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Agent-Based learning model for assessing strategic generation investments in electricity markets

The liberalization of electricity markets has significantly changed the perspective of the power generation business. Nowadays, generation companies pursue economic goals due their investment decisions are based on expectations of profitability and the risk of their alternatives. These expectations...

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Main Author: Blanco Bogado, Gerardo Alejandro (author)
Other Authors: Baum Ramos, Gabriel Fernando (author), Olsina, Fernando (author), Lopez Moscarda, Sonia Beatriz (author)
Format: article
Language:English
Published: 2017
Subjects:
5. Energía
INVESTMENT
SIMILARITY LEARNING
STRATEGIC BEHAVIOR
UNCERTAINTY
ENERGIA ELECTRICA
Online Access:http://hdl.handle.net/20.500.14066/3025
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author Blanco Bogado, Gerardo Alejandro
author2 Baum Ramos, Gabriel Fernando
Olsina, Fernando
Lopez Moscarda, Sonia Beatriz
author2_role author
author
author
author_browse Baum Ramos, Gabriel Fernando
Blanco Bogado, Gerardo Alejandro
Lopez Moscarda, Sonia Beatriz
Olsina, Fernando
author_facet Blanco Bogado, Gerardo Alejandro
Baum Ramos, Gabriel Fernando
Olsina, Fernando
Lopez Moscarda, Sonia Beatriz
author_role author
bitstream.checksum.fl_str_mv 5cfdbc29f846c60730419137cbeb3327
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bitstream.checksumAlgorithm.fl_str_mv MD5
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bitstream.url.fl_str_mv http://repositorio.conacyt.gov.py/bitstream/20.500.14066/3025/1/14-INV-271art14.pdf
http://repositorio.conacyt.gov.py/bitstream/20.500.14066/3025/2/license.txt
http://repositorio.conacyt.gov.py/bitstream/20.500.14066/3025/3/14-INV-271art14.pdf.txt
dc.contributor.other.es.fl_str_mv Universidad Nacional de Asunción - Facultad Politécnica
dc.creator.none.fl_str_mv Blanco Bogado, Gerardo Alejandro
Baum Ramos, Gabriel Fernando
Olsina, Fernando
Lopez Moscarda, Sonia Beatriz
dc.date.accessioned.none.fl_str_mv 2022-04-22T02:58:05Z
dc.date.available.none.fl_str_mv 2022-04-22T02:58:05Z
dc.date.issued.none.fl_str_mv 2017
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/20.500.14066/3025
dc.language.iso.es.fl_str_mv eng
dc.relation.projectCONACYT.es.fl_str_mv 14-INV-271
dc.rights.accessRights.es.fl_str_mv info:eu-repo/semantics/openAccess
dc.subject.classification.es.fl_str_mv 5. Energía
dc.subject.other.es.fl_str_mv INVESTMENT
SIMILARITY LEARNING
STRATEGIC BEHAVIOR
UNCERTAINTY
ENERGIA ELECTRICA
dc.title.es.fl_str_mv Agent-Based learning model for assessing strategic generation investments in electricity markets
dc.type.es.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
description The liberalization of electricity markets has significantly changed the perspective of the power generation business. Nowadays, generation companies pursue economic goals due their investment decisions are based on expectations of profitability and the risk of their alternatives. These expectations are difficult to predict because they depend upon various factors that are highly uncertain, including both exogenous uncertainties -such as variations of demand and endogenous uncertainties - such as the behavior of competing generation agents. This paper proposes a numerical tool that financially evaluates investment alternatives of generation companies based on a novel adaptive learning technique that links the generation agents' experiences under the current situation considering their expectations of profitability and risk. In this model, the Agent-based Computational Economics approach has been applied. This method represents generation agents through autonomous and heterogeneous entities pursuing economic goals and interacting through computer models.
eu_rights_str_mv openAccess
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network_acronym_str CONACYT
network_name_str Repositorio Institucional CONACYT
oai_identifier_str oai:repositorio.conacyt.gov.py:20.500.14066/3025
publishDate 2017
publishDateSort 2017
repository.mail.fl_str_mv repositorio.institucional@conacyt.gov.py
repository.name.fl_str_mv Repositorio Institucional CONACYT
repository_id_str
spelling 160600136600d6977343-3bea-410f-b411-99fa2e1f0516600675600Universidad Nacional de Asunción - Facultad Politécnica2022-04-22T02:58:05Z2022-04-22T02:58:05Z2017http://hdl.handle.net/20.500.14066/3025The liberalization of electricity markets has significantly changed the perspective of the power generation business. Nowadays, generation companies pursue economic goals due their investment decisions are based on expectations of profitability and the risk of their alternatives. These expectations are difficult to predict because they depend upon various factors that are highly uncertain, including both exogenous uncertainties -such as variations of demand and endogenous uncertainties - such as the behavior of competing generation agents. This paper proposes a numerical tool that financially evaluates investment alternatives of generation companies based on a novel adaptive learning technique that links the generation agents' experiences under the current situation considering their expectations of profitability and risk. In this model, the Agent-based Computational Economics approach has been applied. This method represents generation agents through autonomous and heterogeneous entities pursuing economic goals and interacting through computer models.Consejo Nacional de Ciencia y TecnologíaPROCIENCIAeng5. EnergíaINVESTMENTSIMILARITY LEARNINGSTRATEGIC BEHAVIORUNCERTAINTYENERGIA ELECTRICAAgent-Based learning model for assessing strategic generation investments in electricity marketsinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion14-INV-271info:eu-repo/semantics/openAccessBlanco Bogado, Gerardo AlejandroBaum Ramos, Gabriel FernandoOlsina, FernandoLopez Moscarda, Sonia BeatrizORIGINAL14-INV-271art14.pdf14-INV-271art14.pdf14-INV-271art14application/pdf576085http://repositorio.conacyt.gov.py/bitstream/20.500.14066/3025/1/14-INV-271art14.pdf5cfdbc29f846c60730419137cbeb3327MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81698http://repositorio.conacyt.gov.py/bitstream/20.500.14066/3025/2/license.txt858b22fda432bd774e469302988c1974MD52TEXT14-INV-271art14.pdf.txt14-INV-271art14.pdf.txtExtracted texttext/plain351http://repositorio.conacyt.gov.py/bitstream/20.500.14066/3025/3/14-INV-271art14.pdf.txt3c3d094de8aac865d5874c65b3c011b9MD5320.500.14066/3025oai:repositorio.conacyt.gov.py:20.500.14066/30252026-02-12 19:30:25.932Repositorio Institucional CONACYTrepositorio.institucional@conacyt.gov.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
spellingShingle Agent-Based learning model for assessing strategic generation investments in electricity markets
Blanco Bogado, Gerardo Alejandro
5. Energía
INVESTMENT
SIMILARITY LEARNING
STRATEGIC BEHAVIOR
UNCERTAINTY
ENERGIA ELECTRICA
status_str publishedVersion
title Agent-Based learning model for assessing strategic generation investments in electricity markets
title_full Agent-Based learning model for assessing strategic generation investments in electricity markets
title_fullStr Agent-Based learning model for assessing strategic generation investments in electricity markets
title_full_unstemmed Agent-Based learning model for assessing strategic generation investments in electricity markets
title_short Agent-Based learning model for assessing strategic generation investments in electricity markets
title_sort Agent-Based learning model for assessing strategic generation investments in electricity markets
topic 5. Energía
INVESTMENT
SIMILARITY LEARNING
STRATEGIC BEHAVIOR
UNCERTAINTY
ENERGIA ELECTRICA
url http://hdl.handle.net/20.500.14066/3025

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