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Conference Paper: Scenario-based real-time demand response considering wind power and price uncertainty

TitleScenario-based real-time demand response considering wind power and price uncertainty
Authors
KeywordsConsumer utility
Demand response
Real-time pricing
Stochastic optimization
Wind power
Issue Date2015
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1002121
Citation
The 12th International Conference on the European Energy Market (EEM 2015), Lisbon, Portugal, 19-22 May 2015. In Conference Proceedings, 2015, p. 1-5 How to Cite?
AbstractReal-time pricing can potentially lead to economic advantages for consumers in the environment of smart grid. Compared with flat rates, dynamic pricing allows consumers more engagement through measures of demand response (DR). This paper investigated the optimal hourly electricity consumption scheduling problem of a given consumer responding real-time price. The objective of the proposed model is to maximize the surplus of a consumer that is equipped with wind power and storage devices. Hourly utility curve is considered as a function of power consumption. Bidirectional communication between the consumer and the supplier allows for interval price updates, so the consumer can flexibly adjust hourly demand. Key sources influencing final performance are price uncertainty and renewable power generation uncertainty. Uncertainties are modelled via scenario-based stochastic optimization, where its feasibility is illustrated in numerical simulations.
Persistent Identifierhttp://hdl.handle.net/10722/217359
ISBN

 

DC FieldValueLanguage
dc.contributor.authorWei, M-
dc.contributor.authorZhong, J-
dc.date.accessioned2015-09-18T05:57:22Z-
dc.date.available2015-09-18T05:57:22Z-
dc.date.issued2015-
dc.identifier.citationThe 12th International Conference on the European Energy Market (EEM 2015), Lisbon, Portugal, 19-22 May 2015. In Conference Proceedings, 2015, p. 1-5-
dc.identifier.isbn978-1-4673-6692-2-
dc.identifier.urihttp://hdl.handle.net/10722/217359-
dc.description.abstractReal-time pricing can potentially lead to economic advantages for consumers in the environment of smart grid. Compared with flat rates, dynamic pricing allows consumers more engagement through measures of demand response (DR). This paper investigated the optimal hourly electricity consumption scheduling problem of a given consumer responding real-time price. The objective of the proposed model is to maximize the surplus of a consumer that is equipped with wind power and storage devices. Hourly utility curve is considered as a function of power consumption. Bidirectional communication between the consumer and the supplier allows for interval price updates, so the consumer can flexibly adjust hourly demand. Key sources influencing final performance are price uncertainty and renewable power generation uncertainty. Uncertainties are modelled via scenario-based stochastic optimization, where its feasibility is illustrated in numerical simulations.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1002121-
dc.relation.ispartofInternational Conference on European Electricity Market (EEM)-
dc.rightsInternational Conference on European Electricity Market (EEM). Copyright © IEEE.-
dc.rights©2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectConsumer utility-
dc.subjectDemand response-
dc.subjectReal-time pricing-
dc.subjectStochastic optimization-
dc.subjectWind power-
dc.titleScenario-based real-time demand response considering wind power and price uncertainty-
dc.typeConference_Paper-
dc.identifier.emailWei, M: weiming2@hku.hk-
dc.identifier.emailZhong, J: jinzhong@hkucc.hku.hk-
dc.identifier.authorityZhong, J=rp00212-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/EEM.2015.7216740-
dc.identifier.hkuros250720-
dc.identifier.spage1-
dc.identifier.epage5-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 151022-

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