Optimal dispatch of hydrogen/electric vehicle charging station based on charging decision prediction |
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| Affiliation: | 1. College of Electrical Engineering and Automation, Fuzhou University, 350108 Fuzhou, China;2. Fujian Smart Electrical Engineering Technology Research Center, Fuzhou University, 350108 Fuzhou, China;1. School of Rare Earths, University of Science and Technology of China, Hefei, 230026, PR China;2. Ganjiang Innovation Academy, Chinese Academy of Sciences, Ganzhou, 341000, PR China;3. Key Laboratory of Advanced Fuel Cells and Electrolyzers Technology of Zhejiang Province, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, Zhejiang, 315201, PR China;4. Guangzhou Power Supply Bureau, Guangdong Power Grid Co., Ltd., No. 2 of Tianhe nan 2nd Rd, Guangzhou, Guangdong Province, 510620, PR China;1. College of Materials Science and Engineering, Qingdao University of Science and Technology, No. 53 Zhengzhou Road, Qingdao 266042, China;2. College of Electromechanical Engineering, Qingdao University of Science and Technology, No. 53 Zhengzhou Road, Qingdao 266042, China;1. Department of Advanced Energy Materials, College of Materials Science and Engineering, Sichuan University, Chengdu, 610064, PR China;2. Institute of New Energy and Low-Carbon Technology, Sichuan University, Chengdu, 610065, PR China;3. Engineering Research Center of Alternative Energy Materials & Devices, Ministry of Education, Chengdu, 610064, PR China;4. Technology Innovation Center of Hydrogen Storage-Transportation and Fueling Equipment for State Market Regulation, 610199, PR China |
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| Abstract: | The hydrogen/electric vehicle charging station (HEVCS) is widely regarded as a highly attractive system for facilitating the popularity of hydrogen and electric vehicles in the future. However, conventional optimal dispatch of HEVCS could lead to poor performance due to the lack of adequate consideration of vehicle charging decision behaviours and neglection of the impacts of different information sources on it. This paper investigates a charging demand prediction method that considers multi-source information and proposes a multi-objective optimal dispatching strategy of HEVCS. First, an information interaction framework of integrated road network, vehicles and HEVCS is introduced. Road network model and HEVCS model are established based on the proposed framework. To improve the flexibility of dispatch, two charging modes are designed, which are intended to guide drivers to adjust their consumption behaviour by electricity price incentives. Furthermore, psychologically based hybrid utility-regret decision model and Weber-Fechner (W–F) stimulus model are developed to reasonably predict drivers' choice of charging stations and charging modes. The daily revenue of HEVCS and the total queuing time of drivers are the objective functions considered in this paper simultaneously. The above multi-objective optimization results that the proposed strategy can effectively improve the benefits of HEVCS and reduce energy waste. Additionally, this paper discusses the results of a sensitivity analysis conducted by varying incentive discount, which reveals the combined benefits of the HEVCS and the vehicles are effectively increased by setting reasonable incentive discounts. |
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| Keywords: | Hydrogen/electricity vehicle charging station (HEVCS) Hydrogen and electric vehicles Multi-source information Charging decision prediction Dispatching strategy |
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