Prompt and effective corrective actions in response to unexpected contingencies are crucial for improving power system resilience and preventing cascading blackouts. The optimal load shedding (OLS) accounting for network limits has the potential to address the diverse system-wide impacts of contingency scenarios as compared to traditional local schemes. However, due to the fast cascading propagation of initial contingencies, real-time OLS solutions are challenging to attain in large systems with high computation and communication needs. In this paper, we propose a decentralized design that leverages offline training of a neural network (NN) model for individual load centers to autonomously construct the OLS solutions from locally available measurements. Our learning-for-OLS approach can greatly reduce the computation and communication needs during online emergency responses, thus preventing the cascading propagation of contingencies for enhanced power grid resilience. Numerical studies on both the IEEE 118-bus system and a synthetic Texas 2000-bus system have demonstrated the efficiency and effectiveness of our scalable OLS learning design for timely power system emergency operations.

翻译：针对突发事故的快速有效纠正措施对于提升电力系统韧性、防止级联停电至关重要。相较于传统局部方案，考虑网络约束的最优减载（OLS）有潜力应对事故场景对系统造成的多样化影响。然而，由于初始事故的快速级联传播，在计算与通信需求较高的大型系统中，实时求解最优减载极具挑战性。本文提出一种去中心化设计方案，通过利用神经网络（NN）模型对各个负荷中心进行离线训练，使其能够基于本地可用测量数据自主构建最优减载方案。所提出的学习驱动型最优减载方法可大幅降低在线应急响应过程中的计算与通信需求，从而通过遏制事故的级联传播来增强电网韧性。在IEEE 118节点系统及德克萨斯州2000节点合成系统上的数值研究验证了该可扩展最优减载学习设计在电力系统紧急运行中的高效性与有效性。