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Author |
Srivastava, A.; Van Passel, S.; Laes, E. |
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Title |
Dissecting demand response : a quantile analysis of flexibility, household attitudes, and demographics |
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A1 Journal article |
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Year  |
2019 |
Publication |
Energy Research and Social Science |
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Volume |
52 |
Issue |
52 |
Pages |
169-180 |
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Keywords |
A1 Journal article; Economics; Engineering Management (ENM) |
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Abstract |
Demand response (DR) can aid with grid integration of renewables, ensuring security of supply, and reducing generation costs. However, not enough is known about how residential customers’ perceptions of DR shape their response to such programs. This paper offers a deeper understanding of – and reveals the heterogeneity in – this relationship by conducting a quantile regression analysis of a Belgian DR trial, combining data on response with information on household attitudes towards smart appliances. Results overall suggest that improving response requires subtle shifts in electricity consumption behaviour, which can be achieved through changes in user perceptions. Specifically, if customers are inclined to be flexible, a stronger perception of smart appliances as being beneficial can greatly improve response. With those who are less flexible, the cost of smart appliances is a bigger concern. Thus, when designing DR programs, policymakers should aim to promote modest behaviour changes – so as to minimise inconvenience – in customers, by improving awareness on the benefits of smart appliances. Uptake of such DR programs may be improved by explaining the financial benefits or offering incentives to less flexible population segments. Lastly, improving response among older population segments will require a deeper investigation into their concerns. |
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Wos |
000468215900016 |
Publication Date |
2019-03-04 |
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ISSN |
2214-6296 |
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Additional Links |
UA library record; WoS full record |
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Times cited |
1 |
Open Access |
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Notes |
; This work continued on the results and data of the project Linear that was supported by the Flemish Ministry of Science and organised by the Institute for Science and Technology (IWT). The authors gratefully acknowledge the support extended by Wim Cardinaels at VITO in helping them access the underlying Linear data. ; |
Approved |
Most recent IF: NA |
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Call Number |
UA @ admin @ c:irua:158910 |
Serial |
6183 |
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| Permanent link to this record |
