Multi Objective Optimization of Electric Vehicle Charging Strategy Considering User Selectivity
Electric vehicles (EVs) are increasing in number every year, and large-scale uncontrolled EV charging can impose significant load pressure on the power grid (PG), affecting its stability and economy. This paper proposes an EV charging strategy that considers user selectivity. The user’s selection st...
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MDPI AG
2025-02-01
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| Series: | World Electric Vehicle Journal |
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| Online Access: | https://www.mdpi.com/2032-6653/16/2/95 |
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| author | Sheng Li Xiangyu Yan Guanhua Wang |
| author_facet | Sheng Li Xiangyu Yan Guanhua Wang |
| author_sort | Sheng Li |
| collection | DOAJ |
| description | Electric vehicles (EVs) are increasing in number every year, and large-scale uncontrolled EV charging can impose significant load pressure on the power grid (PG), affecting its stability and economy. This paper proposes an EV charging strategy that considers user selectivity. The user’s selection strategy includes options for fast and slow charging types, as well as the choice of whether to comply with grid-controlled charging. Charging types are selected based on the ability to reach the desired state of charge (SOC), while compliance with grid-controlled charging is determined by comparing the unit charging cost (CC). An objective function is established to minimize the peak valley load difference (PVLD) rate of PGs and users’ CC. To achieve this, an improved non-dominated sorting whale optimization algorithm (INSWOA) is proposed which initializes the population through logistic mapping, introduces nonlinear convergence factors for position updates, and uses adaptive inertia weights to improve population diversity, enhance global optimization ability, reduce premature convergence, and improve solution accuracy. Finally, simulating distribution networks in a certain region, the results obtained from the INSWOA were compared with those from the non-dominated sorting whale optimization algorithm (NSWOA) and other algorithms. The comparisons demonstrated that the INSWOA significantly reduced the PVLD rate of the PG load and users’ CCs, highlighting its high practical value. |
| format | Article |
| id | doaj-art-b2a795a48fa94053b06b37bd77cdf376 |
| institution | DOAJ |
| issn | 2032-6653 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj-art-b2a795a48fa94053b06b37bd77cdf3762025-08-20T02:45:30ZengMDPI AGWorld Electric Vehicle Journal2032-66532025-02-011629510.3390/wevj16020095Multi Objective Optimization of Electric Vehicle Charging Strategy Considering User SelectivitySheng Li0Xiangyu Yan1Guanhua Wang2School of Electric Power Engineering, School of Shenguorong, Nanjing Institute of Technology, Nanjing 211167, ChinaSchool of Electric Power Engineering, School of Shenguorong, Nanjing Institute of Technology, Nanjing 211167, ChinaSchool of Electric Power Engineering, School of Shenguorong, Nanjing Institute of Technology, Nanjing 211167, ChinaElectric vehicles (EVs) are increasing in number every year, and large-scale uncontrolled EV charging can impose significant load pressure on the power grid (PG), affecting its stability and economy. This paper proposes an EV charging strategy that considers user selectivity. The user’s selection strategy includes options for fast and slow charging types, as well as the choice of whether to comply with grid-controlled charging. Charging types are selected based on the ability to reach the desired state of charge (SOC), while compliance with grid-controlled charging is determined by comparing the unit charging cost (CC). An objective function is established to minimize the peak valley load difference (PVLD) rate of PGs and users’ CC. To achieve this, an improved non-dominated sorting whale optimization algorithm (INSWOA) is proposed which initializes the population through logistic mapping, introduces nonlinear convergence factors for position updates, and uses adaptive inertia weights to improve population diversity, enhance global optimization ability, reduce premature convergence, and improve solution accuracy. Finally, simulating distribution networks in a certain region, the results obtained from the INSWOA were compared with those from the non-dominated sorting whale optimization algorithm (NSWOA) and other algorithms. The comparisons demonstrated that the INSWOA significantly reduced the PVLD rate of the PG load and users’ CCs, highlighting its high practical value.https://www.mdpi.com/2032-6653/16/2/95EVsselective strategycharging cost (CC)peak valley load difference (PVLD)optimize schedulingnon-dominated sorting whale optimization algorithm (NSWOA) |
| spellingShingle | Sheng Li Xiangyu Yan Guanhua Wang Multi Objective Optimization of Electric Vehicle Charging Strategy Considering User Selectivity World Electric Vehicle Journal EVs selective strategy charging cost (CC) peak valley load difference (PVLD) optimize scheduling non-dominated sorting whale optimization algorithm (NSWOA) |
| title | Multi Objective Optimization of Electric Vehicle Charging Strategy Considering User Selectivity |
| title_full | Multi Objective Optimization of Electric Vehicle Charging Strategy Considering User Selectivity |
| title_fullStr | Multi Objective Optimization of Electric Vehicle Charging Strategy Considering User Selectivity |
| title_full_unstemmed | Multi Objective Optimization of Electric Vehicle Charging Strategy Considering User Selectivity |
| title_short | Multi Objective Optimization of Electric Vehicle Charging Strategy Considering User Selectivity |
| title_sort | multi objective optimization of electric vehicle charging strategy considering user selectivity |
| topic | EVs selective strategy charging cost (CC) peak valley load difference (PVLD) optimize scheduling non-dominated sorting whale optimization algorithm (NSWOA) |
| url | https://www.mdpi.com/2032-6653/16/2/95 |
| work_keys_str_mv | AT shengli multiobjectiveoptimizationofelectricvehiclechargingstrategyconsideringuserselectivity AT xiangyuyan multiobjectiveoptimizationofelectricvehiclechargingstrategyconsideringuserselectivity AT guanhuawang multiobjectiveoptimizationofelectricvehiclechargingstrategyconsideringuserselectivity |