Accelerated and Robust Analytical Target Cascading for Distributed Optimal Power Flow
Accelerated and Robust Analytical Target Cascading for Distributed Optimal Power Flow
复制标题
DOI：
10.1109/tii.2020.2973213
复制
发表时间：
2020-12
影响因子：
12.3
通讯作者：
A. Mohammadi;A. Kargarian
中科院分区：
文献类型：
--
作者：
A. Mohammadi;A. Kargarian
基于增广拉格朗日的分布式算法，如分析目标级联（ATC），可能会收敛缓慢，在最优点周围振荡，或者如果惩罚乘数选择不当或目标项的重要性水平不平衡，则会发散。本文提出了一种求解分布式最优潮流的加速鲁棒ATC （AR-ATC）算法。设计一个函数来确定一个平衡系数，将其纳入ATC使目标函数的重要级别项之间达到平衡。如果惩罚乘数初始化为较大的值，建议的函数创建一个平衡系数以避免过早收敛或发散。如果惩罚乘数设置为较小的值，将创建一个平衡系数来减少迭代次数。通过数学论证和仿真研究，分析了AR-ATC的有效性。并对该方法在辅助问题原理、乘法器交替方向法和基于nesterov的乘法器交替方向法等方面的潜在应用进行了数值研究。对直流最优潮流和交流最优潮流问题进行了仿真。
Augmented Lagrangian-based distributed algorithms, such as analytical target cascading (ATC), may converge slowly, oscillate around the optimal point, or diverge if penalty multipliers are not selected appropriately or the level of importance of objective terms is not balanced. This article presents accelerated, robust ATC (AR-ATC) to solve the optimal power flow (OPF) distributedly. A function is designed to determine a balancing coefficient whose incorporation into ATC makes a balance between the important level of terms of objective functions. If penalty multipliers are initialized to large values, the proposed function creates a balancing coefficient to avoid the premature convergence or divergence. A balancing coefficient will be created to reduce the number of iterations if penalty multipliers are set to small values. Mathematical justifications and simulation studies are performed to analyze the effectiveness of AR-ATC. Potential applications of the proposed method on auxiliary problem principle, alternating direction method of multipliers (ADMM), and Nesterov-based ADMM are also studied numerically. Simulations are performed for direct current optimal power flow and alternating current optimal power flow problems.