论文标题
AC最佳功率流量的学习增强学习的准Newton方法
A Learning-boosted Quasi-Newton Method for AC Optimal Power Flow
论文作者
论文摘要
电网运算符通常全天解决大规模的非convex最佳功率流（OPF）问题，以确定发电机的最佳设定点，同时遵守物理约束。尽管牛顿 - 拉夫森（Newton-Raphson）是许多OPF求解器的核心，但在数字上可能是缓慢而不稳定的。为了减少与计算完整的雅各布及其倒数相关的计算负担，许多准Newton方法试图通过利用近似雅各布矩阵来找到最佳条件的解决方案。在本文中，提出了一种基于机器学习的准Newton方法，该方法对候选最佳解决方案进行了迭代更新，而无需计算Jacobian或近似Jacobian矩阵。提出的基于学习的算法利用了带有反馈的深神网络。通过正确选择权重和激活功能，该模型成为收缩映射，可以保证收敛。最多1,354台总线的网络显示的结果表明，该方法能够非常快速地找到AC OPF的近似解决方案。
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.
