Resource Optimization Method Based on Spatio-Temporal Modeling in a Complex Cluster Environment for Electric Vehicle Charging Scenarios
In intelligent cluster systems, the spatio-temporal complexity of agent data collection and resource allocation, as well as the problems in collaborative organizations, present substantial challenges to efficient resource distribution. To address this, a novel self-organizing prediction method for s...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | World Electric Vehicle Journal |
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| Online Access: | https://www.mdpi.com/2032-6653/16/5/284 |
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| author | Hongwei Wang Wei Liu Chenghui Wang Kao Guo Zihao Wang |
| author_facet | Hongwei Wang Wei Liu Chenghui Wang Kao Guo Zihao Wang |
| author_sort | Hongwei Wang |
| collection | DOAJ |
| description | In intelligent cluster systems, the spatio-temporal complexity of agent data collection and resource allocation, as well as the problems in collaborative organizations, present substantial challenges to efficient resource distribution. To address this, a novel self-organizing prediction method for spatio-temporal resource allocation is proposed. In the spatio-temporal modeling part, dilated convolution is applied for time modeling. Its dilation rate grows exponentially with the layer depth, allowing it to effectively capture the time trends of graph nodes and handle long time series data. For spatial modeling, an innovative dual-view dynamic graph convolutional network architecture is utilized to accurately explore the static and dynamic correlation information of the spatial layout of charging piles. Meanwhile, a composite self-organizing mechanism integrating a trust model is put forward. The trust model assists agents in choosing partners, and the Q-learning algorithm of the intelligent cluster realizes the independent evaluation of rewards and the optimization of relationship adaptation. In the experimental scenario of electric vehicle charging, considering charging piles as agents, under the home charging mode, the self-organizing charging scheduling can reduce the total load range by up to 90.37%. It effectively shifts the load demand from peak periods to valley periods, minimizes the total peak–valley load difference, and significantly improves the security and reliability of the microgrid, thus providing a practical solution for resource allocation in intelligent clusters. |
| format | Article |
| id | doaj-art-a013e1e01847465e91c63d9595dc2dff |
| institution | OA Journals |
| issn | 2032-6653 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj-art-a013e1e01847465e91c63d9595dc2dff2025-08-20T01:56:38ZengMDPI AGWorld Electric Vehicle Journal2032-66532025-05-0116528410.3390/wevj16050284Resource Optimization Method Based on Spatio-Temporal Modeling in a Complex Cluster Environment for Electric Vehicle Charging ScenariosHongwei Wang0Wei Liu1Chenghui Wang2Kao Guo3Zihao Wang4Hubei Key Laboratory of Intelligent Robot, the School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430205, ChinaHubei Key Laboratory of Intelligent Robot, the School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430205, ChinaHubei Key Laboratory of Intelligent Robot, the School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430205, ChinaHubei Key Laboratory of Intelligent Robot, the School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430205, ChinaHubei Key Laboratory of Intelligent Robot, the School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430205, ChinaIn intelligent cluster systems, the spatio-temporal complexity of agent data collection and resource allocation, as well as the problems in collaborative organizations, present substantial challenges to efficient resource distribution. To address this, a novel self-organizing prediction method for spatio-temporal resource allocation is proposed. In the spatio-temporal modeling part, dilated convolution is applied for time modeling. Its dilation rate grows exponentially with the layer depth, allowing it to effectively capture the time trends of graph nodes and handle long time series data. For spatial modeling, an innovative dual-view dynamic graph convolutional network architecture is utilized to accurately explore the static and dynamic correlation information of the spatial layout of charging piles. Meanwhile, a composite self-organizing mechanism integrating a trust model is put forward. The trust model assists agents in choosing partners, and the Q-learning algorithm of the intelligent cluster realizes the independent evaluation of rewards and the optimization of relationship adaptation. In the experimental scenario of electric vehicle charging, considering charging piles as agents, under the home charging mode, the self-organizing charging scheduling can reduce the total load range by up to 90.37%. It effectively shifts the load demand from peak periods to valley periods, minimizes the total peak–valley load difference, and significantly improves the security and reliability of the microgrid, thus providing a practical solution for resource allocation in intelligent clusters.https://www.mdpi.com/2032-6653/16/5/284intelligent cluster environmentspatio-temporalresource allocationself-organizationelectric vehicle charging scenarios |
| spellingShingle | Hongwei Wang Wei Liu Chenghui Wang Kao Guo Zihao Wang Resource Optimization Method Based on Spatio-Temporal Modeling in a Complex Cluster Environment for Electric Vehicle Charging Scenarios World Electric Vehicle Journal intelligent cluster environment spatio-temporal resource allocation self-organization electric vehicle charging scenarios |
| title | Resource Optimization Method Based on Spatio-Temporal Modeling in a Complex Cluster Environment for Electric Vehicle Charging Scenarios |
| title_full | Resource Optimization Method Based on Spatio-Temporal Modeling in a Complex Cluster Environment for Electric Vehicle Charging Scenarios |
| title_fullStr | Resource Optimization Method Based on Spatio-Temporal Modeling in a Complex Cluster Environment for Electric Vehicle Charging Scenarios |
| title_full_unstemmed | Resource Optimization Method Based on Spatio-Temporal Modeling in a Complex Cluster Environment for Electric Vehicle Charging Scenarios |
| title_short | Resource Optimization Method Based on Spatio-Temporal Modeling in a Complex Cluster Environment for Electric Vehicle Charging Scenarios |
| title_sort | resource optimization method based on spatio temporal modeling in a complex cluster environment for electric vehicle charging scenarios |
| topic | intelligent cluster environment spatio-temporal resource allocation self-organization electric vehicle charging scenarios |
| url | https://www.mdpi.com/2032-6653/16/5/284 |
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