The increasing complexity of power grid management, driven by the emergence of prosumers and the demand for cleaner energy solutions, has needed innovative approaches to ensure stability and efficiency. This paper presents a novel approach within the model-free framework of reinforcement learning, aimed at optimizing power network operations without prior expert knowledge. We introduce a masked topological action space, enabling agents to explore diverse strategies for cost reduction while maintaining reliable service using the state logic as a guide for choosing proper actions. Through extensive experimentation across 20 different scenarios in a simulated 5-substation environment, we demonstrate that our approach achieves a consistent reduction in power losses, while ensuring grid stability against potential blackouts. The results underscore the effectiveness of combining dynamic observation formalization with opponent-based training, showing a viable way for autonomous management solutions in modern energy systems or even for building a foundational model for this field.

翻译：随着产消者的涌现以及对更清洁能源解决方案的需求，电力网络管理的复杂性日益增加，这需要创新方法来确保稳定性和效率。本文提出了一种在无模型强化学习框架内的新方法，旨在无需先验专家知识的情况下优化电力网络运行。我们引入了一种掩码拓扑动作空间，使智能体能够探索多样化的成本降低策略，同时利用状态逻辑作为选择适当动作的指导来维持可靠服务。通过在模拟的5变电站环境中的20种不同场景下进行广泛实验，我们证明该方法能持续降低功率损耗，同时确保电网在面对潜在停电时的稳定性。结果突显了将动态观测形式化与基于对手的训练相结合的有效性，为现代能源系统的自主管理解决方案乃至构建该领域的基础模型展示了一条可行路径。