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Machine learning-accelerated distributed optimisation methods for optimal power flow: A review
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DOI：10.1016/j.rser.2025.116190.png)
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Abstract
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• We review the intersection of distributed optimisation and machine learning for OPF. • Machine learning can accelerate convergence of distributed optimisation. • Distributed optimisation can ensure feasibility of machine learning output. • Large potential for innovation in this area for feasible, tractable OPF.
Keywords:
distributed optimisation
machine learning
optimal power flow
convergence acceleration
feasibility guarantees
Journal
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16.3
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1.6W
Citations:
18.5W
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ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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