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Scalable Learning for Optimal Load Shedding Under Power Grid Emergency Operations
[article]
2022
arXiv
pre-print
Effective and timely responses to unexpected contingencies are crucial for enhancing the resilience of power grids. Given the fast, complex process of cascading propagation, corrective actions such as optimal load shedding (OLS) are difficult to attain in large-scale networks due to the computation complexity and communication latency issues. This work puts forth an innovative learning-for-OLS approach by constructing the optimal decision rules of load shedding under a variety of potential
arXiv:2111.11980v2
fatcat:jbkyma7bibfklgyhudusjo4pya