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2020

A Learning-boosted Quasi-Newton Method for AC Optimal Power Flow

Baker, Kyri

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Power grid operators typically solve large-scale, nonconvex optimal power flow (OPF) problems throughout the day to determine optimal setpoints for generators while adhering to physical constraints.

Despite being at the heart of many OPF solvers, Newton-Raphson can be slow and numerically unstable.
To reduce the computational burden associated with calculating the full Jacobian and its inverse, many Quasi-Newton methods attempt to find a solution to the optimality conditions by leveraging an approximate Jacobian matrix.
In this paper, a Quasi-Newton method based on machine learning is presented which performs iterative updates for candidate optimal solutions without having to calculate a Jacobian or approximate Jacobian matrix.

A Learning-boosted Quasi-Newton Method for AC Optimal Power Flow · Around

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F. Hasan, A. Kargarian, and J. Mohammadi, “Hybrid learning aided inactive constraints filtering algorithm to enhance AC OPF solution time,” Jun. 2020, [Online] Available at: https://arxiv.org/abs/2006.01336
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K. Li and J. Malik, “Learning to optimize,” arXiv preprint arXiv:1606.01885 , Jun. 2016
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A. Zamzam and K. Baker, “Learning optimal solutions for extremely fast AC optimal power flow,” September 2019, [Online] Available at: https://arxiv.org/abs/2861719
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K. Baker and A. Bernstein, “Joint chance constraints in AC optimal power flow: Improving bounds through learning,” IEEE Transactions on Smart Grid , vol. 10, no. 6, pp. 6376 – 6385, Mar. 2019
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F. Diehl, “Warm-starting AC optimal power flow with graph neural networks,” in 33rd Conference on Neural Information Processing Systems (NeurIPS 2019) , Dec. 2019

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