Investigating empirical bidding curves in the electricity spot market: Expected patterns vs anomalies?

In recent years, the dynamics of spot market bidding have evolved dramatically, driven by the increasing penetration of variable renewable energy sources, new market actors, and increased cross-border trading. Understanding and monitoring the market are crucial to ensure efficient operation, but traditional methods have not kept pace with the increasing complexity. This study introduces a machine learning framework to investigate empirical bidding patterns. The clustering of bidding curves is combined with an extensive explanatory variable dataset to define what bidding can be expected under different market conditions. Then, deviations from the expected patterns are investigated by leveraging a combination of hierarchical clustering, multinomial logit modeling, and random forest classification. As such, we identify outliers in the bidding data and detect instances where the observed patterns diverge from those predicted from the explanatory variables.