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Multi-objective economic emission dispatch of thermal power-electric vehicles considering user’s revenue
In recent years, the rapid development of electric vehicles has increased the load power system and brought new challenges to the safe and stable operation of the gird. Although the vehicle-to-grid technology can reduce the load that electric vehicles put on the grid, without any incentives, electri...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9362973/ https://www.ncbi.nlm.nih.gov/pubmed/35966349 http://dx.doi.org/10.1007/s00500-022-07297-0 |
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author | Qiao, Baihao Liu, Jing Huan, Jiajia |
author_facet | Qiao, Baihao Liu, Jing Huan, Jiajia |
author_sort | Qiao, Baihao |
collection | PubMed |
description | In recent years, the rapid development of electric vehicles has increased the load power system and brought new challenges to the safe and stable operation of the gird. Although the vehicle-to-grid technology can reduce the load that electric vehicles put on the grid, without any incentives, electric vehicle owners are more inclined not to use vehicle-to-grid services. In this paper, therefore, a new dynamic economic emission model based on electric vehicles (DEED_EV) is proposed to maximize the electric vehicle user’s revenue, as well as minimize the fuel cost and emission of the thermal power unit. In the DEED_EV model, the stochastic of electric vehicles user’s travel and wear of the battery, as well as some constraints such as electric vehicles charging/discharging rate and status, electric vehicles remain power, electric vehicles travel power capacity, ramp limits, up and down reserves, and the system balance are considered. To solve the DEED_EV model, a multi-objective evolutionary algorithm based on decomposition with a step-by-step constraint handling strategy is developed. Different test cases based on the 10-unit are simulated to verify the proposed model and method. The results show that the DEED_EV model not only encourages more electric vehicles to plug into the grid but also reduces the fuel cost and emission of the thermal power unit. Besides, the electric vehicles in the DEED_EV completely realizes the peak shaving and valley filling of the load. |
format | Online Article Text |
id | pubmed-9362973 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-93629732022-08-10 Multi-objective economic emission dispatch of thermal power-electric vehicles considering user’s revenue Qiao, Baihao Liu, Jing Huan, Jiajia Soft comput Application of Soft Computing In recent years, the rapid development of electric vehicles has increased the load power system and brought new challenges to the safe and stable operation of the gird. Although the vehicle-to-grid technology can reduce the load that electric vehicles put on the grid, without any incentives, electric vehicle owners are more inclined not to use vehicle-to-grid services. In this paper, therefore, a new dynamic economic emission model based on electric vehicles (DEED_EV) is proposed to maximize the electric vehicle user’s revenue, as well as minimize the fuel cost and emission of the thermal power unit. In the DEED_EV model, the stochastic of electric vehicles user’s travel and wear of the battery, as well as some constraints such as electric vehicles charging/discharging rate and status, electric vehicles remain power, electric vehicles travel power capacity, ramp limits, up and down reserves, and the system balance are considered. To solve the DEED_EV model, a multi-objective evolutionary algorithm based on decomposition with a step-by-step constraint handling strategy is developed. Different test cases based on the 10-unit are simulated to verify the proposed model and method. The results show that the DEED_EV model not only encourages more electric vehicles to plug into the grid but also reduces the fuel cost and emission of the thermal power unit. Besides, the electric vehicles in the DEED_EV completely realizes the peak shaving and valley filling of the load. Springer Berlin Heidelberg 2022-08-09 2022 /pmc/articles/PMC9362973/ /pubmed/35966349 http://dx.doi.org/10.1007/s00500-022-07297-0 Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Application of Soft Computing Qiao, Baihao Liu, Jing Huan, Jiajia Multi-objective economic emission dispatch of thermal power-electric vehicles considering user’s revenue |
title | Multi-objective economic emission dispatch of thermal power-electric vehicles considering user’s revenue |
title_full | Multi-objective economic emission dispatch of thermal power-electric vehicles considering user’s revenue |
title_fullStr | Multi-objective economic emission dispatch of thermal power-electric vehicles considering user’s revenue |
title_full_unstemmed | Multi-objective economic emission dispatch of thermal power-electric vehicles considering user’s revenue |
title_short | Multi-objective economic emission dispatch of thermal power-electric vehicles considering user’s revenue |
title_sort | multi-objective economic emission dispatch of thermal power-electric vehicles considering user’s revenue |
topic | Application of Soft Computing |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9362973/ https://www.ncbi.nlm.nih.gov/pubmed/35966349 http://dx.doi.org/10.1007/s00500-022-07297-0 |
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