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.

通过离线训练神经网络模型，我们提出了一种分散设计，用于在发生意外情况时自动构建最优负荷放电解决方案，以提高电力系统的韧性和应急响应能力。在 IEEE 118 节点系统和合成的德克萨斯 2000 节点系统上的数值研究表明，我们的可扩展负荷放电解决方案设计在及时进行电力系统应急操作方面具有高效和有效性。