标题： 基于特征的强化学习在连续日内交易中的光伏应用 显示英文标题

标题： Feature-driven reinforcement learning for photovoltaic in continuous intraday trading

摘要： 光伏（PV）运营商在发电和短期电价方面面临重大不确定性。 连续日内市场使生产商能够实时调整其头寸，可能提高收益并减少不平衡成本。 我们提出了一种基于特征的强化学习（RL）方法，用于光伏日内交易，该方法将数据驱动的特征整合到状态中，并在顺序决策框架中学习投标策略。 该问题被建模为一个马尔可夫决策过程，其奖励平衡了交易利润和不平衡惩罚，并使用主要线性且可解释的策略通过近端策略优化（PPO）进行求解。 该策略基于历史市场数据进行训练，并在样本外进行评估，在各种场景下始终优于基准基线。 广泛的验证表明快速收敛、实时推理和透明的决策规则。 学习的权重突显了市场微观结构和历史特征的核心作用。 综合来看，这些结果表明，基于特征的强化学习为光伏生产商主动参与日内市场提供了一种实用、数据高效且可操作部署的路径。 显示英文摘要

摘要： 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.