Alocação ótima de estações de recargas rápidas em rodovias considerando critérios de diversas naturezas
Ano de defesa: | 2021 |
---|---|
Autor(a) principal: | |
Orientador(a): | |
Banca de defesa: | , |
Tipo de documento: | Dissertação |
Tipo de acesso: | Acesso aberto |
Idioma: | por |
Instituição de defesa: |
Universidade Federal de Santa Maria
Centro de Tecnologia |
Programa de Pós-Graduação: |
Programa de Pós-Graduação em Engenharia Elétrica
|
Departamento: |
Engenharia Elétrica
|
País: |
Brasil
|
Palavras-chave em Português: | |
Palavras-chave em Inglês: | |
Área do conhecimento CNPq: | |
Link de acesso: | http://repositorio.ufsm.br/handle/1/29157 |
Resumo: | The growth of global concern with environmental issues is directly linked to the need to decarbonise the vehicle fleet in order to achieve the objectives set out in the Paris Agreement. The advance of the circulation of Electric Vehicles (VEs) is taking place all over the world, in Brazil even more conservatively, since just in 2018 a Normative Resolution (RN) No. 819 was established to guide the procedures and conditions for commercial and financial exploitation of recharge activities. Conducting studies on charging stations, such as technical and economic impacts, is of paramount importance to encourage the sale of EVs. This methodology aims to define the best allocation for Rapid Recharge Stations (ERRs) on roads that do not yet have this type of infrastructure. For this, the research considers four factors to determine the restriction MDERR (Maximum Distance between two ERRs): autonomy of EVs marketed in the study country, driver's reach anxiety, thermal comfort (air conditioning) and a safety margin, called of unscheduled stops factor. In the matter of choosing the location to install the charging infrastructure, stopping points on the route are analyzed, named after Candidate Establishments (ECs), which can be hotels, motels, restaurants, stops, gas stations, markets, shopping malls, stores and etc. For each of the ECs, scores are determined for three variables: daily passenger vehicle flow, population of the nearby city and level of service that the location provides. With the help of MATLAB software, an optimization with Genetic Algorithm (AG) is elaborated to maximize the score of the chosen CEs within the imposed restrictions. Different scenarios were used to analyze the impact of the weight of each variable in defining the chosen establishments. It is concluded that the variable level of service has a higher impact compared to the flow of vehicles and population as it presents greater variety in neighboring CE. The algorithm points out the Selected Establishments (EE) and the Optional Establishments (EO) that could be chosen without impacting the overall score and the MDERR restriction. |
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2023-05-22T15:37:18Z2023-05-22T15:37:18Z2021-07-29http://repositorio.ufsm.br/handle/1/29157The growth of global concern with environmental issues is directly linked to the need to decarbonise the vehicle fleet in order to achieve the objectives set out in the Paris Agreement. The advance of the circulation of Electric Vehicles (VEs) is taking place all over the world, in Brazil even more conservatively, since just in 2018 a Normative Resolution (RN) No. 819 was established to guide the procedures and conditions for commercial and financial exploitation of recharge activities. Conducting studies on charging stations, such as technical and economic impacts, is of paramount importance to encourage the sale of EVs. This methodology aims to define the best allocation for Rapid Recharge Stations (ERRs) on roads that do not yet have this type of infrastructure. For this, the research considers four factors to determine the restriction MDERR (Maximum Distance between two ERRs): autonomy of EVs marketed in the study country, driver's reach anxiety, thermal comfort (air conditioning) and a safety margin, called of unscheduled stops factor. In the matter of choosing the location to install the charging infrastructure, stopping points on the route are analyzed, named after Candidate Establishments (ECs), which can be hotels, motels, restaurants, stops, gas stations, markets, shopping malls, stores and etc. For each of the ECs, scores are determined for three variables: daily passenger vehicle flow, population of the nearby city and level of service that the location provides. With the help of MATLAB software, an optimization with Genetic Algorithm (AG) is elaborated to maximize the score of the chosen CEs within the imposed restrictions. Different scenarios were used to analyze the impact of the weight of each variable in defining the chosen establishments. It is concluded that the variable level of service has a higher impact compared to the flow of vehicles and population as it presents greater variety in neighboring CE. The algorithm points out the Selected Establishments (EE) and the Optional Establishments (EO) that could be chosen without impacting the overall score and the MDERR restriction.O crescimento da preocupação mundial com as questões ambientais está diretamente ligado a necessidade de descarbonização da frota veicular para atingir os objetivos tratados no Acordo de Paris. O avanço da circulação de Veículos Elétricos (VEs) está ocorrendo em todo mundo, no Brasil ainda de forma mais conservadora, visto que recém em 2018 se estabeleceu uma Resolução Normativa (RN) nº 819 para nortear os procedimentos e condições para exploração comercial e financeira das atividades de recarga. Realizar estudos sobre as estações de recargas, como impactos técnicos e econômicos é de suma importância para incentivar a comercialização de VEs. A presente metodologia tem como objetivo definir a melhor alocação para Estações de Recargas Rápidas (ERRs) em rodovias que ainda não possuem esse tipo de infraestrutura. Para isso, a pesquisa considera quatro fatores para determinar a restrição MDERR (Máxima Distância Entre duas ERRs): autonomia dos VEs comercializados no país do estudo, ansiedade de alcance do motorista, conforto térmico (ar condicionado) e uma margem de segurança, chamada de fator de paradas não programadas. Na questão da escolha do local para instalar a infraestrutura de carregamento, analisa-se pontos de parada existentes na rota, nomeados de Estabelecimentos Candidatos (ECs), que podem ser hotéis, motéis, restaurantes, paradouros, postos de combustíveis, mercados, shoppings, lojas e etc. Para cada um dos ECs, determina-se notas para três variáveis: fluxo de veículos de passeio diários, população da cidade próxima e nível de serviço que o local fornece. Com o auxílio do software MATLAB elabora-se uma otimização com Algoritmo Genético (AG) para maximizar a pontuação dos ECs escolhidos dentro das restrições impostas. Diferentes cenários foram usados para analisar o impacto do peso de cada variável na definição dos estabelecimentos escolhidos. Conclui-se que a variável nível de serviço apresenta impacto superior comparado a fluxo de veículos e população por apresentar maior variedade em EC vizinhos. O algoritmo aponta os Estabelecimentos Escolhidos (EE) e os Estabelecimentos Opcionais (EO) que poderiam ser escolhidos sem impactar na nota global e na restrição de MDERR.porUniversidade Federal de Santa MariaCentro de TecnologiaPrograma de Pós-Graduação em Engenharia ElétricaUFSMBrasilEngenharia ElétricaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAlgoritmo genéticoAlocaçãoEstação de recarga rápidaRodoviasVeículos elétricosGenetic algorithmAllocationFast charging stationHighwaysElectric vehiclesCNPQ::ENGENHARIAS::ENGENHARIA ELETRICAAlocação ótima de estações de recargas rápidas em rodovias considerando critérios de diversas naturezasOptimal allocation of fast recharging stations on highways considering criteria of different naturesinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisAbaide, Alzenira da Rosahttp://lattes.cnpq.br/2427825596072142Santos, Laura Lisiane Callai dosSantos, Moises Machadohttp://lattes.cnpq.br/7742197081251389Lucca, Tiago Guterres3004000000076006006006006006777278d-7b2f-4400-81f8-c04c5b371ad453d23113-96d4-4072-a9cb-67030cfdc8f4e4b168ce-b1a9-45a4-95c6-f39d2bda73ff2ac7aed0-c113-4e8e-9ccd-50c5bf609d2areponame:Manancial - Repositório Digital da UFSMinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSMORIGINALDIS_PPGEE_2021_LUCCA_TIAGO.pdfDIS_PPGEE_2021_LUCCA_TIAGO.pdfDissertaçãoapplication/pdf2919881http://repositorio.ufsm.br/bitstream/1/29157/1/DIS_PPGEE_2021_LUCCA_TIAGO.pdf52b2f19a815de0f6cde09dda108a0949MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81956http://repositorio.ufsm.br/bitstream/1/29157/3/license.txt2f0571ecee68693bd5cd3f17c1e075dfMD53CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805http://repositorio.ufsm.br/bitstream/1/29157/2/license_rdf4460e5956bc1d1639be9ae6146a50347MD521/291572023-05-22 12:37:18.888oai:repositorio.ufsm.br:1/29157TElDRU7Dh0EgREUgRElTVFJJQlVJw4fDg08gTsODTy1FWENMVVNJVkEKCkNvbSBhIGFwcmVzZW50YcOnw6NvIGRlc3RhIGxpY2Vuw6dhLCB2b2PDqiAobyBhdXRvciAoZXMpIG91IG8gdGl0dWxhciBkb3MgZGlyZWl0b3MgZGUgYXV0b3IpIGNvbmNlZGUgw6AgVW5pdmVyc2lkYWRlCkZlZGVyYWwgZGUgU2FudGEgTWFyaWEgKFVGU00pIG8gZGlyZWl0byBuw6NvLWV4Y2x1c2l2byBkZSByZXByb2R1emlyLCAgdHJhZHV6aXIgKGNvbmZvcm1lIGRlZmluaWRvIGFiYWl4byksIGUvb3UKZGlzdHJpYnVpciBhIHN1YSB0ZXNlIG91IGRpc3NlcnRhw6fDo28gKGluY2x1aW5kbyBvIHJlc3VtbykgcG9yIHRvZG8gbyBtdW5kbyBubyBmb3JtYXRvIGltcHJlc3NvIGUgZWxldHLDtG5pY28gZQplbSBxdWFscXVlciBtZWlvLCBpbmNsdWluZG8gb3MgZm9ybWF0b3Mgw6F1ZGlvIG91IHbDrWRlby4KClZvY8OqIGNvbmNvcmRhIHF1ZSBhIFVGU00gcG9kZSwgc2VtIGFsdGVyYXIgbyBjb250ZcO6ZG8sIHRyYW5zcG9yIGEgc3VhIHRlc2Ugb3UgZGlzc2VydGHDp8OjbwpwYXJhIHF1YWxxdWVyIG1laW8gb3UgZm9ybWF0byBwYXJhIGZpbnMgZGUgcHJlc2VydmHDp8Ojby4KClZvY8OqIHRhbWLDqW0gY29uY29yZGEgcXVlIGEgVUZTTSBwb2RlIG1hbnRlciBtYWlzIGRlIHVtYSBjw7NwaWEgYSBzdWEgdGVzZSBvdQpkaXNzZXJ0YcOnw6NvIHBhcmEgZmlucyBkZSBzZWd1cmFuw6dhLCBiYWNrLXVwIGUgcHJlc2VydmHDp8Ojby4KClZvY8OqIGRlY2xhcmEgcXVlIGEgc3VhIHRlc2Ugb3UgZGlzc2VydGHDp8OjbyDDqSBvcmlnaW5hbCBlIHF1ZSB2b2PDqiB0ZW0gbyBwb2RlciBkZSBjb25jZWRlciBvcyBkaXJlaXRvcyBjb250aWRvcwpuZXN0YSBsaWNlbsOnYS4gVm9jw6ogdGFtYsOpbSBkZWNsYXJhIHF1ZSBvIGRlcMOzc2l0byBkYSBzdWEgdGVzZSBvdSBkaXNzZXJ0YcOnw6NvIG7Do28sIHF1ZSBzZWphIGRlIHNldQpjb25oZWNpbWVudG8sIGluZnJpbmdlIGRpcmVpdG9zIGF1dG9yYWlzIGRlIG5pbmd1w6ltLgoKQ2FzbyBhIHN1YSB0ZXNlIG91IGRpc3NlcnRhw6fDo28gY29udGVuaGEgbWF0ZXJpYWwgcXVlIHZvY8OqIG7Do28gcG9zc3VpIGEgdGl0dWxhcmlkYWRlIGRvcyBkaXJlaXRvcyBhdXRvcmFpcywgdm9jw6oKZGVjbGFyYSBxdWUgb2J0ZXZlIGEgcGVybWlzc8OjbyBpcnJlc3RyaXRhIGRvIGRldGVudG9yIGRvcyBkaXJlaXRvcyBhdXRvcmFpcyBwYXJhIGNvbmNlZGVyIMOgIFVGU00Kb3MgZGlyZWl0b3MgYXByZXNlbnRhZG9zIG5lc3RhIGxpY2Vuw6dhLCBlIHF1ZSBlc3NlIG1hdGVyaWFsIGRlIHByb3ByaWVkYWRlIGRlIHRlcmNlaXJvcyBlc3TDoSBjbGFyYW1lbnRlCmlkZW50aWZpY2FkbyBlIHJlY29uaGVjaWRvIG5vIHRleHRvIG91IG5vIGNvbnRlw7pkbyBkYSB0ZXNlIG91IGRpc3NlcnRhw6fDo28gb3JhIGRlcG9zaXRhZGEuCgpDQVNPIEEgVEVTRSBPVSBESVNTRVJUQcOHw4NPIE9SQSBERVBPU0lUQURBIFRFTkhBIFNJRE8gUkVTVUxUQURPIERFIFVNIFBBVFJPQ8ONTklPIE9VCkFQT0lPIERFIFVNQSBBR8OKTkNJQSBERSBGT01FTlRPIE9VIE9VVFJPIE9SR0FOSVNNTyBRVUUgTsODTyBTRUpBIEEgVUZTTQosIFZPQ8OKIERFQ0xBUkEgUVVFIFJFU1BFSVRPVSBUT0RPUyBFIFFVQUlTUVVFUiBESVJFSVRPUyBERSBSRVZJU8ODTyBDT01PClRBTULDiU0gQVMgREVNQUlTIE9CUklHQcOHw5VFUyBFWElHSURBUyBQT1IgQ09OVFJBVE8gT1UgQUNPUkRPLgoKQSBVRlNNIHNlIGNvbXByb21ldGUgYSBpZGVudGlmaWNhciBjbGFyYW1lbnRlIG8gc2V1IG5vbWUgKHMpIG91IG8ocykgbm9tZShzKSBkbyhzKQpkZXRlbnRvcihlcykgZG9zIGRpcmVpdG9zIGF1dG9yYWlzIGRhIHRlc2Ugb3UgZGlzc2VydGHDp8OjbywgZSBuw6NvIGZhcsOhIHF1YWxxdWVyIGFsdGVyYcOnw6NvLCBhbMOpbSBkYXF1ZWxhcwpjb25jZWRpZGFzIHBvciBlc3RhIGxpY2Vuw6dhLgoKRepositório Institucionalhttp://repositorio.ufsm.br/PUBhttp://repositorio.ufsm.br/oai/requestopendoar:39132023-05-22T15:37:18Manancial - Repositório Digital da UFSM - Universidade Federal de Santa Maria (UFSM)false |
dc.title.por.fl_str_mv |
Alocação ótima de estações de recargas rápidas em rodovias considerando critérios de diversas naturezas |
dc.title.alternative.eng.fl_str_mv |
Optimal allocation of fast recharging stations on highways considering criteria of different natures |
title |
Alocação ótima de estações de recargas rápidas em rodovias considerando critérios de diversas naturezas |
spellingShingle |
Alocação ótima de estações de recargas rápidas em rodovias considerando critérios de diversas naturezas Lucca, Tiago Guterres Algoritmo genético Alocação Estação de recarga rápida Rodovias Veículos elétricos Genetic algorithm Allocation Fast charging station Highways Electric vehicles CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA |
title_short |
Alocação ótima de estações de recargas rápidas em rodovias considerando critérios de diversas naturezas |
title_full |
Alocação ótima de estações de recargas rápidas em rodovias considerando critérios de diversas naturezas |
title_fullStr |
Alocação ótima de estações de recargas rápidas em rodovias considerando critérios de diversas naturezas |
title_full_unstemmed |
Alocação ótima de estações de recargas rápidas em rodovias considerando critérios de diversas naturezas |
title_sort |
Alocação ótima de estações de recargas rápidas em rodovias considerando critérios de diversas naturezas |
author |
Lucca, Tiago Guterres |
author_facet |
Lucca, Tiago Guterres |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Abaide, Alzenira da Rosa |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/2427825596072142 |
dc.contributor.referee1.fl_str_mv |
Santos, Laura Lisiane Callai dos |
dc.contributor.referee2.fl_str_mv |
Santos, Moises Machado |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/7742197081251389 |
dc.contributor.author.fl_str_mv |
Lucca, Tiago Guterres |
contributor_str_mv |
Abaide, Alzenira da Rosa Santos, Laura Lisiane Callai dos Santos, Moises Machado |
dc.subject.por.fl_str_mv |
Algoritmo genético Alocação Estação de recarga rápida Rodovias Veículos elétricos |
topic |
Algoritmo genético Alocação Estação de recarga rápida Rodovias Veículos elétricos Genetic algorithm Allocation Fast charging station Highways Electric vehicles CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA |
dc.subject.eng.fl_str_mv |
Genetic algorithm Allocation Fast charging station Highways Electric vehicles |
dc.subject.cnpq.fl_str_mv |
CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA |
description |
The growth of global concern with environmental issues is directly linked to the need to decarbonise the vehicle fleet in order to achieve the objectives set out in the Paris Agreement. The advance of the circulation of Electric Vehicles (VEs) is taking place all over the world, in Brazil even more conservatively, since just in 2018 a Normative Resolution (RN) No. 819 was established to guide the procedures and conditions for commercial and financial exploitation of recharge activities. Conducting studies on charging stations, such as technical and economic impacts, is of paramount importance to encourage the sale of EVs. This methodology aims to define the best allocation for Rapid Recharge Stations (ERRs) on roads that do not yet have this type of infrastructure. For this, the research considers four factors to determine the restriction MDERR (Maximum Distance between two ERRs): autonomy of EVs marketed in the study country, driver's reach anxiety, thermal comfort (air conditioning) and a safety margin, called of unscheduled stops factor. In the matter of choosing the location to install the charging infrastructure, stopping points on the route are analyzed, named after Candidate Establishments (ECs), which can be hotels, motels, restaurants, stops, gas stations, markets, shopping malls, stores and etc. For each of the ECs, scores are determined for three variables: daily passenger vehicle flow, population of the nearby city and level of service that the location provides. With the help of MATLAB software, an optimization with Genetic Algorithm (AG) is elaborated to maximize the score of the chosen CEs within the imposed restrictions. Different scenarios were used to analyze the impact of the weight of each variable in defining the chosen establishments. It is concluded that the variable level of service has a higher impact compared to the flow of vehicles and population as it presents greater variety in neighboring CE. The algorithm points out the Selected Establishments (EE) and the Optional Establishments (EO) that could be chosen without impacting the overall score and the MDERR restriction. |
publishDate |
2021 |
dc.date.issued.fl_str_mv |
2021-07-29 |
dc.date.accessioned.fl_str_mv |
2023-05-22T15:37:18Z |
dc.date.available.fl_str_mv |
2023-05-22T15:37:18Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
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info:eu-repo/semantics/masterThesis |
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masterThesis |
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publishedVersion |
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http://repositorio.ufsm.br/handle/1/29157 |
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http://repositorio.ufsm.br/handle/1/29157 |
dc.language.iso.fl_str_mv |
por |
language |
por |
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300400000007 |
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600 600 600 600 600 |
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Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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openAccess |
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Universidade Federal de Santa Maria Centro de Tecnologia |
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Programa de Pós-Graduação em Engenharia Elétrica |
dc.publisher.initials.fl_str_mv |
UFSM |
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Brasil |
dc.publisher.department.fl_str_mv |
Engenharia Elétrica |
publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Centro de Tecnologia |
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