Retracted: Optimal Stochastic Energy Management of Smart City Incorporating Transportation System and Power Grid

Parhoudeh, S; Baziar, A; Lopez, PE; Moazzen, F

Retracted: Optimal Stochastic Energy Management of Smart City Incorporating Transportation System and Power Grid

Parhoudeh, S Baziar, A Lopez, PE Moazzen, F

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Industry Applications, 2023, PP, (99), pp. 1-1
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Parhoudeh, S
dc.contributor.author	Baziar, A
dc.contributor.author	Lopez, PE
dc.contributor.author	Moazzen, F https://orcid.org/0000-0002-1834-662X
dc.date.accessioned	2024-05-15T02:59:13Z
dc.date.available	2024-05-15T02:59:13Z
dc.date.issued	2023-01-01
dc.identifier.citation	IEEE Transactions on Industry Applications, 2023, PP, (99), pp. 1-1
dc.identifier.issn	0093-9994
dc.identifier.issn	1939-9367
dc.identifier.uri	http://hdl.handle.net/10453/178972
dc.description.abstract	This paper intends to present an effective stochastic framework based energy management scheme within the smart city based on point estimate method. In this way, considering the buildings, electric transportation systems and subway electrical demands are of essential needs of the smart city. For the co-operation of the electrical infrastructure as well as the transportation system, a linear model is proposed which can model the interrelation of these structures one each other. To improve the operation of the subway system, the breaking energy is recovered to be used for the electrical demands of the smart city. To this end, the vehicle-2-grid and vehicle-2-subway schemes are employed to decrease the total cost of the smart city operation. In order to capture the uncertainty effects, a stochastic framework based on point estimate method is proposed here. The high capability and appropriate performance of the proposed model is investigated on a test smart city system.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Industry Applications
dc.relation.isbasedon	10.1109/TIA.2020.3012938
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	40 Engineering
dc.title	Retracted: Optimal Stochastic Energy Management of Smart City Incorporating Transportation System and Power Grid
dc.type	Journal Article
utslib.citation.volume	PP
utslib.for	08 Information and Computing Sciences
utslib.for	09 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	closed_access	*
dc.date.updated	2024-05-15T02:59:11Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

This paper intends to present an effective stochastic framework based energy management scheme within the smart city based on point estimate method. In this way, considering the buildings, electric transportation systems and subway electrical demands are of essential needs of the smart city. For the co-operation of the electrical infrastructure as well as the transportation system, a linear model is proposed which can model the interrelation of these structures one each other. To improve the operation of the subway system, the breaking energy is recovered to be used for the electrical demands of the smart city. To this end, the vehicle-2-grid and vehicle-2-subway schemes are employed to decrease the total cost of the smart city operation. In order to capture the uncertainty effects, a stochastic framework based on point estimate method is proposed here. The high capability and appropriate performance of the proposed model is investigated on a test smart city system.

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http://hdl.handle.net/10453/178972