Optimization of In-Motion EV Charging Infrastructure for Power Systems Using Generative Adversarial Network-Based Distributionally Robust Techniques

Dong Hua, Peifeng Yan, Suisheng Liu, Qinglin Lin, Peiyi Cui () and Qian Li
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Dong Hua: Department of Electrical Engineering, South China University of Technology, 381 Wushan Road, Tianhe District, Guangzhou 510641, China
Peifeng Yan: Department of Electrical Engineering, South China University of Technology, 381 Wushan Road, Tianhe District, Guangzhou 510641, China
Suisheng Liu: Guangdong KingWa Energy Technology Co., Ltd., No. 88, Industry Avenue North, Guangzhou 510000, China
Qinglin Lin: Guangdong KingWa Energy Technology Co., Ltd., No. 88, Industry Avenue North, Guangzhou 510000, China
Peiyi Cui: Guangdong KingWa Energy Technology Co., Ltd., No. 88, Industry Avenue North, Guangzhou 510000, China
Qian Li: Energy Development Research Institute, CSG, No. 9, Nanling Avenue, Tianhe District, Guangzhou 510665, China

Energies, 2025, vol. 18, issue 2, 1-28

Abstract: 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.

Keywords: dynamic charging lanes; electric vehicle charging; power distribution optimization; generative adversarial networks (GAN); distributionally robust optimization (DRO); renewable energy integration; grid stability (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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