Virtual Power Plant Day Ahead Energy Unit Commitment

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dc.contributor.authorArias, Andres Felipe
dc.contributor.authorLamadrid, Alberto
dc.contributor.authorValencia, Carlos
dc.date.accessioned2021-12-24T17:49:36Z
dc.date.available2021-12-24T17:49:36Z
dc.date.issued2022-01-04
dc.description.abstractIn this article we present a model for the interaction of distributed energy resources (DER) with the electricity system, using reinforcement learning. Our method relaxes the requirements for information necessary to train and engage in Pareto improving trading, and can directly incorporate the inherent intermittency of variable renewable energy sources. The distributed resources include consumers of electricity, energy storage systems, and variable renewable energy. We modify the algorithms to improve the scheduling of the resources. In our empirical application, we use data from Colombia subject to large variability due to El NiƱo Southern Oscillation and illustrate the use of the model under large variations in the data used to train the model.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2022.421
dc.identifier.isbn978-0-9981331-5-7
dc.identifier.urihttp://hdl.handle.net/10125/79756
dc.language.isoeng
dc.relation.ispartofProceedings of the 55th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectDistributed, Renewable, and Mobile Resources
dc.subjectconstrained cross entropy
dc.subjectcopula autoregressive
dc.subjectday-ahead unit commitment
dc.subjectdeep reinforcement learning
dc.subjectvariable renewable energy sources
dc.titleVirtual Power Plant Day Ahead Energy Unit Commitment
dc.type.dcmitext

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