论文标题
使用机器学习初始化ACOPF的连续线性编程求解器
Initializing Successive Linear Programming Solver for ACOPF using Machine Learning
论文作者
论文摘要
连续的线性编程（SLP）方法是解决大规模非线性优化问题的有利方法之一。解决交替的当前最佳功率流（ACOPF）问题也不例外，尤其是考虑到全国各地的大型现实世界传输网络。但是，提高SLP算法的计算性能至关重要。实现此目标的一种方法是通过近乎最佳的解决方案对算法的有效初始化。本文研究了Scikit-Learn库中可用的各种机器学习（ML）算法，以初始化SLP-ACOPF求解器，包括检查线性和非线性ML算法。我们评估了每种机器学习算法的质量，以预测功率流解决方案所需的变量。然后将解决方案用作SLP-ACOPF算法的初始化。该方法已在一个充血且不受欢迎的3个公交系统上进行测试。将这项工作中最出色的ML算法获得的结果与用于初始化SLP-ACOPF求解器初始化的DCOPF解决方案的结果进行了比较。
A Successive linear programming (SLP) approach is one of the favorable approaches for solving large scale nonlinear optimization problems. Solving an alternating current optimal power flow (ACOPF) problem is no exception, particularly considering the large real-world transmission networks across the country. It is, however, essential to improve the computational performance of the SLP algorithm. One way to achieve this goal is through the efficient initialization of the algorithm with a near-optimal solution. This paper examines various machine learning (ML) algorithms available in the Scikit-Learn library to initialize an SLP-ACOPF solver, including examining linear and nonlinear ML algorithms. We evaluate the quality of each of these machine learning algorithms for predicting variables needed for a power flow solution. The solution is then used as an initialization for an SLP-ACOPF algorithm. The approach is tested on a congested and non-congested 3 bus systems. The results obtained from the best-performed ML algorithm in this work are compared with the results of a DCOPF solution for the initialization of an SLP-ACOPF solver.
