Jacob Roshni Anna, Paul Steve, Chowdhury Souma, Gel Yulia R, Zhang Jie
Department of Electrical and Computer Engineering, The University of Texas at Dallas, Richardson, TX, 75080, USA.
Department of Mechanical and Aerospace Engineering, University at Buffalo, Buffalo, NY, 14260, USA.
Nat Commun. 2024 Jun 4;15(1):4766. doi: 10.1038/s41467-024-49207-y.
Self-healing smart grids are characterized by fast-acting, intelligent control mechanisms that minimize power disruptions during outages. The corrective actions adopted during outages in power distribution networks include reconfiguration through switching control and emergency load shedding. The conventional decision-making models for outage mitigation are, however, not suitable for smart grids due to their slow response and computational inefficiency. Here, we present a graph reinforcement learning model for outage management in the distribution network to enhance its resilience. The distinctive characteristic of our approach is that it explicitly accounts for the underlying network topology and its variations with switching control, while also capturing the complex interdependencies between state variables (along nodes and edges) by modeling the task as a graph learning problem. Our model learns the optimal control policy for power restoration using a Capsule-based graph neural network. We validate our model on three test networks, namely the 13, 34, and 123-bus modified IEEE networks where it is shown to achieve near-optimal, real-time performance. The resilience improvement of our model in terms of loss of energy is 607.45 kWs and 596.52 kWs for 13 and 34 buses, respectively. Our model also demonstrates generalizability across a broad range of outage scenarios.
自愈智能电网的特点是具有快速响应、智能控制机制，可在停电期间将电力中断降至最低。配电网停电期间采取的纠正措施包括通过开关控制进行重构和紧急负荷削减。然而，传统的停电缓解决策模型由于响应速度慢和计算效率低，不适用于智能电网。在此，我们提出一种用于配电网停电管理的图强化学习模型，以增强其恢复能力。我们方法的独特之处在于，它明确考虑了基础网络拓扑及其随开关控制的变化，同时通过将任务建模为图学习问题来捕捉状态变量（沿节点和边）之间的复杂相互依存关系。我们的模型使用基于胶囊的图神经网络学习电力恢复的最优控制策略。我们在三个测试网络上验证了我们的模型，即13节点、34节点和123节点的修改后的IEEE网络，结果表明该模型能够实现接近最优的实时性能。对于13节点和34节点网络，我们的模型在能量损失方面的恢复能力提升分别为607.45千瓦秒和596.52千瓦秒。我们的模型还展示了在广泛的停电场景中的通用性。
