Attention-Enhanced Causal Reinforcement Learning for Robust Renewable Energy System Scheduling

Renewable energy is gradually integrating into modern power systems, but its inherent volatility and uncertainty pose significant challenges to real-time scheduling and operational reliability. Traditional reinforcement learning (RL) methods are limited by their limited generalization ability and often cannot adapt to scenarios not encountered during training. To address these limitations, this paper proposes a new framework that combines causal reinforcement learning with an attention mechanism. In this method, the scheduling problem is formulated as a Markov decision process, and both basic rewards and causal rewards are incorporated to guide the learning process. The attention mechanism dynamically updates the weights of each operational target, enabling the agent to focus on key elements and effectively respond to changing environmental conditions. Comprehensive experiments were conducted using actual data from the UK power system in 2020, covering all seasonal variations and a wide range of operational scenarios. The proposed method was systematically evaluated under various conditions, including extreme and unseen cases. The results show that this framework significantly enhances the adaptability, robustness, and generalization performance of reinforcement learning algorithms. Compared to traditional methods, it achieves higher operational efficiency and renewable energy utilization. These findings highlight the potential of causal relationships and attention-based reinforcement learning in achieving intelligent, reliable, and scalable energy management in future power systems.