Optimal stochastic conditional value at risk-based management of a demand response aggregator considering load uncertainty

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dc.contributor.authorVahid-Ghavidel, Morteza
dc.contributor.authorJavadi, Mohammad Sadegh
dc.contributor.authorSantos, Sérgio F.
dc.contributor.authorGough, Matthew
dc.contributor.authorShafie-khah, Miadreza
dc.contributor.authorCatalão, João P. S.
dc.contributor.authorSantos, Sérgio F.
dc.date.accessioned2022-09-06T10:35:10Z
dc.date.available2022-09-06T10:35:10Z
dc.date.issued2021-11-03
dc.description.abstractThis paper models a novel demand response (DR) trading strategy. In this model, the DR aggregator obtains the DR from the end-users via two types of DR programs, i.e. a time-of-use (TOU) program and an incentive-based DR program. Then, it offers this DR to the wholesale market. Three consumer sectors, namely residential, commercial and industrial, are included in this problem. The DR program is dependent on their corresponding load profiles during the studied time horizon. This paper uses a mixed-integer linear programming (MILP) problem and it is solved using the CPLEX solver through a stochastic programming approach in GAMS. The risk measure chosen to represent the load uncertainty of the users who are participating in the DR program is Conditional Value-at-Risk (CVaR). The proposed problem is simulated and assessed through a case study of a test system. The results indicate that the industrial loads play a major role in the power system and this directly affects the DR program. Moreover, the risk-averse decision-maker in this model favors a reduced participation in the DR programs when compared to a decision-maker who is risk-neutral, since the risk-averse decision maker prefers to be more secure against uncertainties. In other words, an increase in risk factor results in a decrease in the participation rate of the consumers in DR programs.pt_PT
dc.identifier.citationVahid-Ghavidel, M., Javadi, M. S., Santos, S. F., Gough, M., Shafie-khah, M., & Catalão, J. P. S. (2021). Optimal stochastic conditional value at risk-based management of a demand response aggregator considering load uncertainty. In 2021 IEEE International Conference on Environment and Electrical Engineering and 2021 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), Bari, Italy, 7th-10th September 2021 (pp. 1-5). 10.1109/EEEIC/ICPSEurope51590.2021.9584827. Repositório Institucional UPT. http://hdl.handle.net/11328/4433pt_PT
dc.identifier.doi10.1109/EEEIC/ICPSEurope51590.2021.9584827pt_PT
dc.identifier.isbn978-1-6654-3613-7 (Electronic)
dc.identifier.urihttp://hdl.handle.net/11328/4433
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherIEEEpt_PT
dc.relationsupport by FEDER funds through COMPETE 2020 and by Portuguese funds through FCT, under POCI-01- 0145-FEDER-029803 (02/SAICT/2017)pt_PT
dc.rightsopen accesspt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectConditional value at risk (CVaR)pt_PT
dc.subjectDemand responsept_PT
dc.subjectDR aggregatorpt_PT
dc.subjectStochastic programmingpt_PT
dc.subjectRisk managementpt_PT
dc.subjectTime-of-use (TOU)pt_PT
dc.titleOptimal stochastic conditional value at risk-based management of a demand response aggregator considering load uncertaintypt_PT
dc.typeconferenceObjectpt_PT
degois.publication.firstPage1pt_PT
degois.publication.lastPage5pt_PT
degois.publication.locationBari, Italypt_PT
degois.publication.title2021 IEEE International Conference on Environment and Electrical Engineering and 2021 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)pt_PT
dspace.entity.typePublicationen
person.affiliation.nameREMIT – Research on Economics, Management and Information Technologies
person.familyNameSantos
person.givenNameSérgio F.
person.identifier.ciencia-idE41A-D6C6-E3D7
person.identifier.orcid0000-0003-3277-2833
person.identifier.ridGNM-6353-2022
person.identifier.scopus-author-id56483358000
relation.isAuthorOfPublicationd0a5755c-d682-4ead-bf2a-ce1ad6c63ede
relation.isAuthorOfPublication.latestForDiscoveryd0a5755c-d682-4ead-bf2a-ce1ad6c63ede

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