Modeling of suppliers' learning behaviors in an electricity market environment

dc.contributor.advisor Chen-Ching Liu
dc.contributor.author Yu, Nanpeng
dc.contributor.department Electrical and Computer Engineering
dc.date 2018-08-22T21:36:27.000
dc.date.accessioned 2020-06-30T07:38:14Z
dc.date.available 2020-06-30T07:38:14Z
dc.date.copyright Mon Jan 01 00:00:00 UTC 2007
dc.date.issued 2007-01-01
dc.description.abstract <p>The Day-Ahead electricity market is modeled as a multi-agent system with interacting agents including supplier agents, Load Serving Entities, and a Market Operator. Simulation of the market clearing results under the scenario in which agents have learning capabilities is compared with the scenario where agents report true marginal costs. It is shown that, with Q-Learning, electricity suppliers are making more profits compared to the scenario without learning due to strategic gaming. As a result, the LMP at each bus is substantially higher.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/rtd/14641/
dc.identifier.articleid 15640
dc.identifier.contextkey 6997439
dc.identifier.doi https://doi.org/10.31274/rtd-180813-15826
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath rtd/14641
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/68190
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/rtd/14641/1447491.PDF|||Fri Jan 14 20:23:55 UTC 2022
dc.subject.disciplines Electrical and Electronics
dc.subject.keywords Electrical and computer engineering;Electrical engineering
dc.title Modeling of suppliers' learning behaviors in an electricity market environment
dc.type article
dc.type.genre thesis
dspace.entity.type Publication
relation.isOrgUnitOfPublication a75a044c-d11e-44cd-af4f-dab1d83339ff
thesis.degree.level thesis
thesis.degree.name Master of Science
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