May 10, 2024, 4:41 a.m. | Yuqi Zhou, Hao Zhu

cs.LG updates on arXiv.org arxiv.org

arXiv:2405.05521v1 Announce Type: new
Abstract: 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 …

abstract accounting arxiv cs.lg diverse impacts improving machine machine learning network ols power prompt resilience scalable type

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