An aggregator-based dynamic pricing mechanism and optimal scheduling scheme for the electric vehicle charging
High penetration of electric vehicles (EVs) in an uncontrolled manner could have disruptive impacts on the power grid, however, such impacts could be mitigated through an EV demand response program. The successful implementation of an efficient, effective, and aggregated demand response from EV char...
| Главные авторы: | , , , , , , |
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| Формат: | Статья |
| Язык: | English |
| Опубликовано: |
Frontiers Media S.A.
2023-01-01
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| Серии: | Frontiers in Energy Research |
| Предметы: | |
| Online-ссылка: | https://www.frontiersin.org/articles/10.3389/fenrg.2022.1037253/full |
| _version_ | 1828058010629439488 |
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| author | Yuxi Liu Jie Zhu Yuanrui Sang Mostafa Sahraei-Ardakani Tianjun Jing Yongning Zhao Yingying Zheng |
| author_facet | Yuxi Liu Jie Zhu Yuanrui Sang Mostafa Sahraei-Ardakani Tianjun Jing Yongning Zhao Yingying Zheng |
| author_sort | Yuxi Liu |
| collection | DOAJ |
| description | High penetration of electric vehicles (EVs) in an uncontrolled manner could have disruptive impacts on the power grid, however, such impacts could be mitigated through an EV demand response program. The successful implementation of an efficient, effective, and aggregated demand response from EV charging depends on the incentive pricing mechanism and the load shifting protocols. In this study, a genetic algorithm-based multi-objective optimization model is developed to generate hourly dynamic Time-of-Use electricity tariffs and facilitate the decision making in load scheduling. As an illustrative example, a case study was carried out to examine the effect of applying demand response for EVs in Beijing, China. With the assumptions made, the maximum peak load can be reduced by 9.8% and the maximum customer savings for the EVs owners can reach 11.85%, compared to the business-as-usual case. |
| first_indexed | 2024-04-10T21:21:14Z |
| format | Article |
| id | doaj.art-e81fa5b7e2b545a7b7129b7e70d3f19d |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-04-10T21:21:14Z |
| publishDate | 2023-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-e81fa5b7e2b545a7b7129b7e70d3f19d2023-01-20T04:46:39ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2023-01-011010.3389/fenrg.2022.10372531037253An aggregator-based dynamic pricing mechanism and optimal scheduling scheme for the electric vehicle chargingYuxi Liu0Jie Zhu1Yuanrui Sang2Mostafa Sahraei-Ardakani3Tianjun Jing4Yongning Zhao5Yingying Zheng6College of Information and Electrical Engineering, China Agricultural University, Beijing, ChinaCollege of Information and Electrical Engineering, China Agricultural University, Beijing, ChinaDepartment of Electrical and Computer Engineering, The University of Texas at El Paso, El Paso, TX, United StatesDepartment of Electrical and Computer Engineering, The University of Utah, Salt Lake City, UT, United StatesCollege of Information and Electrical Engineering, China Agricultural University, Beijing, ChinaCollege of Information and Electrical Engineering, China Agricultural University, Beijing, ChinaCollege of Information and Electrical Engineering, China Agricultural University, Beijing, ChinaHigh penetration of electric vehicles (EVs) in an uncontrolled manner could have disruptive impacts on the power grid, however, such impacts could be mitigated through an EV demand response program. The successful implementation of an efficient, effective, and aggregated demand response from EV charging depends on the incentive pricing mechanism and the load shifting protocols. In this study, a genetic algorithm-based multi-objective optimization model is developed to generate hourly dynamic Time-of-Use electricity tariffs and facilitate the decision making in load scheduling. As an illustrative example, a case study was carried out to examine the effect of applying demand response for EVs in Beijing, China. With the assumptions made, the maximum peak load can be reduced by 9.8% and the maximum customer savings for the EVs owners can reach 11.85%, compared to the business-as-usual case.https://www.frontiersin.org/articles/10.3389/fenrg.2022.1037253/fullelectric vehiclessmart griddemand responseoptimal chargingdynamic pricingcharging schedule |
| spellingShingle | Yuxi Liu Jie Zhu Yuanrui Sang Mostafa Sahraei-Ardakani Tianjun Jing Yongning Zhao Yingying Zheng An aggregator-based dynamic pricing mechanism and optimal scheduling scheme for the electric vehicle charging Frontiers in Energy Research electric vehicles smart grid demand response optimal charging dynamic pricing charging schedule |
| title | An aggregator-based dynamic pricing mechanism and optimal scheduling scheme for the electric vehicle charging |
| title_full | An aggregator-based dynamic pricing mechanism and optimal scheduling scheme for the electric vehicle charging |
| title_fullStr | An aggregator-based dynamic pricing mechanism and optimal scheduling scheme for the electric vehicle charging |
| title_full_unstemmed | An aggregator-based dynamic pricing mechanism and optimal scheduling scheme for the electric vehicle charging |
| title_short | An aggregator-based dynamic pricing mechanism and optimal scheduling scheme for the electric vehicle charging |
| title_sort | aggregator based dynamic pricing mechanism and optimal scheduling scheme for the electric vehicle charging |
| topic | electric vehicles smart grid demand response optimal charging dynamic pricing charging schedule |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2022.1037253/full |
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