Health-aware V2G scheduling: A multi-modal transformer optimization framework

Vehicle-to-Grid (V2G) systems offer radical capabilities to grid stability and integrate renewable energy, but are not being utilized due to shortsighted scheduling and non-consideration of long-term battery health, and poor modeling of uncertainties in user behavior, grid behavior, and degradation. To eliminate such barriers, this paper will suggest a Multi-Mode Transformer-Based Optimization Framework. Our framework incorporates three predictors, each proposed under the Transformer framework, of user behavior, grid prices, and battery State-of-Health (SOH) to predict these variables unlike conventional models like LSTM, GRU, and ARIMA, which are proven to be robust through the use of empirical data. These forecasts are used to run a multi-objective optimization engine, which is incorporated in a receding horizon Model Predictive Control (MPC) scheme. The key of the innovations is the iterative degradation sensitive refinement mechanism. This supports a closed-loop feedback design which would be able to smoothly incorporate state-of-health degradation predictions into the optimization process. On-the-road experiments demonstrate high-performance attaining 1.8 times reduced battery depreciation and 8 percentage cumulative revenues of a 100 EV fleet in 12 months. The framework provides sustainable high-fidelity solution which matches grid service contribution with battery capacity in the face of uncertainty.