Optimal Placement of Charging Stations in Road Networks: A Reinforcement Learning Approach with Attention Mechanism
With the aim of promoting energy conservation and emission reduction, electric vehicles (EVs) have gained significant attention as a strategic industry in many countries. However, the insufficiency of accessible charging infrastructure remains a challenge, hindering the widespread adoption of EVs. T...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/14/8473 |
| _version_ | 1797590382879440896 |
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| author | Jiaqi Liu Jian Sun Xiao Qi |
| author_facet | Jiaqi Liu Jian Sun Xiao Qi |
| author_sort | Jiaqi Liu |
| collection | DOAJ |
| description | With the aim of promoting energy conservation and emission reduction, electric vehicles (EVs) have gained significant attention as a strategic industry in many countries. However, the insufficiency of accessible charging infrastructure remains a challenge, hindering the widespread adoption of EVs. To address this issue, we propose a novel approach to optimize the placement of charging stations within a road network, known as the charging station location problem (CSLP). Our method considers multiple factors, including fairness in charging station distribution, benefits associated with their placement, and drivers’ discomfort. Fairness is quantified by the balance in charging station coverage across the network, while driver comfort is measured by the total time spent during the charging process. Then, the CSLP is formulated as a reinforcement learning problem, and we introduce a novel model called PPO-Attention. This model incorporates an attention layer into the Proximal Policy Optimization (PPO) algorithm, enhancing the algorithm’s capacity to identify and understand the intricate interdependencies between different nodes in the network. We have conducted extensive tests on urban road networks in Europe, North America, and Asia. The results demonstrate the superior performance of our approach compared to existing baseline algorithms. On average, our method achieves a profit increase of 258.04% and reduces waiting time by 73.40%, travel time by 18.46%, and charging time by 40.10%. |
| first_indexed | 2024-03-11T01:19:41Z |
| format | Article |
| id | doaj.art-91d93191d29c4364918e101fcc8673e9 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T01:19:41Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-91d93191d29c4364918e101fcc8673e92023-11-18T18:13:37ZengMDPI AGApplied Sciences2076-34172023-07-011314847310.3390/app13148473Optimal Placement of Charging Stations in Road Networks: A Reinforcement Learning Approach with Attention MechanismJiaqi Liu0Jian Sun1Xiao Qi2Key Laboratory of Road and Traffic Engineering, Ministry of Education, College of Transportation Engineering, Tongji University, Shanghai 201804, ChinaKey Laboratory of Road and Traffic Engineering, Ministry of Education, College of Transportation Engineering, Tongji University, Shanghai 201804, ChinaKey Laboratory of Road and Traffic Engineering, Ministry of Education, College of Transportation Engineering, Tongji University, Shanghai 201804, ChinaWith the aim of promoting energy conservation and emission reduction, electric vehicles (EVs) have gained significant attention as a strategic industry in many countries. However, the insufficiency of accessible charging infrastructure remains a challenge, hindering the widespread adoption of EVs. To address this issue, we propose a novel approach to optimize the placement of charging stations within a road network, known as the charging station location problem (CSLP). Our method considers multiple factors, including fairness in charging station distribution, benefits associated with their placement, and drivers’ discomfort. Fairness is quantified by the balance in charging station coverage across the network, while driver comfort is measured by the total time spent during the charging process. Then, the CSLP is formulated as a reinforcement learning problem, and we introduce a novel model called PPO-Attention. This model incorporates an attention layer into the Proximal Policy Optimization (PPO) algorithm, enhancing the algorithm’s capacity to identify and understand the intricate interdependencies between different nodes in the network. We have conducted extensive tests on urban road networks in Europe, North America, and Asia. The results demonstrate the superior performance of our approach compared to existing baseline algorithms. On average, our method achieves a profit increase of 258.04% and reduces waiting time by 73.40%, travel time by 18.46%, and charging time by 40.10%.https://www.mdpi.com/2076-3417/13/14/8473location selectionreinforcement learningattention mechanismproximal policy optimization |
| spellingShingle | Jiaqi Liu Jian Sun Xiao Qi Optimal Placement of Charging Stations in Road Networks: A Reinforcement Learning Approach with Attention Mechanism Applied Sciences location selection reinforcement learning attention mechanism proximal policy optimization |
| title | Optimal Placement of Charging Stations in Road Networks: A Reinforcement Learning Approach with Attention Mechanism |
| title_full | Optimal Placement of Charging Stations in Road Networks: A Reinforcement Learning Approach with Attention Mechanism |
| title_fullStr | Optimal Placement of Charging Stations in Road Networks: A Reinforcement Learning Approach with Attention Mechanism |
| title_full_unstemmed | Optimal Placement of Charging Stations in Road Networks: A Reinforcement Learning Approach with Attention Mechanism |
| title_short | Optimal Placement of Charging Stations in Road Networks: A Reinforcement Learning Approach with Attention Mechanism |
| title_sort | optimal placement of charging stations in road networks a reinforcement learning approach with attention mechanism |
| topic | location selection reinforcement learning attention mechanism proximal policy optimization |
| url | https://www.mdpi.com/2076-3417/13/14/8473 |
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