Distributed Residential Demand Response Using Building Mass and Electric Thermal Storage System

The increment of energy demand in residential electric networks increases the energy cost and reduces the operating reliability. This behavior is accentuated in cold climate regions where most of the consumers use electric space heating systems. In this context, thermal energy storage systems can be...

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Bibliographic Details
Main Authors: Aliabadi, Farshad Etedadi, Agbossou, Kodjo, Henao, Nilson, Kelouwani, Sousso, Laurencelle, Francois
Format: Conference Proceeding
Language:English
Subjects:
Buildings
Costs
Demand response
Dynamic scheduling
electric thermal storage
home energy management
passive building thermal storage
Peak to average power ratio
smart grids
Thermal engineering
Thermal management
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Summary:The increment of energy demand in residential electric networks increases the energy cost and reduces the operating reliability. This behavior is accentuated in cold climate regions where most of the consumers use electric space heating systems. In this context, thermal energy storage systems can be used as flexibility sources to relieve power grid stress. However, it is necessary to design an effective demand response technique to take advantage of the maximum potential of thermal storage systems. This research aims to propose and evaluate a distributed demand response approach to control integrated passive building thermal storage and electric thermal storage systems (utilizing bricks as storage means). The proposed approach is implemented as a dynamic price-based and day-ahead scheduling optimization problem based on the game theory concept. The formulation expresses the consumer's participation willingness in the demand response program and their associated financial incentives. A simulation is carried out to examine the electrical/thermal behavior of the system and reveal the effectiveness of the proposed method in flattening the aggregated demand profile, reducing costs, and ensuring the households' comfort. The numerical results show the capability of the proposed algorithm to improve the peak-to-average ratio by 57.36% and decrease the peak demand by 57.3%, while reducing the total electricity cost by 24.19% and increasing the share of distributed energy resources.
ISSN:2575-2693
DOI:10.1109/SEGE55279.2022.9889758