Solving the Time-Ahead Pricing of Energy Supply Problem with Nonlinear Utility Function for Consumers via a Bilevel Optimization Approach

Coppta Team's Technical Report of the 20014 AIMMS-MOPTA Optimization Modeling Competition (http://coral.ise.lehigh.edu/mopta2014/competition/)

Autores:
Huertas, Jorge A.
Eslava, Daniel M.
Pardo, Andrés F.
González, Jaime E.
Tipo de recurso:
Work document
Fecha de publicación:
2014
Institución:
Universidad de los Andes
Repositorio:
Séneca: repositorio Uniandes
Idioma:
eng
OAI Identifier:
oai:repositorio.uniandes.edu.co:1992/31241
Acceso en línea:
http://hdl.handle.net/1992/31241
Palabra clave:
Energy
Bilevel
Non-linear
Piecewise linearization
Rights
openAccess
License
https://repositorio.uniandes.edu.co/static/pdf/aceptacion_uso_es.pdf
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dc.title.es_CO.fl_str_mv Solving the Time-Ahead Pricing of Energy Supply Problem with Nonlinear Utility Function for Consumers via a Bilevel Optimization Approach
title Solving the Time-Ahead Pricing of Energy Supply Problem with Nonlinear Utility Function for Consumers via a Bilevel Optimization Approach
spellingShingle Solving the Time-Ahead Pricing of Energy Supply Problem with Nonlinear Utility Function for Consumers via a Bilevel Optimization Approach
Energy
Bilevel
Non-linear
Piecewise linearization
title_short Solving the Time-Ahead Pricing of Energy Supply Problem with Nonlinear Utility Function for Consumers via a Bilevel Optimization Approach
title_full Solving the Time-Ahead Pricing of Energy Supply Problem with Nonlinear Utility Function for Consumers via a Bilevel Optimization Approach
title_fullStr Solving the Time-Ahead Pricing of Energy Supply Problem with Nonlinear Utility Function for Consumers via a Bilevel Optimization Approach
title_full_unstemmed Solving the Time-Ahead Pricing of Energy Supply Problem with Nonlinear Utility Function for Consumers via a Bilevel Optimization Approach
title_sort Solving the Time-Ahead Pricing of Energy Supply Problem with Nonlinear Utility Function for Consumers via a Bilevel Optimization Approach
dc.creator.fl_str_mv Huertas, Jorge A.
Eslava, Daniel M.
Pardo, Andrés F.
González, Jaime E.
dc.contributor.author.none.fl_str_mv Huertas, Jorge A.
Eslava, Daniel M.
Pardo, Andrés F.
González, Jaime E.
dc.subject.keyword.es_CO.fl_str_mv Energy
Bilevel
Non-linear
Piecewise linearization
topic Energy
Bilevel
Non-linear
Piecewise linearization
description Coppta Team's Technical Report of the 20014 AIMMS-MOPTA Optimization Modeling Competition (http://coral.ise.lehigh.edu/mopta2014/competition/)
publishDate 2014
dc.date.issued.none.fl_str_mv 2014-12
dc.date.accessioned.none.fl_str_mv 2019-10-05T17:07:10Z
dc.date.available.none.fl_str_mv 2019-10-05T17:07:10Z
dc.type.spa.fl_str_mv Documento de trabajo
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dc.language.iso.es_CO.fl_str_mv eng
language eng
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spelling Al consultar y hacer uso de este recurso, está aceptando las condiciones de uso establecidas por los autores.https://repositorio.uniandes.edu.co/static/pdf/aceptacion_uso_es.pdfinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Huertas, Jorge A.6724d1d1-9e26-4c1b-89ac-52fcd662e010600Eslava, Daniel M.c9a8722d-46f1-4f73-b5b0-523d91941e13600Pardo, Andrés F.ee948573-5136-4425-b0a4-7f58c32d96ef600González, Jaime E.ed9a5d0f-ad9d-46e1-9df1-27960d349dde600Bogotá2019-10-05T17:07:10Z2019-10-05T17:07:10Z2014-12http://hdl.handle.net/1992/31241instname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/Coppta Team's Technical Report of the 20014 AIMMS-MOPTA Optimization Modeling Competition (http://coral.ise.lehigh.edu/mopta2014/competition/)The time-ahead pricing of energy supply problem is one decision-making process within the management of the distribution electricity system. Specifically, this problem aims to determine how to price the energy for end consumers. We present a bilevel optimization strategy to solve the problem taking into account that given the price of energy at a period of time, the end customers have the capacity to change their energy consumption behavior. Additionally, we consider multiple consumption profiles to segment the customers adding flexibility in the model for the decision maker. Furthermore, we solve nonlinear optimization models embedded in the bilevel optimization approach through piecewise linearization. Finally, we test our solution strategy retrieving available historical data from public sources.22application/pdfenginstname:Universidad de los Andesreponame:SénecaSolving the Time-Ahead Pricing of Energy Supply Problem with Nonlinear Utility Function for Consumers via a Bilevel Optimization ApproachDocumento de trabajodraftinfo:eu-repo/semantics/workingPaperhttp://purl.org/coar/resource_type/c_8042http://purl.org/coar/version/c_b1a7d7d4d402bccehttp://purl.org/coar/version/c_970fb48d4fbd8a85Texthttps://purl.org/redcol/resource_type/WPEnergyBilevelNon-linearPiecewise linearizationPublicationLICENSElicense.txtlicense.txttext/plain; charset=utf-81865https://repositorio.uniandes.edu.co/bitstreams/8ffc7028-2825-481c-99e9-7879acdeba22/download3712501b71477eef138f931c5a7aac67MD53TEXTSolving the Time-Ahead Pricing.pdf.txtSolving the Time-Ahead Pricing.pdf.txtExtracted texttext/plain45045https://repositorio.uniandes.edu.co/bitstreams/1b692a63-2afa-42f5-8096-059943c07c42/download431eb4dd8ce7bc4e67c2ed8e1102b8bcMD57THUMBNAILSolving the Time-Ahead Pricing.pdf.jpgSolving the Time-Ahead Pricing.pdf.jpgIM Thumbnailimage/jpeg11417https://repositorio.uniandes.edu.co/bitstreams/bee1be4c-2e18-49f1-a425-96c8152297d2/download1c0357ebb4375c7c2cdd28f5d23867c3MD58ORIGINALSolving the Time-Ahead Pricing.pdfSolving the Time-Ahead Pricing.pdfSolving the Time-Ahead Pricingapplication/pdf838894https://repositorio.uniandes.edu.co/bitstreams/97b69645-b317-45a0-8a67-a648d05f3b07/download8de84f3aa79520b4c6912694354f6763MD541992/31241oai:repositorio.uniandes.edu.co:1992/312412023-10-10 19:16:53.392https://repositorio.uniandes.edu.co/static/pdf/aceptacion_uso_es.pdfopen.accesshttps://repositorio.uniandes.edu.coRepositorio institucional Sénecaadminrepositorio@uniandes.edu.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