VAr planning using genetic algorithm and linear programming
VAr planning using genetic algorithm and linear programming
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DOI：
10.1049/ip-gtd:20010214
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发表时间：
2001-05
期刊：
影响因子：
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通讯作者：
J. Mantovani;S.A.G. Modesto;Ariovaldo V. Garcia
中科院分区：
文献类型：
--
作者：
J. Mantovani;S.A.G. Modesto;Ariovaldo V. Garcia
由连续的线性编程（SLP）和简单的遗传算法（SGA）组成的组合方法解决了反应性计划问题。这个问题分为运营和计划子问题；操作子问题是一个非线性，条件不良和非凸问题，包括确定电压控制和反应源的调整。计划子问题包括考虑系统的运行，经济和物理特征，获得最佳的反应源扩展。 SLP解决了与实际变量有关的最佳反应调度问题，而SGA用于确定建模问题中存在的二进制和离散变量的必要调整。一旦定义了一组候选母线，实施的程序将提供所需的反应源的位置和大小，以维护操作和安全性约束。
A combined methodology consisting of successive linear programming (SLP) and a simple genetic algorithm (SGA) solves the reactive planning problem. The problem is divided into operating and planning subproblems; the operating subproblem, which is a nonlinear, ill-conditioned and nonconvex problem, consists of determining the voltage control and the adjustment of reactive sources. The planning subproblem consists of obtaining the optimal reactive source expansion considering operational, economical and physical characteristics of the system. SLP solves the optimal reactive dispatch problem related to real variables, while SGA is used to determine the necessary adjustments of both the binary and discrete variables existing in the modelling problem. Once the set of candidate busbars has been defined, the program implemented gives the location and size of the reactive sources needed, if any, to maintain the operating and security constraints.