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Conference Paper: Optimal bidding strategy for demand response aggregator in day-ahead markets via stochastic programming and robust optimization

TitleOptimal bidding strategy for demand response aggregator in day-ahead markets via stochastic programming and robust optimization
Authors
KeywordsDay-ahead markets
Demand response aggregator
Price uncertainties
Robust optimization
Scenario-based stochastic programming
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?
AbstractThis paper evaluates the optimal bidding strategy for demand response (DR) aggregator in day-ahead (DA) markets. Because of constraint of minimum power quantity requirement, small-sized customers have to become indirect participants of electricity markets via the DR aggregator, who could offer various contracts accessing customers' demand reduction capacity in advance. In day-ahead markets, DR aggregator schedules those contracts and submits accumulated DR offers to the system operator. The objective is to maximize the profit of the DR aggregator. The key element affecting the bidding decision and aggregator's profit is the uncertain hourly DA prices. The stochastic programming adopts scenario-based approach for helping the profit-seeking DR aggregator control uncertainties. Robust optimization employs forecast values with bounded price intervals to address uncertainties while adjusting the robustness of the solution flexibly. Both scenarios can be modelled as mixed-integer linear programming (MILP) problems which could be solved by available solvers.
Persistent Identifierhttp://hdl.handle.net/10722/216363
ISBN

 

DC FieldValueLanguage
dc.contributor.authorWei, M-
dc.contributor.authorZhong, J-
dc.date.accessioned2015-09-18T05:25:09Z-
dc.date.available2015-09-18T05:25:09Z-
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/10722/216363-
dc.description.abstractThis paper evaluates the optimal bidding strategy for demand response (DR) aggregator in day-ahead (DA) markets. Because of constraint of minimum power quantity requirement, small-sized customers have to become indirect participants of electricity markets via the DR aggregator, who could offer various contracts accessing customers' demand reduction capacity in advance. In day-ahead markets, DR aggregator schedules those contracts and submits accumulated DR offers to the system operator. The objective is to maximize the profit of the DR aggregator. The key element affecting the bidding decision and aggregator's profit is the uncertain hourly DA prices. The stochastic programming adopts scenario-based approach for helping the profit-seeking DR aggregator control uncertainties. Robust optimization employs forecast values with bounded price intervals to address uncertainties while adjusting the robustness of the solution flexibly. Both scenarios can be modelled as mixed-integer linear programming (MILP) problems which could be solved by available solvers.-
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.subjectDay-ahead markets-
dc.subjectDemand response aggregator-
dc.subjectPrice uncertainties-
dc.subjectRobust optimization-
dc.subjectScenario-based stochastic programming-
dc.titleOptimal bidding strategy for demand response aggregator in day-ahead markets via stochastic programming and robust optimization-
dc.typeConference_Paper-
dc.identifier.emailZhong, J: jinzhong@hkucc.hku.hk-
dc.identifier.authorityZhong, J=rp00212-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/EEM.2015.7216732-
dc.identifier.hkuros250721-
dc.identifier.spage1-
dc.identifier.epage5-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 151022-

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