ARTIFICIAL INTELLIGENCE-BASED DEMAND-SIDE RESPONSE MANAGEMENT OF RENEWABLE ENERGY

Hanna, B; Xu, G; Wang, X; Hossain, J

ARTIFICIAL INTELLIGENCE-BASED DEMAND-SIDE RESPONSE MANAGEMENT OF RENEWABLE ENERGY

Hanna, B

Xu, G

Wang, X

Hossain, J

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Publisher:: WITPRESS LTD.
Publication Type:: Conference Proceeding
Citation:: WIT Transactions on Ecology and the Environment, 2022, 255, pp. 49-61
Issue Date:: 2022-08-01

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Field	Value	Language
dc.contributor.author	Hanna, B https://orcid.org/0000-0003-3935-7712
dc.contributor.author	Xu, G https://orcid.org/0000-0003-4493-6663
dc.contributor.author	Wang, X https://orcid.org/0000-0001-9582-3445
dc.contributor.author	Hossain, J
dc.date	2022-05-23
dc.date.accessioned	2023-03-29T21:33:24Z
dc.date.available	2023-03-29T21:33:24Z
dc.date.issued	2022-08-01
dc.identifier.citation	WIT Transactions on Ecology and the Environment, 2022, 255, pp. 49-61
dc.identifier.isbn	9781784664572
dc.identifier.issn	1746-448X
dc.identifier.issn	1743-3541
dc.identifier.uri	http://hdl.handle.net/10453/168806
dc.description.abstract	Renewable energy (RE) sources will aid in the decarbonization of the energy sector, which would assist in alleviating the negative consequences of climate change. However, using RE resources for hybrid power generation has two technological challenges, uncertainty and variability owing to RE features, making estimating generated power availability difficult. Artificial intelligence techniques have been used in a variety of applications in power systems, but demand-side response (DR) is just lately receiving major research interest. The DR is highlighted as one of the most promising ways of providing the electricity system with demand flexibility; as a result, many system operators believe that growing the scale and breadth of the DR programme is critical. There are many different sorts of demand reduction programmes, and the most common classification is dependent on who begins the demand reduction. There are two types of DR schemes: (1) price-based programmes and (2) incentive-based programmes.
dc.language	en
dc.publisher	WITPRESS LTD.
dc.relation.ispartof	WIT Transactions on Ecology and the Environment
dc.relation.ispartof	Energy Production and Management in the 21st Century V : The Quest for Sustainable Energy
dc.relation.isbasedon	10.2495/EPM220051
dc.rights	info:eu-repo/semantics/openAccess
dc.title	ARTIFICIAL INTELLIGENCE-BASED DEMAND-SIDE RESPONSE MANAGEMENT OF RENEWABLE ENERGY
dc.type	Conference Proceeding
utslib.citation.volume	255
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/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2023-03-29T21:33:23Z
pubs.finish-date	2022-05-25
pubs.publication-status	Published
pubs.start-date	2022-05-23
pubs.volume	255

Abstract:

Renewable energy (RE) sources will aid in the decarbonization of the energy sector, which would assist in alleviating the negative consequences of climate change. However, using RE resources for hybrid power generation has two technological challenges, uncertainty and variability owing to RE features, making estimating generated power availability difficult. Artificial intelligence techniques have been used in a variety of applications in power systems, but demand-side response (DR) is just lately receiving major research interest. The DR is highlighted as one of the most promising ways of providing the electricity system with demand flexibility; as a result, many system operators believe that growing the scale and breadth of the DR programme is critical. There are many different sorts of demand reduction programmes, and the most common classification is dependent on who begins the demand reduction. There are two types of DR schemes: (1) price-based programmes and (2) incentive-based programmes.

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http://hdl.handle.net/10453/168806