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dc.contributor.author
Salazar, Eduardo J.
dc.contributor.author
Samper, Mauricio Eduardo
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dc.contributor.author
Patiño, Héctor Daniel
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dc.date.available
2024-03-25T13:48:17Z
dc.date.issued
2023-09
dc.identifier.citation
Salazar, Eduardo J.; Samper, Mauricio Eduardo; Patiño, Héctor Daniel; Dynamic customer demand management: A reinforcement learning model based on real-time pricing and incentives; Elsevier; Renewable Energy Focus; 46; 9-2023; 39-56
dc.identifier.issn
1755-0084
dc.identifier.uri
http://hdl.handle.net/11336/231464
dc.description.abstract
The demand response model proposed in this work offers a game-changing solution to the challenges posed by the unpredictability of renewable energy sources. By combining both pricing and incentives, this model significantly improves the accuracy of demand response strategies, leading to more effective modulation of customer demand. The real-time and time-of-use pricing options presented to customers incentivize them to actively increase or decrease their energy consumption, thereby contributing to the stability of the energy grid. This work also sheds light on the crucial role that characteristic parameters such as the internal or external coincidence factor play in the classification of customers using the k-means algorithm. The reinforcement learning method used in the model not only optimizes prices and incentives, but also ensures that both customers and energy distribution companies benefit equally. A sensitivity analysis of customer elasticity highlights the dynamic interplay between clustering and reinforcement learning algorithms and customer behavior, demonstrating the power and effectiveness of this model. With its innovative approach and cutting-edge techniques, this work sets a new model for demand response and makes a compelling case for the inclusion of prices and incentives in future models.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier
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dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
INCENTIVE-BASED DEMAND RESPONSE
dc.subject
K-MEANS ALGORITHM
dc.subject
PRICE-BASED DEMAND RESPONSE
dc.subject
REINFORCEMENT Q-LEARNING
dc.subject.classification
Ingeniería Eléctrica y Electrónica
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dc.subject.classification
Ingeniería Eléctrica, Ingeniería Electrónica e Ingeniería de la Información
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dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS
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dc.title
Dynamic customer demand management: A reinforcement learning model based on real-time pricing and incentives
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2024-03-25T12:24:50Z
dc.journal.volume
46
dc.journal.pagination
39-56
dc.journal.pais
Países Bajos
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dc.description.fil
Fil: Salazar, Eduardo J.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Energía Eléctrica. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; Argentina
dc.description.fil
Fil: Samper, Mauricio Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Energía Eléctrica. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; Argentina
dc.description.fil
Fil: Patiño, Héctor Daniel. Universidad Nacional de San Juan; Argentina
dc.journal.title
Renewable Energy Focus
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.ref.2023.05.004
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