Collaborative Optimization Scheduling Strategy for Electric Vehicle Charging Stations Considering Spatiotemporal Distribution of Different Power Charging Demands
The rapid growth of electric vehicle (EV) adoption has led to an increased demand for charging infrastructure, creating significant challenges for power grid load management and dispatch optimization. This paper addresses these challenges by proposing a coordinated optimization dispatch strategy for...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | World Electric Vehicle Journal |
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| Online Access: | https://www.mdpi.com/2032-6653/16/3/176 |
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| author | Hongxin Liu Aiping Pang Jie Yin Haixia Yi Huqun Mu |
| author_facet | Hongxin Liu Aiping Pang Jie Yin Haixia Yi Huqun Mu |
| author_sort | Hongxin Liu |
| collection | DOAJ |
| description | The rapid growth of electric vehicle (EV) adoption has led to an increased demand for charging infrastructure, creating significant challenges for power grid load management and dispatch optimization. This paper addresses these challenges by proposing a coordinated optimization dispatch strategy for EV charging, which integrates time, space, and varying power requirements. This study develops a dynamic spatiotemporal distribution model that accounts for charging demand at different power levels, traffic network characteristics, and congestion factors, providing a more accurate simulation of charging demand in dynamic traffic conditions. A comprehensive optimization framework is introduced, and is designed to reduce peak congestion, enhance service efficiency, and optimize system performance. This framework dynamically adjusts the selection of charging stations (CSs), charging times, and charging types, with a focus on improving user satisfaction, balancing the grid load, and minimizing electricity purchase costs. To solve the optimization model, a hybrid approach combining particle swarm optimization (PSO) and the TOPSIS method is employed. PSO optimizes the overall objective function, while the TOPSIS method evaluates user satisfaction. The results highlight the effectiveness of the proposed strategy in improving system performance and providing a balanced, efficient EV charging solution. |
| format | Article |
| id | doaj-art-e58881576171404bbc0f27f89990758b |
| institution | DOAJ |
| issn | 2032-6653 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj-art-e58881576171404bbc0f27f89990758b2025-08-20T02:43:05ZengMDPI AGWorld Electric Vehicle Journal2032-66532025-03-0116317610.3390/wevj16030176Collaborative Optimization Scheduling Strategy for Electric Vehicle Charging Stations Considering Spatiotemporal Distribution of Different Power Charging DemandsHongxin Liu0Aiping Pang1Jie Yin2Haixia Yi3Huqun Mu4Electrical Engineering College, Guizhou University, Guiyang 550025, ChinaElectrical Engineering College, Guizhou University, Guiyang 550025, ChinaElectrical Engineering College, Guizhou University, Guiyang 550025, ChinaElectrical Engineering College, Guizhou University, Guiyang 550025, ChinaElectrical Engineering College, Guizhou University, Guiyang 550025, ChinaThe rapid growth of electric vehicle (EV) adoption has led to an increased demand for charging infrastructure, creating significant challenges for power grid load management and dispatch optimization. This paper addresses these challenges by proposing a coordinated optimization dispatch strategy for EV charging, which integrates time, space, and varying power requirements. This study develops a dynamic spatiotemporal distribution model that accounts for charging demand at different power levels, traffic network characteristics, and congestion factors, providing a more accurate simulation of charging demand in dynamic traffic conditions. A comprehensive optimization framework is introduced, and is designed to reduce peak congestion, enhance service efficiency, and optimize system performance. This framework dynamically adjusts the selection of charging stations (CSs), charging times, and charging types, with a focus on improving user satisfaction, balancing the grid load, and minimizing electricity purchase costs. To solve the optimization model, a hybrid approach combining particle swarm optimization (PSO) and the TOPSIS method is employed. PSO optimizes the overall objective function, while the TOPSIS method evaluates user satisfaction. The results highlight the effectiveness of the proposed strategy in improving system performance and providing a balanced, efficient EV charging solution.https://www.mdpi.com/2032-6653/16/3/176different power charging demandsspatiotemporal distributioncollaborative optimization schedulingroad network integration |
| spellingShingle | Hongxin Liu Aiping Pang Jie Yin Haixia Yi Huqun Mu Collaborative Optimization Scheduling Strategy for Electric Vehicle Charging Stations Considering Spatiotemporal Distribution of Different Power Charging Demands World Electric Vehicle Journal different power charging demands spatiotemporal distribution collaborative optimization scheduling road network integration |
| title | Collaborative Optimization Scheduling Strategy for Electric Vehicle Charging Stations Considering Spatiotemporal Distribution of Different Power Charging Demands |
| title_full | Collaborative Optimization Scheduling Strategy for Electric Vehicle Charging Stations Considering Spatiotemporal Distribution of Different Power Charging Demands |
| title_fullStr | Collaborative Optimization Scheduling Strategy for Electric Vehicle Charging Stations Considering Spatiotemporal Distribution of Different Power Charging Demands |
| title_full_unstemmed | Collaborative Optimization Scheduling Strategy for Electric Vehicle Charging Stations Considering Spatiotemporal Distribution of Different Power Charging Demands |
| title_short | Collaborative Optimization Scheduling Strategy for Electric Vehicle Charging Stations Considering Spatiotemporal Distribution of Different Power Charging Demands |
| title_sort | collaborative optimization scheduling strategy for electric vehicle charging stations considering spatiotemporal distribution of different power charging demands |
| topic | different power charging demands spatiotemporal distribution collaborative optimization scheduling road network integration |
| url | https://www.mdpi.com/2032-6653/16/3/176 |
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