Two-stage stochastic energy procurement model for a large consumer in hydrothermal systems
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1016/j.eneco.2022.105841 http://hdl.handle.net/11449/230328 |
Resumo: | The Energy Procurement (EP) problem faced by a large consumer is concerned with planning the energy procurement in the various energy markets available, such that its short- and medium-term demands are met, and the risks involved in such trading are mitigated. Although a number of EP models have been proposed for purely thermal systems, no specific model has been addressed for solving this problem for a large consumer located in a hydro-dominated system. In this paper we discuss the main specific features and issues involving EP problems for hydro-dominated markets. A central issue is the estimation of future energy prices in the pool market. In hydro-dominated systems, uncertainties in incremental water inflows into reservoirs affect directly such prices, as well as the estimated demands, and these are difficult correlations to be captured by a price estimation model. In this paper, we propose a Price Scenario Generation (PSG) model to estimate future pool prices, which is able to capture spatial and temporal correlation among uncertain prices, water inflows and demands. The estimated prices obtained by the PSG are introduced in the proposed Energy Procurement Model for Hydrothermal Systems (EPMHS), which calculates the optimal procurement decisions, involving the portions of energy traded in the pool, bilateral and futures markets, as well as the self-produced energy. The proposed EPMHS is formulated as a sequence of mixed-integer two-stage stochastic linear programming problems, where pool prices are handled as uncertain parameters. The EPMHS represents trading risks using the Conditional Value at Risk (CVaR) metric. We also propose a strategy for including yearly estimation of water inflows into the EPMHS, since prices in hydro-dominated markets are generally driven by water inflows forecasts. The model proposed is applied to the generation system of the northeast region of Brazil, and the results reveal coherent correlations between hydro and economic variables. |
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Two-stage stochastic energy procurement model for a large consumer in hydrothermal systemsEnergy procurement in electricity marketsLarge consumersMedium-term hydrothermal schedulingPortfolio optimization problemThe Energy Procurement (EP) problem faced by a large consumer is concerned with planning the energy procurement in the various energy markets available, such that its short- and medium-term demands are met, and the risks involved in such trading are mitigated. Although a number of EP models have been proposed for purely thermal systems, no specific model has been addressed for solving this problem for a large consumer located in a hydro-dominated system. In this paper we discuss the main specific features and issues involving EP problems for hydro-dominated markets. A central issue is the estimation of future energy prices in the pool market. In hydro-dominated systems, uncertainties in incremental water inflows into reservoirs affect directly such prices, as well as the estimated demands, and these are difficult correlations to be captured by a price estimation model. In this paper, we propose a Price Scenario Generation (PSG) model to estimate future pool prices, which is able to capture spatial and temporal correlation among uncertain prices, water inflows and demands. The estimated prices obtained by the PSG are introduced in the proposed Energy Procurement Model for Hydrothermal Systems (EPMHS), which calculates the optimal procurement decisions, involving the portions of energy traded in the pool, bilateral and futures markets, as well as the self-produced energy. The proposed EPMHS is formulated as a sequence of mixed-integer two-stage stochastic linear programming problems, where pool prices are handled as uncertain parameters. The EPMHS represents trading risks using the Conditional Value at Risk (CVaR) metric. We also propose a strategy for including yearly estimation of water inflows into the EPMHS, since prices in hydro-dominated markets are generally driven by water inflows forecasts. The model proposed is applied to the generation system of the northeast region of Brazil, and the results reveal coherent correlations between hydro and economic variables.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Department of Electrical Engineering Faculty of Engineering-FEB Unesp-Univ. Estadual PaulistaDepartment of Mathematics Faculty of Sciences-FC Unesp-Univ. Estadual PaulistaDepartment of Electrical Engineering IFPR - Instituto Federal do ParanáDepartment of Electrical Engineering Faculty of Engineering-FEB Unesp-Univ. Estadual PaulistaDepartment of Mathematics Faculty of Sciences-FC Unesp-Univ. Estadual PaulistaFAPESP: 2013/18036-1FAPESP: 2014/20853-0CNPq: 305548/2019-0CNPq: 309780/2017-9CNPq: 313495/2017-3Universidade Estadual Paulista (UNESP)IFPR - Instituto Federal do ParanáSilva, Rodolfo Rodrigues BarrionuevoMartins, André Christóvão Pio [UNESP]Soler, Edilaine Martins [UNESP]Baptista, Edméa Cássia [UNESP]Balbo, Antonio Roberto [UNESP]Nepomuceno, Leonardo [UNESP]2022-04-29T08:39:21Z2022-04-29T08:39:21Z2022-03-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1016/j.eneco.2022.105841Energy Economics, v. 107.0140-9883http://hdl.handle.net/11449/23032810.1016/j.eneco.2022.1058412-s2.0-85123946176Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengEnergy Economicsinfo:eu-repo/semantics/openAccess2024-06-28T13:34:13Zoai:repositorio.unesp.br:11449/230328Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T17:33:52.723171Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Two-stage stochastic energy procurement model for a large consumer in hydrothermal systems |
title |
Two-stage stochastic energy procurement model for a large consumer in hydrothermal systems |
spellingShingle |
Two-stage stochastic energy procurement model for a large consumer in hydrothermal systems Silva, Rodolfo Rodrigues Barrionuevo Energy procurement in electricity markets Large consumers Medium-term hydrothermal scheduling Portfolio optimization problem |
title_short |
Two-stage stochastic energy procurement model for a large consumer in hydrothermal systems |
title_full |
Two-stage stochastic energy procurement model for a large consumer in hydrothermal systems |
title_fullStr |
Two-stage stochastic energy procurement model for a large consumer in hydrothermal systems |
title_full_unstemmed |
Two-stage stochastic energy procurement model for a large consumer in hydrothermal systems |
title_sort |
Two-stage stochastic energy procurement model for a large consumer in hydrothermal systems |
author |
Silva, Rodolfo Rodrigues Barrionuevo |
author_facet |
Silva, Rodolfo Rodrigues Barrionuevo Martins, André Christóvão Pio [UNESP] Soler, Edilaine Martins [UNESP] Baptista, Edméa Cássia [UNESP] Balbo, Antonio Roberto [UNESP] Nepomuceno, Leonardo [UNESP] |
author_role |
author |
author2 |
Martins, André Christóvão Pio [UNESP] Soler, Edilaine Martins [UNESP] Baptista, Edméa Cássia [UNESP] Balbo, Antonio Roberto [UNESP] Nepomuceno, Leonardo [UNESP] |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) IFPR - Instituto Federal do Paraná |
dc.contributor.author.fl_str_mv |
Silva, Rodolfo Rodrigues Barrionuevo Martins, André Christóvão Pio [UNESP] Soler, Edilaine Martins [UNESP] Baptista, Edméa Cássia [UNESP] Balbo, Antonio Roberto [UNESP] Nepomuceno, Leonardo [UNESP] |
dc.subject.por.fl_str_mv |
Energy procurement in electricity markets Large consumers Medium-term hydrothermal scheduling Portfolio optimization problem |
topic |
Energy procurement in electricity markets Large consumers Medium-term hydrothermal scheduling Portfolio optimization problem |
description |
The Energy Procurement (EP) problem faced by a large consumer is concerned with planning the energy procurement in the various energy markets available, such that its short- and medium-term demands are met, and the risks involved in such trading are mitigated. Although a number of EP models have been proposed for purely thermal systems, no specific model has been addressed for solving this problem for a large consumer located in a hydro-dominated system. In this paper we discuss the main specific features and issues involving EP problems for hydro-dominated markets. A central issue is the estimation of future energy prices in the pool market. In hydro-dominated systems, uncertainties in incremental water inflows into reservoirs affect directly such prices, as well as the estimated demands, and these are difficult correlations to be captured by a price estimation model. In this paper, we propose a Price Scenario Generation (PSG) model to estimate future pool prices, which is able to capture spatial and temporal correlation among uncertain prices, water inflows and demands. The estimated prices obtained by the PSG are introduced in the proposed Energy Procurement Model for Hydrothermal Systems (EPMHS), which calculates the optimal procurement decisions, involving the portions of energy traded in the pool, bilateral and futures markets, as well as the self-produced energy. The proposed EPMHS is formulated as a sequence of mixed-integer two-stage stochastic linear programming problems, where pool prices are handled as uncertain parameters. The EPMHS represents trading risks using the Conditional Value at Risk (CVaR) metric. We also propose a strategy for including yearly estimation of water inflows into the EPMHS, since prices in hydro-dominated markets are generally driven by water inflows forecasts. The model proposed is applied to the generation system of the northeast region of Brazil, and the results reveal coherent correlations between hydro and economic variables. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-04-29T08:39:21Z 2022-04-29T08:39:21Z 2022-03-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1016/j.eneco.2022.105841 Energy Economics, v. 107. 0140-9883 http://hdl.handle.net/11449/230328 10.1016/j.eneco.2022.105841 2-s2.0-85123946176 |
url |
http://dx.doi.org/10.1016/j.eneco.2022.105841 http://hdl.handle.net/11449/230328 |
identifier_str_mv |
Energy Economics, v. 107. 0140-9883 10.1016/j.eneco.2022.105841 2-s2.0-85123946176 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Energy Economics |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128827124088832 |