A hybrid prediction-based microgrid energy management strategy considering demand-side response and data interruption

Yuan, D; Lu, Z; Zhang, J; Li, X

A hybrid prediction-based microgrid energy management strategy considering demand-side response and data interruption

Yuan, D Lu, Z Zhang, J

Li, X

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Publication Type:: Journal Article
Citation:: International Journal of Electrical Power and Energy Systems, 2019, 113 pp. 139 - 153
Issue Date:: 2019-12-01

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Field	Value	Language
dc.contributor.author	Yuan, D	en_US
dc.contributor.author	Lu, Z	en_US
dc.contributor.author	Zhang, J https://orcid.org/0000-0003-2616-6722	en_US
dc.contributor.author	Li, X	en_US
dc.date.issued	2019-12-01	en_US
dc.identifier.citation	International Journal of Electrical Power and Energy Systems, 2019, 113 pp. 139 - 153	en_US
dc.identifier.issn	0142-0615	en_US
dc.identifier.uri	http://hdl.handle.net/10453/135627
dc.description.abstract	© 2019 Data interruption may cause the centralized energy management system of a microgrid (MG)to collapse. To solve this problem, a hybrid prediction-based energy management strategy is proposed in this paper to predict interrupted data for the centralized dispatching process of an MG. This hybrid prediction method is designed following a combination of model predictive control and extreme learning machine techniques. Based on the predicted data of distributed energy resources and three types of loads from demand-side response, an optimization model is formulated to minimize the operational cost. If some predicted data are interrupted during transmission, then a prospect vulnerability assessment method is applied to select a neighboring device to predict the interrupted data. In the end, an improved particle swarm optimization algorithm is proposed with the help of genetic algorithms to accelerate convergence to global optimal solutions for the proposed MG energy management problem. The effectiveness of the proposed models and solution methods is also verified by a case study.	en_US
dc.relation.ispartof	International Journal of Electrical Power and Energy Systems	en_US
dc.relation.isbasedon	10.1016/j.ijepes.2019.05.045	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject.classification	Energy	en_US
dc.title	A hybrid prediction-based microgrid energy management strategy considering demand-side response and data interruption	en_US
dc.type	Journal Article
utslib.citation.volume	113	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
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	open_access	*
utslib.copyright.embargo	2021-12-01T00:00:00+1100
pubs.publication-status	Published	en_US
pubs.volume	113	en_US

Abstract:

© 2019 Data interruption may cause the centralized energy management system of a microgrid (MG)to collapse. To solve this problem, a hybrid prediction-based energy management strategy is proposed in this paper to predict interrupted data for the centralized dispatching process of an MG. This hybrid prediction method is designed following a combination of model predictive control and extreme learning machine techniques. Based on the predicted data of distributed energy resources and three types of loads from demand-side response, an optimization model is formulated to minimize the operational cost. If some predicted data are interrupted during transmission, then a prospect vulnerability assessment method is applied to select a neighboring device to predict the interrupted data. In the end, an improved particle swarm optimization algorithm is proposed with the help of genetic algorithms to accelerate convergence to global optimal solutions for the proposed MG energy management problem. The effectiveness of the proposed models and solution methods is also verified by a case study.

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http://hdl.handle.net/10453/135627