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.

翻译：在实时能源市场中进行随机电池竞价是一个精细的过程，其效果取决于预测的准确性以及为优化所选取的代表性场景。本文提出了一种开创性的方法，将基于Transformer的预测与加权约束动态时间规整（wcDTW）相结合，以优化场景选择。我们的方法利用Transformer的预测能力来预见能源价格，同时通过wcDTW确保多个不确定产品之间的一致性，从而筛选出相关的历史场景。通过在2023年7月PJM市场中的广泛模拟，我们的方法与传统方法相比，收入增加了10%，凸显了其在实时市场中革新电池竞价策略的潜力。