Optimization of Electric Power Systems States by Heuristic Methods

Abstract

Relevance: today, the growing demand for electricity, a sharp reduction in natural resources, and an increasingly acute nature of environmental problems require the widespread introduction of renewable energy installations into power systems, planning and managing their operation based on the conditions of ensuring maximum efficiency by all criteria. Until now, among the traditional methods for optimizing the operating conditions of many electric power systems (EPS), the gradient and relative equality of gain methods have been widely used. At the same time, studies are currently underway on the use of a number of heuristic algorithms in solving complex optimization problems in certain areas of technology. In such methods, in contrast to traditional methods, the absence of restrictive requirements such as the continuity of the objective function and functional boundary conditions, as well as the presence of a single extremum in the problem, provides opportunities for additional efficiency due to increased optimization accuracy. Therefore, it is important to study and explore the possibilities of their use for optimizing the state of power systems. The article presents the results of a study of the possibilities of using heuristic methods, such as Particle Swarm Optimization (PSO) algorithms, in solving problems of optimizing the states of power systems and shows that they can be effectively used in optimizing the states of modern complex power systems.  

Goal: to study the efficiency of PSO (particle swarm optimization) algorithms in optimizing the states of power systems, to identify their advantages and to evaluate the possibilities of practical application.

Methods: the study uses the traditional relative growth equality method and heuristic methods, such as PSO (particle swarm optimization) algorithms. Using the MATLAB software package, the results were compared and analyzed for accuracy and efficiency.

Results: the considered heuristic optimization methods can be effectively used to solve problems of planning and managing the states of complex power systems.