Multi-Timescale Battery-Charging Optimization for Electric Heavy-Duty Truck Battery-Swapping Stations, Considering Source–Load–Storage Uncertainty
With the widespread adoption of renewable energy sources like wind power and photovoltaic (PV) power, uncertainties in the renewable energy output and the battery-swapping demand for electric heavy-duty trucks make it challenging for battery-swapping stations to optimize battery-charging management...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/2/241 |
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| _version_ | 1832588630433988608 |
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| author | Peijun Shi Guojian Ni Rifeng Jin Haibo Wang Jinsong Wang Zhongwei Sun Guizhi Qiu |
| author_facet | Peijun Shi Guojian Ni Rifeng Jin Haibo Wang Jinsong Wang Zhongwei Sun Guizhi Qiu |
| author_sort | Peijun Shi |
| collection | DOAJ |
| description | With the widespread adoption of renewable energy sources like wind power and photovoltaic (PV) power, uncertainties in the renewable energy output and the battery-swapping demand for electric heavy-duty trucks make it challenging for battery-swapping stations to optimize battery-charging management centrally. Uncoordinated large-scale charging behavior can increase operation costs for battery-swapping stations and even affect the stability of the power grid. To mitigate this, this paper proposes a multi-timescale battery-charging optimization for electric heavy-duty truck battery-swapping stations, taking into account the source–load–storage uncertainty. First, a model incorporating uncertainties in renewable energy output, time-of-use pricing, and grid load fluctuations is developed for the battery-swapping station. Second, based on day-ahead and intra-day timescales, the optimization problem for battery-charging strategies at battery-swapping stations is decomposed into day-ahead and intra-day optimization problems. We propose a day-ahead charging strategy optimization algorithm based on intra-day optimization feedback information-gap decision theory (IGDT) and an improved grasshopper algorithm for intra-day charging strategy optimization. The key contributions include the following: (1) the development of a battery-charging model for electric heavy-duty truck battery-swapping stations that accounts for the uncertainty in the power output of energy sources, loads, and storage; (2) the proposal of a day-ahead battery-charging optimization algorithm based on intra-day-optimization feedback information-gap decision theory (IGDT), which allows for dynamic adjustment of risk preferences; (3) the proposal of an intra-day battery-charging optimization algorithm based on an improved grasshopper optimization algorithm, which enhances algorithm convergence speed and stability, avoiding local optima. Finally, simulation comparisons confirm the success of the proposed approach. The simulation results demonstrate that the proposed method reduces charging costs by 4.26% and 6.03% compared with the two baseline algorithms, respectively, and improves grid stability, highlighting its effectiveness for managing battery-swapping stations under uncertainty. |
| format | Article |
| id | doaj-art-749b25d580b54bf5aafbf68ba91fc346 |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-749b25d580b54bf5aafbf68ba91fc3462025-01-24T13:30:45ZengMDPI AGEnergies1996-10732025-01-0118224110.3390/en18020241Multi-Timescale Battery-Charging Optimization for Electric Heavy-Duty Truck Battery-Swapping Stations, Considering Source–Load–Storage UncertaintyPeijun Shi0Guojian Ni1Rifeng Jin2Haibo Wang3Jinsong Wang4Zhongwei Sun5Guizhi Qiu6Datang Beijing Tianjin Hebei Energy Marketing Co., Ltd., Beijing 100031, ChinaSchool of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, ChinaDatang Henan Power Generation Co., Ltd., Zhengzhou 450000, ChinaDatang Beijing Tianjin Hebei Energy Marketing Co., Ltd., Beijing 100031, ChinaChina Datang Corporation Science and Technology General Research Institute North China Electric Power Test and Research Institute, Beijing 100043, ChinaSchool of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, ChinaChina Datang Corporation Science and Technology General Research Institute North China Electric Power Test and Research Institute, Beijing 100043, ChinaWith the widespread adoption of renewable energy sources like wind power and photovoltaic (PV) power, uncertainties in the renewable energy output and the battery-swapping demand for electric heavy-duty trucks make it challenging for battery-swapping stations to optimize battery-charging management centrally. Uncoordinated large-scale charging behavior can increase operation costs for battery-swapping stations and even affect the stability of the power grid. To mitigate this, this paper proposes a multi-timescale battery-charging optimization for electric heavy-duty truck battery-swapping stations, taking into account the source–load–storage uncertainty. First, a model incorporating uncertainties in renewable energy output, time-of-use pricing, and grid load fluctuations is developed for the battery-swapping station. Second, based on day-ahead and intra-day timescales, the optimization problem for battery-charging strategies at battery-swapping stations is decomposed into day-ahead and intra-day optimization problems. We propose a day-ahead charging strategy optimization algorithm based on intra-day optimization feedback information-gap decision theory (IGDT) and an improved grasshopper algorithm for intra-day charging strategy optimization. The key contributions include the following: (1) the development of a battery-charging model for electric heavy-duty truck battery-swapping stations that accounts for the uncertainty in the power output of energy sources, loads, and storage; (2) the proposal of a day-ahead battery-charging optimization algorithm based on intra-day-optimization feedback information-gap decision theory (IGDT), which allows for dynamic adjustment of risk preferences; (3) the proposal of an intra-day battery-charging optimization algorithm based on an improved grasshopper optimization algorithm, which enhances algorithm convergence speed and stability, avoiding local optima. Finally, simulation comparisons confirm the success of the proposed approach. The simulation results demonstrate that the proposed method reduces charging costs by 4.26% and 6.03% compared with the two baseline algorithms, respectively, and improves grid stability, highlighting its effectiveness for managing battery-swapping stations under uncertainty.https://www.mdpi.com/1996-1073/18/2/241electric heavy-duty truck battery-swapping stationsource–load–storage uncertaintymulti-timescale optimizationbattery-charging optimizationimproved IGDT |
| spellingShingle | Peijun Shi Guojian Ni Rifeng Jin Haibo Wang Jinsong Wang Zhongwei Sun Guizhi Qiu Multi-Timescale Battery-Charging Optimization for Electric Heavy-Duty Truck Battery-Swapping Stations, Considering Source–Load–Storage Uncertainty Energies electric heavy-duty truck battery-swapping station source–load–storage uncertainty multi-timescale optimization battery-charging optimization improved IGDT |
| title | Multi-Timescale Battery-Charging Optimization for Electric Heavy-Duty Truck Battery-Swapping Stations, Considering Source–Load–Storage Uncertainty |
| title_full | Multi-Timescale Battery-Charging Optimization for Electric Heavy-Duty Truck Battery-Swapping Stations, Considering Source–Load–Storage Uncertainty |
| title_fullStr | Multi-Timescale Battery-Charging Optimization for Electric Heavy-Duty Truck Battery-Swapping Stations, Considering Source–Load–Storage Uncertainty |
| title_full_unstemmed | Multi-Timescale Battery-Charging Optimization for Electric Heavy-Duty Truck Battery-Swapping Stations, Considering Source–Load–Storage Uncertainty |
| title_short | Multi-Timescale Battery-Charging Optimization for Electric Heavy-Duty Truck Battery-Swapping Stations, Considering Source–Load–Storage Uncertainty |
| title_sort | multi timescale battery charging optimization for electric heavy duty truck battery swapping stations considering source load storage uncertainty |
| topic | electric heavy-duty truck battery-swapping station source–load–storage uncertainty multi-timescale optimization battery-charging optimization improved IGDT |
| url | https://www.mdpi.com/1996-1073/18/2/241 |
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