Feature-driven reinforcement learning for photovoltaic in continuous intraday trading
This provides a practical solution for PV producers to improve revenues and reduce imbalance costs in intraday trading, though it is incremental as it builds on existing RL methods.
The paper tackled the problem of uncertainty in photovoltaic generation and electricity prices for PV operators in continuous intraday markets by proposing a feature-driven reinforcement learning approach, which consistently outperformed benchmark baselines in out-of-sample evaluations.
Photovoltaic (PV) operators face substantial uncertainty in generation and short-term electricity prices. Continuous intraday markets enable producers to adjust their positions in real time, potentially improving revenues and reducing imbalance costs. We propose a feature-driven reinforcement learning (RL) approach for PV intraday trading that integrates data-driven features into the state and learns bidding policies in a sequential decision framework. The problem is cast as a Markov Decision Process with a reward that balances trading profit and imbalance penalties and is solved with Proximal Policy Optimization (PPO) using a predominantly linear, interpretable policy. Trained on historical market data and evaluated out-of-sample, the strategy consistently outperforms benchmark baselines across diverse scenarios. Extensive validation shows rapid convergence, real-time inference, and transparent decision rules. Learned weights highlight the central role of market microstructure and historical features. Taken together, these results indicate that feature-driven RL offers a practical, data-efficient, and operationally deployable pathway for active intraday participation by PV producers.