Enhancement of the Energy Flexibility and Contingency Response of a Building Through Predictive Control of Passive and Active Storage |
title_full |
A Methodology for the Enhancement of the Energy Flexibility and Contingency Response of a Building Through Predictive Control of Passive and Active Storage |
title_fullStr |
A Methodology for the Enhancement of the Energy Flexibility and Contingency Response of a Building Through Predictive Control of Passive and Active Storage |
title_full_unstemmed |
A Methodology for the Enhancement of the Energy Flexibility and Contingency Response of a Building Through Predictive Control of Passive and Active Storage |
title_sort |
methodology for the enhancement of the energy flexibility and contingency response of a building through predictive control of passive and active storage |
publisher |
MDPI AG |
series |
Energies |
issn |
1996-1073 |
publishDate |
2021-03-01 |
description |
Optimal management of thermal energy storage in a building is essential to provide predictable energy flexibility to a smart grid. Active technologies such as Electric Thermal Storage (ETS) can assist in building heating load management and can complement the building’s passive thermal storage capacity. The presented paper outlines a methodology that utilizes the concept of Building Energy Flexibility Index (BEFI) and shows that implementing Model Predictive Control (MPC) with dedicated thermal storage can provide predictable energy flexibility to the grid during critical times. When the utility notifies the customer 12 h before a Demand Response (DR) event, a BEFI up to 65 kW (100% reduction) can be achieved. A dynamic rate structure as the objective function is shown to be successful in reducing the peak demand, while a greater reduction in energy consumption in a 24-hour period is seen with a rate structure with a demand charge. Contingency reserve participation was also studied and strategies included reducing the zone temperature setpoint by 2<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mo>∘</mo></msup></semantics></math></inline-formula>C for 3 h or using the stored thermal energy by discharging the device for 3 h. Favourable results were found for both options, where a BEFI of up to 47 kW (96%) is achieved. The proposed methodology for modeling and evaluation of control strategies is suitable for other similar convectively conditioned buildings equipped with active and passive storage. |
topic |
energy flexibility predictive control thermal storage active storage passive storage contingency reserve |
url |
https://www.mdpi.com/1996-1073/14/5/1387 |
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