CDT-RiskNet: A risk-aware copula-diffusion-transformer network for stochastic EV charging station optimization in energy market

Wang, Xihao, Wang, Xiaojun, Liu, Zhao, Wu, Jianzhong ORCID: https://orcid.org/0000-0001-7928-3602 and He, Jinghan 2026. CDT-RiskNet: A risk-aware copula-diffusion-transformer network for stochastic EV charging station optimization in energy market. IEEE Transactions on Sustainable Energy 10.1109/tste.2026.3663278

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Abstract

The growing penetration of photovoltaic (PV) systems and electric vehicles (EVs) poses systemic uncertainty for EV charging stations (EVCSs) participating in day-ahead energy market. These uncertainties are characterized by complex dependencies and time-varying tail risks that challenge traditional scenario generation and risk management techniques. This paper presents CDT-RiskNet, a Copula-Diffusion-Transformer framework for risk-aware stochastic optimization. A copula-enhanced diffusion model is developed to generate realistic joint scenarios of PV generation and EV charging demand. To manage time-varying tail risk, a Transformer-based risk module predicts dynamic CVaR weights from both historical and forecasted features, enabling coordinated evaluation across time. Simulation resultson both small-scale and large-scale EVCSs using real-world data demonstrate that CDT-RiskNet improves scenario generation quality, risk control, and adaptability to varying market conditions, leading to better economic performance under uncertainty.

Item Type:	Article
Date Type:	Published Online
Status:	In Press
Schools:	Schools > Engineering
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
ISSN:	1949-3029
Last Modified:	23 Feb 2026 13:45
URI:	https://orca.cardiff.ac.uk/id/eprint/185121

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