Learning to Bid and Schedule in One-Price Power Markets

Farzaneh Pourahmadi – Technical University of Denmark

We propose a learning-based strategy for bidding and scheduling in day-ahead electricity markets with one-price imbalance settlement, focusing on hybrid renewable systems combining wind generation and hydrogen production. In such markets, conventional approaches often result in high-risk, all-or-nothing bidding behavior due to the uncertainty in imbalance prices—a phenomenon we term “betting.” To address this, we develop a data-driven framework that learns linear decision policies from contextual features to jointly optimize electricity bidding and hydrogen scheduling. By incorporating explicit risk constraints, our method transitions from speculative to diversified trading behavior. We evaluate the strategy under different regulatory scenarios governing grid power usage and hydrogen certification. This work highlights the potential of learning to exploit arbitrage opportunities and manage risk in hybrid renewable operations under one-price market mechanisms.