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。

基于机器学习的电力系统事态下可扩展和最优负荷摘牌

Machine Learning for Scalable and Optimal Load Shedding Under Power  System Contingency

A simple yet successful approach to parallel satisfiability (SAT) solving is
to run several different (a portfolio of) SAT solvers on the input problem at
the same time until one solver finds a solution. The SAT solvers in the
portfolio can be instances of a single solver with different configuration
settings. Additionally the solvers can exchange information usually in the form
of clauses. In this paper we investigate whether this approach is applicable in
the case of massively parallel SAT solving. Our solver is intended to run on
clusters with thousands of processors, hence the name HordeSat. HordeSat is a
fully distributed portfolio-based SAT solver with a modular design that allows
it to use any SAT solver that implements a given interface. HordeSat has a
decentralized design and features hierarchical parallelism with interleaved
communication and search. We experimentally evaluated it using all the
benchmark problems from the application tracks of the 2011 and 2014
International SAT Competitions. The experiments demonstrate that HordeSat is
scalable up to hundreds or even thousands of processors achieving significant
speedups especially for hard instances.

本研究探讨了在大规模并行 SAT 问题求解中应用基于投资组合的分布式 SAT 求解方法，并实现了一个名为 HordeSat 的可扩展的并行 SAT 求解器，其使用分层并行和分布式设计，实现了数百到数千个处理器的可观加速效果。