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A long-term risk management tool for electricity markets using swarm intelligence

dc.contributor.authorAzevedo, Filipe
dc.contributor.authorVale, Zita
dc.contributor.authorOliveira, P. B. Moura
dc.contributor.authorKhodr, H. M.
dc.date.accessioned2013-05-16T10:30:50Z
dc.date.available2013-05-16T10:30:50Z
dc.date.issued2010
dc.date.updated2013-04-17T14:47:02Z
dc.description.abstractThis paper addresses the optimal involvement in derivatives electricity markets of a power producer to hedge against the pool price volatility. To achieve this aim, a swarm intelligence meta-heuristic optimization technique for long-term risk management tool is proposed. This tool investigates the long-term opportunities for risk hedging available for electric power producers through the use of contracts with physical (spot and forward contracts) and financial (options contracts) settlement. The producer risk preference is formulated as a utility function (U) expressing the trade-off between the expectation and the variance of the return. Variance of return and the expectation are based on a forecasted scenario interval determined by a long-term price range forecasting model. This model also makes use of particle swarm optimization (PSO) to find the best parameters allow to achieve better forecasting results. On the other hand, the price estimation depends on load forecasting. This work also presents a regressive long-term load forecast model that make use of PSO to find the best parameters as well as in price estimation. The PSO technique performance has been evaluated by comparison with a Genetic Algorithm (GA) based approach. A case study is presented and the results are discussed taking into account the real price and load historical data from mainland Spanish electricity market demonstrating the effectiveness of the methodology handling this type of problems. Finally, conclusions are dully drawn.por
dc.identifier10.1016/j.epsr.2009.10.002
dc.identifier.doi10.1016/j.epsr.2009.10.002pt_PT
dc.identifier.issn0378-7796
dc.identifier.urihttp://hdl.handle.net/10400.22/1597
dc.language.isoengpor
dc.publisherElsevierpor
dc.relation.ispartofseriesElectric Power Systems Research; Vol. 80, Issue 4
dc.relation.publisherversionhttp://www.sciencedirect.com/science/article/pii/S0378779609002338por
dc.subjectElectricity marketspor
dc.subjectLoad forecastpor
dc.subjectOptimizationpor
dc.subjectParticle swarm optimizationpor
dc.subjectPortfoliopor
dc.subjectPrice forecastpor
dc.subjectRisk managementpor
dc.titleA long-term risk management tool for electricity markets using swarm intelligencepor
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage389por
oaire.citation.issueIssue 4
oaire.citation.startPage380por
oaire.citation.titleElectric Power Systems Research
oaire.citation.volumeVol. 80
person.familyNameVale
person.givenNameZita
person.identifier632184
person.identifier.ciencia-id721B-B0EB-7141
person.identifier.orcid0000-0002-4560-9544
person.identifier.ridA-5824-2012
person.identifier.scopus-author-id7004115775
rcaap.rightsopenAccesspor
rcaap.typearticlepor
relation.isAuthorOfPublicationff1df02d-0c0f-4db1-bf7d-78863a99420b
relation.isAuthorOfPublication.latestForDiscoveryff1df02d-0c0f-4db1-bf7d-78863a99420b

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