FedPT-V2G: Security enhanced federated transformer learning for real-time V2G dispatch with non-IID data.

The rising popularity of electric vehicles (EVs) underscores the potential of vehicle-to-grid (V2G) technology to contribute to load peak-shaving, valley-filling, and photovoltaic (PV) self-consumption. Effective V2G control strategies can be obtained by data-driven techniques, which is able to leverage historical and current data to inform future decision-making amidst uncertainties. However, the centralized collection and sharing of data among charging stations face challenges due to data asset concerns. Furthermore, even if data sharing hurdles are overcome, the non-independent and non-identically distributed (Non-IID) nature of data across charging stations can still negatively impact performance. In this study, we introduce FedPT-V2G, a security-enhanced federated transformer learning approach for real-time V2G dispatch that addresses Non-IID data. We employ deep learning models trained on historical and current data to enable real-time decision-making, facilitating both load shifting and PV self-consumption. Additionally, we utilize federated learning to jointly train a global model across all charging stations without collecting or sharing any local private data. We pioneer the application of the Proximal algorithm and Transformer model to tackle data distribution discrepancies within the V2G scheduling prediction task. The Proximal algorithm employs regularization techniques to align local models at each charging station more closely with the global model during updates. Concurrently, the multi-head attention mechanism within the Transformer model allows learned feature vectors to diverge, enabling better exploitation of variations across the entire feature space. Finally, we validate the proposed FedPT-V2G approach through extensive numerical simulations, demonstrating comparable performance to centralized learning on both balanced (98.93% vs 98.65%) and imbalanced (92.15% vs 92.20% in label skew) datasets. • End-to-end learning based on historical data to make V2G decisions in real-time. • Enable load shifting and PV self-consumption under diverse uncertainties. • Apply Proximal algorithm and Transformer model to handle Non-IID data in V2G task. • Verify the proposed FedPT-V2G approach through extensive numerical simulations. • Achieve similar performance to centralized learning on both IID and Non-IID data. [ABSTRACT FROM AUTHOR]