Maximum demand flexibility from the demand response of a big group of residential homes

Wu, J; Li, L; Zhang, J

Maximum demand flexibility from the demand response of a big group of residential homes

Wu, J Li, L

Zhang, J

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Publisher:: ELSEVIER SCI LTD
Publication Type:: Journal Article
Citation:: International Journal of Electrical Power and Energy Systems, 2023, 147
Issue Date:: 2023-05-01

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Field	Value	Language
dc.contributor.author	Wu, J
dc.contributor.author	Li, L https://orcid.org/0000-0001-8485-2216
dc.contributor.author	Zhang, J
dc.date.accessioned	2024-02-01T03:40:30Z
dc.date.available	2024-02-01T03:40:30Z
dc.date.issued	2023-05-01
dc.identifier.citation	International Journal of Electrical Power and Energy Systems, 2023, 147
dc.identifier.issn	0142-0615
dc.identifier.issn	1879-3517
dc.identifier.uri	http://hdl.handle.net/10453/175211
dc.description.abstract	Residential demand response (DR) programs are designed to reduce peak load from residential homes. It is essential for utilities to identify how much peak load could be potentially reduced from DR to optimally plan and operate the power system. However, it is extremely challenging to identify such a maximum load reduction, i.e., demand flexibility, offered by DR for a large group of residential homes due to the difficulties in modeling the willingness of customers to participate in DR programs and diverse loads in households. The willingness is also related to the incentives; a higher incentive can obviously attract more customers to participate. Targeting these problems, this paper provides an electricity cost minimization model for residential homes. DR incentives and customer willingness to respond to incentives are modeled to identify the realistic maximum demand flexibility. A case study using actual data from 98 homes at the Pecan Street microgrid database shows 9.7% of the controllable peak load (43 kW) can be reduced when the DR incentive is half of the peak electricity tariff. This demand flexibility increases to 66 kW, accounting for 14.9% of the controllable peak loads, when the incentive increases to twice of the peak tariff.
dc.language	English
dc.publisher	ELSEVIER SCI LTD
dc.relation.ispartof	International Journal of Electrical Power and Energy Systems
dc.relation.isbasedon	10.1016/j.ijepes.2022.108800
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0906 Electrical and Electronic Engineering
dc.subject.classification	Energy
dc.subject.classification	4008 Electrical engineering
dc.subject.classification	4009 Electronics, sensors and digital hardware
dc.subject.classification	4601 Applied computing
dc.title	Maximum demand flexibility from the demand response of a big group of residential homes
dc.type	Journal Article
utslib.citation.volume	147
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	embargoed	*
utslib.copyright.embargo	2025-05-01T00:00:00+1000Z
dc.date.updated	2024-02-01T03:40:28Z
pubs.publication-status	Published
pubs.volume	147

Abstract:

Residential demand response (DR) programs are designed to reduce peak load from residential homes. It is essential for utilities to identify how much peak load could be potentially reduced from DR to optimally plan and operate the power system. However, it is extremely challenging to identify such a maximum load reduction, i.e., demand flexibility, offered by DR for a large group of residential homes due to the difficulties in modeling the willingness of customers to participate in DR programs and diverse loads in households. The willingness is also related to the incentives; a higher incentive can obviously attract more customers to participate. Targeting these problems, this paper provides an electricity cost minimization model for residential homes. DR incentives and customer willingness to respond to incentives are modeled to identify the realistic maximum demand flexibility. A case study using actual data from 98 homes at the Pecan Street microgrid database shows 9.7% of the controllable peak load (43 kW) can be reduced when the DR incentive is half of the peak electricity tariff. This demand flexibility increases to 66 kW, accounting for 14.9% of the controllable peak loads, when the incentive increases to twice of the peak tariff.

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http://hdl.handle.net/10453/175211