Robust energy systems scheduling considering uncertainties and demand side emission impacts

Wang, Y; Qiu, J; Tao, Y

Robust energy systems scheduling considering uncertainties and demand side emission impacts

Wang, Y

Qiu, J Tao, Y

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Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Energy, 2022, 239
Issue Date:: 2022-01-15

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Field	Value	Language
dc.contributor.author	Wang, Y https://orcid.org/0000-0003-1013-5497
dc.contributor.author	Qiu, J
dc.contributor.author	Tao, Y
dc.date.accessioned	2023-04-04T06:21:42Z
dc.date.available	2023-04-04T06:21:42Z
dc.date.issued	2022-01-15
dc.identifier.citation	Energy, 2022, 239
dc.identifier.issn	0360-5442
dc.identifier.issn	1873-6785
dc.identifier.uri	http://hdl.handle.net/10453/169109
dc.description.abstract	Distributed Companies (DISCOs) with distributed energy resources (DERs) are considered as one of the most flexible forms of power supply towards decarbonization society. However, to achieve this low-carbon goal, it would not be effective without appropriate incentives and the effective demand side involvement. Meanwhile, uncertainties in the system operation such as outputs of intermittent renewable energy, electric vehicles (EVs), and demand side behaviors introduce difficulty for scheduling, and further influence the system's carbon emission. This pa-per proposes a two-stage model to investigate the dynamic carbon impacts caused by the above uncertainties to system scheduling through active demand side from both the economic aspect and environmental aspect. The carbon impacts are reflected upon a defined integrated selling price of DISCO based on the result of carbon emission flow (CEF) and real-time carbon price from the carbon trading market. The uncertainties are modeled as individual probability distributions, and the electricity consumption is forecasted by the least absolute shrinkage and selection operator (LASSO)-quantile regression neural network (QRNN). To address the system uncertainties, a probability-weighted robust optimization (PWRO) approach is applied. Case studies are tested on a modified IEEE-33 bus system, the simulation result indicates that the proposed model with PWRO can effectively handle uncertainties, while a fairer bill plan for customers towards a low-carbon economy can be obtained.
dc.language	English
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartof	Energy
dc.relation.isbasedon	10.1016/j.energy.2021.122317
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0913 Mechanical Engineering, 0914 Resources Engineering and Extractive Metallurgy, 0915 Interdisciplinary Engineering
dc.subject.classification	Energy
dc.title	Robust energy systems scheduling considering uncertainties and demand side emission impacts
dc.type	Journal Article
utslib.citation.volume	239
utslib.for	0913 Mechanical Engineering
utslib.for	0914 Resources Engineering and Extractive Metallurgy
utslib.for	0915 Interdisciplinary 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	2023-04-04T06:21:40Z
pubs.publication-status	Published
pubs.volume	239

Abstract:

Distributed Companies (DISCOs) with distributed energy resources (DERs) are considered as one of the most flexible forms of power supply towards decarbonization society. However, to achieve this low-carbon goal, it would not be effective without appropriate incentives and the effective demand side involvement. Meanwhile, uncertainties in the system operation such as outputs of intermittent renewable energy, electric vehicles (EVs), and demand side behaviors introduce difficulty for scheduling, and further influence the system's carbon emission. This pa-per proposes a two-stage model to investigate the dynamic carbon impacts caused by the above uncertainties to system scheduling through active demand side from both the economic aspect and environmental aspect. The carbon impacts are reflected upon a defined integrated selling price of DISCO based on the result of carbon emission flow (CEF) and real-time carbon price from the carbon trading market. The uncertainties are modeled as individual probability distributions, and the electricity consumption is forecasted by the least absolute shrinkage and selection operator (LASSO)-quantile regression neural network (QRNN). To address the system uncertainties, a probability-weighted robust optimization (PWRO) approach is applied. Case studies are tested on a modified IEEE-33 bus system, the simulation result indicates that the proposed model with PWRO can effectively handle uncertainties, while a fairer bill plan for customers towards a low-carbon economy can be obtained.

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http://hdl.handle.net/10453/169109