Abstract
The development of the capabilities of computational tools has created up new possibilities for the effective use of a number of classical mathematical methods and algorithms for solving many important problems in the power engineering. In particular, a set of algorithms are developed to optimize the modes of electric power systems based on genetic algorithms. At the same time, the issues of taking into account functional constraints in solving such problems by genetic algorithms need to be improved. In accordance with it in this article the problems of taking into account of different constraints in optimization of modes of power systems using genetic algorithms are considered. The algorithm of optimization by genetic algorithm taking into account of functional constraints in forms of equality and inequality by penalty functions is proposed. The results of research of proposed algorithm’s efficiency in example of optimization of mode of power system with 8 buses, 4 thermal power plants and 3 transmission lines with controlled power flow are presented.
Highlights
IntroductionOptimization of modes of modern power systems on active power is a complex task of nonlinear mathematical programming with a set of simple and functional constraints in the forms of equality and inequality
The algorithm of optimization by genetic algorithm taking into account of functional constraints in forms of equality and inequality by penalty functions is proposed
Optimization of modes of modern power systems on active power is a complex task of nonlinear mathematical programming with a set of simple and functional constraints in the forms of equality and inequality
Summary
Optimization of modes of modern power systems on active power is a complex task of nonlinear mathematical programming with a set of simple and functional constraints in the forms of equality and inequality. The issues of taking into account functional constraints in solving of optimization problems using nontraditional algorithms, in particular, genetic algorithms [5] [6] [7] [8], require additional research. Genetic algorithms offer a new and powerful approach to solving optimization problems Their use was made possible by expanding the capabilities of computational tools at relatively low costs. These algorithms are used in solving global problems of search optimization, when traditional optimization algorithms cannot be used. The issues of taking into account of functional constraints in the form of equality and inequality in optimization of power system modes by these algorithms are researched not sufficiently
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