考虑运行状态信息的综合能源系统图强化学习优化调度

“双碳”背景下，异质能源的耦合加剧迫使综合能源系统(integrated energy system, IES)拓扑朝着更复杂、更灵活的方向不断演变。然而，现有优化调度方法对非欧网络拓扑知识及其异质潮流约束考虑不足。针对这一问题，提出一种基于图强化学习的综合能源系统优化调度方法。首先，基于图理论在保证节点多样状态的情况下，将异质能源网络拓扑转换为网络图模型。其次，通过建立基于真实图映射的状态-动作-奖励的框架，利用图强化学习的方法学习图模型的非欧拓扑信息，将异质潮流知识加入系统节点运行状态，从而实现IES的安全优化调度。最后，利用某工业园区的真实数据进行仿真验证，所提方法相对于传统方法有效缓解了节点电压越限的问题。结果表明，所提方法能够在考虑IES真实拓扑运行状态信息和异质潮流安全的情况下实现IES的优化调度。

Optimal dispatch of an integrated energy system based on graph reinforcement learning considering operation state information

With the dual-carbon goals, the intensified coupling of heterogeneous energy sources is driving integrated energy system (IES) topology to evolve towards greater complexity and flexibility. However, the existing optimal scheduling methods do not sufficiently consider the knowledge of non-European network topology and its heterogeneous power flow constraints. To address this issue, this paper proposes an optimal dispatch method based on graph reinforcement learning. First, by guaranteeing diverse node states, the heterogeneous energy network topology connections are converted into network graph models using graph theory. Second, a state-action-reward framework based on real graph mapping is established, and the graph reinforcement learning method is employed to learn the non-Euclidean knowledge and heterogeneous flow constraints brought by the graph model, thus achieving safe and optimal scheduling of IES. Finally, the real data of an industrial park is used for simulation verification. Compared with the traditional method, the proposed method effectively alleviates the node voltage over-limit problem. The results indicate that the proposed method can achieve optimal dispatch of IES while considering the actual topology operation state information and heterogeneous flow safety.