Publication
A long-term risk management tool for electricity markets using swarm intelligence
| dc.contributor.author | Azevedo, Filipe | |
| dc.contributor.author | Vale, Zita | |
| dc.contributor.author | Oliveira, P. B. Moura | |
| dc.contributor.author | Khodr, H. M. | |
| dc.date.accessioned | 2013-05-16T10:30:50Z | |
| dc.date.available | 2013-05-16T10:30:50Z | |
| dc.date.issued | 2010 | |
| dc.date.updated | 2013-04-17T14:47:02Z | |
| dc.description.abstract | This 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.identifier | 10.1016/j.epsr.2009.10.002 | |
| dc.identifier.doi | 10.1016/j.epsr.2009.10.002 | pt_PT |
| dc.identifier.issn | 0378-7796 | |
| dc.identifier.uri | http://hdl.handle.net/10400.22/1597 | |
| dc.language.iso | eng | por |
| dc.publisher | Elsevier | por |
| dc.relation.ispartofseries | Electric Power Systems Research; Vol. 80, Issue 4 | |
| dc.relation.publisherversion | http://www.sciencedirect.com/science/article/pii/S0378779609002338 | por |
| dc.subject | Electricity markets | por |
| dc.subject | Load forecast | por |
| dc.subject | Optimization | por |
| dc.subject | Particle swarm optimization | por |
| dc.subject | Portfolio | por |
| dc.subject | Price forecast | por |
| dc.subject | Risk management | por |
| dc.title | A long-term risk management tool for electricity markets using swarm intelligence | por |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.endPage | 389 | por |
| oaire.citation.issue | Issue 4 | |
| oaire.citation.startPage | 380 | por |
| oaire.citation.title | Electric Power Systems Research | |
| oaire.citation.volume | Vol. 80 | |
| person.familyName | Vale | |
| person.givenName | Zita | |
| person.identifier | 632184 | |
| person.identifier.ciencia-id | 721B-B0EB-7141 | |
| person.identifier.orcid | 0000-0002-4560-9544 | |
| person.identifier.rid | A-5824-2012 | |
| person.identifier.scopus-author-id | 7004115775 | |
| rcaap.rights | openAccess | por |
| rcaap.type | article | por |
| relation.isAuthorOfPublication | ff1df02d-0c0f-4db1-bf7d-78863a99420b | |
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