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. The proposed
learning-based algorithm utilizes a deep neural network with feedback. With
proper choice of weights and activation functions, the model becomes a
contraction mapping and convergence can be guaranteed. Results shown for
networks up to 1,354 buses indicate the proposed method is capable of finding
approximate solutions to AC OPF very quickly.

本文提出了一种基于机器学习的拟牛顿方法，使用深度神经网络进行迭代更新，能够快速找到交流 OPF 近似解，并消除了计算雅各比矩阵或近似雅各比矩阵的必要性。