An interior point nonlinear programming for optimal power flow problems with a novel data structure
An interior point nonlinear programming for optimal power flow problems with a novel data structure
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DOI：
10.1109/pica.1997.599388
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发表时间：
1997-05
期刊：
影响因子：
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通讯作者：
Hua Wei;H. Sasaki;J. Kubokawa;R. Yokoyama
中科院分区：
文献类型：
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作者：
Hua Wei;H. Sasaki;J. Kubokawa;R. Yokoyama
本文提出了一种基于原始问题扰动 KKT 条件的最优潮流问题 (OPF) 的新内点非线性规划算法。通过中心方向的概念，我们将该算法扩展到经典功率流（PF）并近似 OPF 问题。对于后者，CPU 时间可以大大减少。为了有效地处理函数不等式约束，导出了一个简化的校正方程，其大小取决于等式约束的大小。提出了一种新颖的数据结构，该结构是通过重新排列校正方程来实现的。与传统的Newton OPF数据结构相比，对于大规模系统，该方案的填充次数大致减半，CPU时间减少约15%。所提出的算法包括四种目标函数和两种不同的数据结构。对规模从 14 到 1047 总线的测试系统进行的广泛数值模拟表明，所提出的方法由于其鲁棒性和快速执行时间而非常适合大规模应用。
This paper presents a new interior point nonlinear programming algorithm for optimal power flow problems (OPF) based on the perturbed KKT conditions of the primal problem. Through the concept of the centering direction, we extend this algorithm to classical power flow (PF) and approximate OPF problems. For the latter, CPU time can be reduced substantially. To efficiently handle functional inequality constraints, a reduced correction equation is derived, the size of which depends on that of equality constraints. A novel data structure is proposed which has been realized by rearranging the correction equation. Compared with the conventional data structure of Newton OPF, the number of fill-ins of the proposed scheme is roughly halved and CPU time is reduced by about 15% for large scale systems. The proposed algorithm includes four kinds of objective functions and two different data structures. Extensive numerical simulations on test systems that range in size from 14 to 1047 buses, have shown that the proposed method is very promising for large scale application due to its robustness and fast execution time.