Supervised Learning for the Bidding of Grid-Connected Batteries in the Day-Ahead Market

This paper discusses the implementation of supervised learning (SL) as a straightforward data-driven technique to compute the day-ahead bids of grid-connected battery energy systems (BESS) participating in energy markets. The objective is to implicitly account for price uncertainty in the BESS schedule before assessing the economic performance. The case study is a 10 MW BESS battery participating in the day-ahead market. Physic-Informed and more traditional loss functions and a large set of tuning parameters are compared based on the generated daily revenues. Either the power injected by the BESS or its state of charge is controlled, illustrating a compromise to find between the accuracy and the resilience of the results, once confronted with the high volatility of energy prices. The performance of AI-based controllers is assessed in terms of precision with a theoretical optimum obtained with a "perfect forecast". A reference bidding strategy using "backcasting" as a forecast is also considered. Simulation over the year 2021 with an hourly training data set of the energy prices of 2020 shows that SL models do not necessarily perform better than reference results (with a minimal error of 58 %). However, discussions about their tuning and design choices shed light on the complex implementation process of the selected case study.