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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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/) |
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2014 |
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2019-10-05T17:07:10Z |
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2019-10-05T17:07:10Z |
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http://purl.org/coar/version/c_b1a7d7d4d402bcce http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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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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 |