Optimization of In-Motion EV Charging Infrastructure for Power Systems Using Generative Adversarial Network-Based Distributionally Robust Techniques
This paper presents an innovative optimization framework for the co-management of dynamic electric vehicle (EV) charging lanes and power distribution networks, addressing grid stability amidst fluctuating EV charging demands. Integrating generative adversarial networks (GANs) and distributionally ro...
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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/1996-1073/18/2/297 |
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| author | Dong Hua Peifeng Yan Suisheng Liu Qinglin Lin Peiyi Cui Qian Li |
| author_facet | Dong Hua Peifeng Yan Suisheng Liu Qinglin Lin Peiyi Cui Qian Li |
| author_sort | Dong Hua |
| collection | DOAJ |
| description | This paper presents an innovative optimization framework for the co-management of dynamic electric vehicle (EV) charging lanes and power distribution networks, addressing grid stability amidst fluctuating EV charging demands. Integrating generative adversarial networks (GANs) and distributionally robust optimization (DRO), the framework models uncertainties in traffic flow and renewable energy generation, optimizing system performance under worst-case conditions to mitigate risks of grid instability. Applied to a highway with eight dynamic charging lanes (500 kW per lane), serving up to 50 EVs simultaneously, the framework balances energy contributions from 15 renewable generators (60% of the mix) and 10 non-renewable generators. Simulation results highlight its effectiveness, maintaining grid stability with voltage deviations within 0.02 p.u., reducing energy losses to under 0.8 MW during peak traffic (1500 vehicles per hour), and achieving 95% lane utilization. Dynamic charging enabled EV users to save USD 0.08 per kilometer through reduced stationary charging downtime, optimized travel efficiency, and lower energy costs. Additionally, the system minimizes maintenance costs by optimizing lane and grid reliability. This study underscores the potential of GAN-based DRO methodologies to enhance the efficiency of power grids supporting dynamic EV charging, offering scalable solutions for diverse regions and traffic scenarios. |
| format | Article |
| id | doaj-art-3f5e4c2fb1fc4641bb450c777440b722 |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-3f5e4c2fb1fc4641bb450c777440b7222025-01-24T13:30:56ZengMDPI AGEnergies1996-10732025-01-0118229710.3390/en18020297Optimization of In-Motion EV Charging Infrastructure for Power Systems Using Generative Adversarial Network-Based Distributionally Robust TechniquesDong Hua0Peifeng Yan1Suisheng Liu2Qinglin Lin3Peiyi Cui4Qian Li5Department of Electrical Engineering, South China University of Technology, 381 Wushan Road, Tianhe District, Guangzhou 510641, ChinaDepartment of Electrical Engineering, South China University of Technology, 381 Wushan Road, Tianhe District, Guangzhou 510641, ChinaGuangdong KingWa Energy Technology Co., Ltd., No. 88, Industry Avenue North, Guangzhou 510000, ChinaGuangdong KingWa Energy Technology Co., Ltd., No. 88, Industry Avenue North, Guangzhou 510000, ChinaGuangdong KingWa Energy Technology Co., Ltd., No. 88, Industry Avenue North, Guangzhou 510000, ChinaEnergy Development Research Institute, CSG, No. 9, Nanling Avenue, Tianhe District, Guangzhou 510665, ChinaThis paper presents an innovative optimization framework for the co-management of dynamic electric vehicle (EV) charging lanes and power distribution networks, addressing grid stability amidst fluctuating EV charging demands. Integrating generative adversarial networks (GANs) and distributionally robust optimization (DRO), the framework models uncertainties in traffic flow and renewable energy generation, optimizing system performance under worst-case conditions to mitigate risks of grid instability. Applied to a highway with eight dynamic charging lanes (500 kW per lane), serving up to 50 EVs simultaneously, the framework balances energy contributions from 15 renewable generators (60% of the mix) and 10 non-renewable generators. Simulation results highlight its effectiveness, maintaining grid stability with voltage deviations within 0.02 p.u., reducing energy losses to under 0.8 MW during peak traffic (1500 vehicles per hour), and achieving 95% lane utilization. Dynamic charging enabled EV users to save USD 0.08 per kilometer through reduced stationary charging downtime, optimized travel efficiency, and lower energy costs. Additionally, the system minimizes maintenance costs by optimizing lane and grid reliability. This study underscores the potential of GAN-based DRO methodologies to enhance the efficiency of power grids supporting dynamic EV charging, offering scalable solutions for diverse regions and traffic scenarios.https://www.mdpi.com/1996-1073/18/2/297dynamic charging laneselectric vehicle chargingpower distribution optimizationgenerative adversarial networks (GAN)distributionally robust optimization (DRO)renewable energy integration |
| spellingShingle | Dong Hua Peifeng Yan Suisheng Liu Qinglin Lin Peiyi Cui Qian Li Optimization of In-Motion EV Charging Infrastructure for Power Systems Using Generative Adversarial Network-Based Distributionally Robust Techniques Energies dynamic charging lanes electric vehicle charging power distribution optimization generative adversarial networks (GAN) distributionally robust optimization (DRO) renewable energy integration |
| title | Optimization of In-Motion EV Charging Infrastructure for Power Systems Using Generative Adversarial Network-Based Distributionally Robust Techniques |
| title_full | Optimization of In-Motion EV Charging Infrastructure for Power Systems Using Generative Adversarial Network-Based Distributionally Robust Techniques |
| title_fullStr | Optimization of In-Motion EV Charging Infrastructure for Power Systems Using Generative Adversarial Network-Based Distributionally Robust Techniques |
| title_full_unstemmed | Optimization of In-Motion EV Charging Infrastructure for Power Systems Using Generative Adversarial Network-Based Distributionally Robust Techniques |
| title_short | Optimization of In-Motion EV Charging Infrastructure for Power Systems Using Generative Adversarial Network-Based Distributionally Robust Techniques |
| title_sort | optimization of in motion ev charging infrastructure for power systems using generative adversarial network based distributionally robust techniques |
| topic | dynamic charging lanes electric vehicle charging power distribution optimization generative adversarial networks (GAN) distributionally robust optimization (DRO) renewable energy integration |
| url | https://www.mdpi.com/1996-1073/18/2/297 |
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