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.

我们引入了一种创新方法，将基于Transformer的预测与加权约束动态时间规整（wcDTW）相结合，以优化实时能源市场中随机电池竞标策略，并通过在PJM市场上进行的广泛模拟，展示了与传统方法相比，我们的方法可使收益增加10％，突显其在实时市场中改变电池竞标策略的潜力。

使用Transformer遇到wcDTW以提高实时电池出价：一种新的情景选择方法