A Cluster-based Optimal Scheduling Strategy for Electric Vehicles Considering User Participation
To solve the scheduling problem of large-scale electric vehicles participating in peak shaving, this paper presents a cluster-based optimal scheduling strategy for electric vehicles considering user participation. Multilayer perceptron neural network is adopted to predict power load and obtain peak...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Office of Control and Information Technology
2021-01-01
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| Series: | Kongzhi Yu Xinxi Jishu |
| Subjects: | |
| Online Access: | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2021.06.400 |
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| _version_ | 1849224975860367360 |
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| author | HAN Yan DING Xiying CHENG Kun LI Xiaodong |
| author_facet | HAN Yan DING Xiying CHENG Kun LI Xiaodong |
| author_sort | HAN Yan |
| collection | DOAJ |
| description | To solve the scheduling problem of large-scale electric vehicles participating in peak shaving, this paper presents a cluster-based optimal scheduling strategy for electric vehicles considering user participation. Multilayer perceptron neural network is adopted to predict power load and obtain peak difference. The electric vehicle cluster classification network based on convolutional neural network is trained by using a large number of vehicle information and considering vehicle owner intention to classify the vehicle peak shaving participation, and quickly determine the total electricity involved in peak shaving. Considering both peak shaving effect and user economic benefits, an improved particle swarm optimization algorithm is proposed to optimize the power participating in peak shaving. Taking the county area as an example, the proposed method is verified on Matlab platform. The accuracy of all kinds of electric vehicle clusters is higher than 90% and the peak difference between peak and valey load is reduced through the charge and discharge of electric vehicle clusters, which can verify the effectiveness of the electric vehicle clustering method and the scheduling scheme of peak shaving and valley filling |
| format | Article |
| id | doaj-art-b5b2b70abdac41668d0d27582a9b5c8f |
| institution | Kabale University |
| issn | 2096-5427 |
| language | zho |
| publishDate | 2021-01-01 |
| publisher | Editorial Office of Control and Information Technology |
| record_format | Article |
| series | Kongzhi Yu Xinxi Jishu |
| spelling | doaj-art-b5b2b70abdac41668d0d27582a9b5c8f2025-08-25T06:49:55ZzhoEditorial Office of Control and Information TechnologyKongzhi Yu Xinxi Jishu2096-54272021-01-0138515682317433A Cluster-based Optimal Scheduling Strategy for Electric Vehicles Considering User ParticipationHAN YanDING XiyingCHENG KunLI XiaodongTo solve the scheduling problem of large-scale electric vehicles participating in peak shaving, this paper presents a cluster-based optimal scheduling strategy for electric vehicles considering user participation. Multilayer perceptron neural network is adopted to predict power load and obtain peak difference. The electric vehicle cluster classification network based on convolutional neural network is trained by using a large number of vehicle information and considering vehicle owner intention to classify the vehicle peak shaving participation, and quickly determine the total electricity involved in peak shaving. Considering both peak shaving effect and user economic benefits, an improved particle swarm optimization algorithm is proposed to optimize the power participating in peak shaving. Taking the county area as an example, the proposed method is verified on Matlab platform. The accuracy of all kinds of electric vehicle clusters is higher than 90% and the peak difference between peak and valey load is reduced through the charge and discharge of electric vehicle clusters, which can verify the effectiveness of the electric vehicle clustering method and the scheduling scheme of peak shaving and valley fillinghttp://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2021.06.400electric vehicle clustersload predictionconvolutional neural networkpeak cutting and valley fillingoptimal schedulingmultilayer perceptron neural network |
| spellingShingle | HAN Yan DING Xiying CHENG Kun LI Xiaodong A Cluster-based Optimal Scheduling Strategy for Electric Vehicles Considering User Participation Kongzhi Yu Xinxi Jishu electric vehicle clusters load prediction convolutional neural network peak cutting and valley filling optimal scheduling multilayer perceptron neural network |
| title | A Cluster-based Optimal Scheduling Strategy for Electric Vehicles
Considering User Participation |
| title_full | A Cluster-based Optimal Scheduling Strategy for Electric Vehicles
Considering User Participation |
| title_fullStr | A Cluster-based Optimal Scheduling Strategy for Electric Vehicles
Considering User Participation |
| title_full_unstemmed | A Cluster-based Optimal Scheduling Strategy for Electric Vehicles
Considering User Participation |
| title_short | A Cluster-based Optimal Scheduling Strategy for Electric Vehicles
Considering User Participation |
| title_sort | cluster based optimal scheduling strategy for electric vehicles considering user participation |
| topic | electric vehicle clusters load prediction convolutional neural network peak cutting and valley filling optimal scheduling multilayer perceptron neural network |
| url | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2021.06.400 |
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