  Stochastic battery bidding in real-time energy markets is a nuanced process,
with its efficacy depending on the accuracy of forecasts and the representative
scenarios chosen for optimization. In this paper, we introduce a pioneering
methodology that amalgamates Transformer-based forecasting with weighted
constrained Dynamic Time Warping (wcDTW) to refine scenario selection. Our
approach harnesses the predictive capabilities of Transformers to foresee
Energy prices, while wcDTW ensures the selection of pertinent historical
scenarios by maintaining the coherence between multiple uncertain products.
Through extensive simulations in the PJM market for July 2023, our method
exhibited a 10% increase in revenue compared to the conventional method,
highlighting its potential to revolutionize battery bidding strategies in
real-time markets.
