Optimal Planning of Charging for Plug-In Electric Vehicles Focusing on Users’ Benefits
Many electric vehicles’ (EVs) charging strategies were proposed to optimize the operations of the power grid, while few focus on users’ benefits from the viewpoint of EV users. However, low participation is always a problem of those strategies since EV users also need a charging strategy to serve th...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2017-07-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/10/7/952 |
| _version_ | 1828371991866900480 |
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| author | Su Su Hao Li David Wenzhong Gao |
| author_facet | Su Su Hao Li David Wenzhong Gao |
| author_sort | Su Su |
| collection | DOAJ |
| description | Many electric vehicles’ (EVs) charging strategies were proposed to optimize the operations of the power grid, while few focus on users’ benefits from the viewpoint of EV users. However, low participation is always a problem of those strategies since EV users also need a charging strategy to serve their needs and interests. This paper proposes a method focusing on EV users’ benefits that reduce the cost of battery capacity degradation, electricity cost, and waiting time for different situations. A cost model of battery capacity degradation under different state of charge (SOC) ranges is developed based on experimental data to estimate the cost of battery degradation. The simulation results show that the appropriate planning of the SOC range reduces 80% of the cost of battery degradation, and the queuing theory also reduces over 60% of the waiting time in the busy situations. Those works can also become a premise of charging management to increase the participation. The proposed strategy focusing on EV users’ benefits would not give negative impacts on the power grid, and the grid load is also optimized by an artificial fish swarm algorithm (AFSA) in the solution space of the charging time restricted by EV users’ benefits. |
| first_indexed | 2024-04-14T06:56:56Z |
| format | Article |
| id | doaj.art-4c2676f458954234b696e077d7c1292b |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-14T06:56:56Z |
| publishDate | 2017-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-4c2676f458954234b696e077d7c1292b2022-12-22T02:06:51ZengMDPI AGEnergies1996-10732017-07-0110795210.3390/en10070952en10070952Optimal Planning of Charging for Plug-In Electric Vehicles Focusing on Users’ BenefitsSu Su0Hao Li1David Wenzhong Gao2National Active Distribution Network Technology Research Center, Beijing Jiaotong University, Beijing 100044, ChinaNational Active Distribution Network Technology Research Center, Beijing Jiaotong University, Beijing 100044, ChinaDepartment of Electrical and Computer Engineering, University of Denver, Denver, CO 80210, USAMany electric vehicles’ (EVs) charging strategies were proposed to optimize the operations of the power grid, while few focus on users’ benefits from the viewpoint of EV users. However, low participation is always a problem of those strategies since EV users also need a charging strategy to serve their needs and interests. This paper proposes a method focusing on EV users’ benefits that reduce the cost of battery capacity degradation, electricity cost, and waiting time for different situations. A cost model of battery capacity degradation under different state of charge (SOC) ranges is developed based on experimental data to estimate the cost of battery degradation. The simulation results show that the appropriate planning of the SOC range reduces 80% of the cost of battery degradation, and the queuing theory also reduces over 60% of the waiting time in the busy situations. Those works can also become a premise of charging management to increase the participation. The proposed strategy focusing on EV users’ benefits would not give negative impacts on the power grid, and the grid load is also optimized by an artificial fish swarm algorithm (AFSA) in the solution space of the charging time restricted by EV users’ benefits.https://www.mdpi.com/1996-1073/10/7/952electric vehiclecost model of battery degradationcharging managementoptimal schedulingload controlMonte Carlo |
| spellingShingle | Su Su Hao Li David Wenzhong Gao Optimal Planning of Charging for Plug-In Electric Vehicles Focusing on Users’ Benefits Energies electric vehicle cost model of battery degradation charging management optimal scheduling load control Monte Carlo |
| title | Optimal Planning of Charging for Plug-In Electric Vehicles Focusing on Users’ Benefits |
| title_full | Optimal Planning of Charging for Plug-In Electric Vehicles Focusing on Users’ Benefits |
| title_fullStr | Optimal Planning of Charging for Plug-In Electric Vehicles Focusing on Users’ Benefits |
| title_full_unstemmed | Optimal Planning of Charging for Plug-In Electric Vehicles Focusing on Users’ Benefits |
| title_short | Optimal Planning of Charging for Plug-In Electric Vehicles Focusing on Users’ Benefits |
| title_sort | optimal planning of charging for plug in electric vehicles focusing on users benefits |
| topic | electric vehicle cost model of battery degradation charging management optimal scheduling load control Monte Carlo |
| url | https://www.mdpi.com/1996-1073/10/7/952 |
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