A three‐stage economy optimization method for the aggregator based on electric vehicle user response volumes
Abstract With the development of intelligent distribution networks and the proposal of the carbon peaking and carbon neutrality goals, the importance of demand‐side management for the improvement of flexible power operation systems is becoming increasingly prominent. To solve the problems of the exc...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-09-01
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| Series: | IET Generation, Transmission & Distribution |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/gtd2.12953 |
| _version_ | 1797728169740992512 |
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| author | Hui Lin Yichen Zhou Yonggang Li Haoyang Zheng Yang Yang Xinghao Zhen Zhaoyuan Bian Ziyi Yue |
| author_facet | Hui Lin Yichen Zhou Yonggang Li Haoyang Zheng Yang Yang Xinghao Zhen Zhaoyuan Bian Ziyi Yue |
| author_sort | Hui Lin |
| collection | DOAJ |
| description | Abstract With the development of intelligent distribution networks and the proposal of the carbon peaking and carbon neutrality goals, the importance of demand‐side management for the improvement of flexible power operation systems is becoming increasingly prominent. To solve the problems of the excessive peak–valley load differences, the insufficient utilization of demand‐side resources, and the unreasonable pricing of aggregators, this paper proposes a three‐stage economy optimization method for the aggregator based on EV(electric vehicle) user response volumes. To gain the advantages of different types of EVs while increasing the dispatchable capacity, the contract mode between the aggregator and EVs is divided into three categories: complete dispatching, rolling reward and punishment mechanism dispatching, and free dispatching. Based on the cloud model, considering loss aversion, the user's own time flexibility, and the impact of the tariff set by the aggregator on the user's decision‐making, we introduced indicator weights to obtain the improved cloud model. Based on the improved cloud model, a user response volume model is obtained. Then, the aggregator performs a three‐stage pricing optimization operation for EVs based on the bid‐winning peak shaving capacity. In the first stage, the attraction between EVs and charging stations is determined based on the law of gravity, and the charging and discharging reward electricity price is set. In the second stage, the dynamic electricity price of three types of EVs and the charging punishment electricity prices of the rolling reward and punishment mechanism dispatching type are determined. In the third stage, the electricity price of the rolling reward and punishment dispatching EVs is further optimized based on their lack of peak shaving capacity. The method proposed in the paper has been verified to be effective in increasing aggregator profits, increasing EV dispatch capacity, and reducing EV charging costs through the example analysis. |
| first_indexed | 2024-03-12T11:10:01Z |
| format | Article |
| id | doaj.art-ea857e7fae8a4532acf7a5f5189d277f |
| institution | Directory Open Access Journal |
| issn | 1751-8687 1751-8695 |
| language | English |
| last_indexed | 2024-03-12T11:10:01Z |
| publishDate | 2023-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Generation, Transmission & Distribution |
| spelling | doaj.art-ea857e7fae8a4532acf7a5f5189d277f2023-09-02T03:10:09ZengWileyIET Generation, Transmission & Distribution1751-86871751-86952023-09-0117173951396710.1049/gtd2.12953A three‐stage economy optimization method for the aggregator based on electric vehicle user response volumesHui Lin0Yichen Zhou1Yonggang Li2Haoyang Zheng3Yang Yang4Xinghao Zhen5Zhaoyuan Bian6Ziyi Yue7Department of Electric Power Engineering North China Electric Power University Baoding People's Republic of ChinaDepartment of Electric Power Engineering North China Electric Power University Baoding People's Republic of ChinaDepartment of Electric Power Engineering North China Electric Power University Baoding People's Republic of ChinaDepartment of Electric Power Engineering North China Electric Power University Baoding People's Republic of ChinaDepartment of Electric Power Engineering North China Electric Power University Baoding People's Republic of ChinaDepartment of Electric Power Engineering North China Electric Power University Baoding People's Republic of ChinaDepartment of Electric Power Engineering North China Electric Power University Baoding People's Republic of ChinaDepartment of Electric Power Engineering North China Electric Power University Baoding People's Republic of ChinaAbstract With the development of intelligent distribution networks and the proposal of the carbon peaking and carbon neutrality goals, the importance of demand‐side management for the improvement of flexible power operation systems is becoming increasingly prominent. To solve the problems of the excessive peak–valley load differences, the insufficient utilization of demand‐side resources, and the unreasonable pricing of aggregators, this paper proposes a three‐stage economy optimization method for the aggregator based on EV(electric vehicle) user response volumes. To gain the advantages of different types of EVs while increasing the dispatchable capacity, the contract mode between the aggregator and EVs is divided into three categories: complete dispatching, rolling reward and punishment mechanism dispatching, and free dispatching. Based on the cloud model, considering loss aversion, the user's own time flexibility, and the impact of the tariff set by the aggregator on the user's decision‐making, we introduced indicator weights to obtain the improved cloud model. Based on the improved cloud model, a user response volume model is obtained. Then, the aggregator performs a three‐stage pricing optimization operation for EVs based on the bid‐winning peak shaving capacity. In the first stage, the attraction between EVs and charging stations is determined based on the law of gravity, and the charging and discharging reward electricity price is set. In the second stage, the dynamic electricity price of three types of EVs and the charging punishment electricity prices of the rolling reward and punishment mechanism dispatching type are determined. In the third stage, the electricity price of the rolling reward and punishment dispatching EVs is further optimized based on their lack of peak shaving capacity. The method proposed in the paper has been verified to be effective in increasing aggregator profits, increasing EV dispatch capacity, and reducing EV charging costs through the example analysis.https://doi.org/10.1049/gtd2.12953demand side managementelectric vehicle chargingelectric vehicles |
| spellingShingle | Hui Lin Yichen Zhou Yonggang Li Haoyang Zheng Yang Yang Xinghao Zhen Zhaoyuan Bian Ziyi Yue A three‐stage economy optimization method for the aggregator based on electric vehicle user response volumes IET Generation, Transmission & Distribution demand side management electric vehicle charging electric vehicles |
| title | A three‐stage economy optimization method for the aggregator based on electric vehicle user response volumes |
| title_full | A three‐stage economy optimization method for the aggregator based on electric vehicle user response volumes |
| title_fullStr | A three‐stage economy optimization method for the aggregator based on electric vehicle user response volumes |
| title_full_unstemmed | A three‐stage economy optimization method for the aggregator based on electric vehicle user response volumes |
| title_short | A three‐stage economy optimization method for the aggregator based on electric vehicle user response volumes |
| title_sort | three stage economy optimization method for the aggregator based on electric vehicle user response volumes |
| topic | demand side management electric vehicle charging electric vehicles |
| url | https://doi.org/10.1049/gtd2.12953 |
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