Study of the Efficiency of Optimizing the States of Complex Power Systems Using Artificial Intelligence Methods - Tulkin Sh. Gayibov; Alisher E. Shanazarov; Afzalbek T. Bozorov (PROBLEMS OF ENERGY AND SOURCES SAVING, 2026)
Relevance. at present, a steady increase in global demand for electrical energy, the limited availability of conventional resources, and the necessity to ensure environmental safety and sustainability require more efficient use of resources in the energy production process. Taking these considerations into account, this paper presents the results of a study on the applicability and comparative performance of five artificial intelligence–based methods and algorithms for power system state optimization, namely the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Artificial Bee Colony (ABC), and Ant Colony Optimization (ACO). This approach is significant not only from a theoretical perspective but also in terms of practical implementation and enhancing the stability of power systems. Aim: the objective of this study is to improve models and algorithms for short-term planning and operational control of modern complex power systems based on an analysis of the effectiveness of artificial intelligence methods in optimizing their operating states. Methods: the research employed modern theoretical approaches to the calculation and optimization of steady-state operating conditions of electric power systems, as well as nonlinear mathematical programming, system analysis, and artificial intelligence methods. Results: in general, the problem of optimizing power system operating states represents a complex multi-extremal problem involving a large number of equality and inequality constraints of simple, functional, and integral types. In certain cases, the use of traditional solution methods leads to the necessity of approximation, which results in reduced accuracy and difficulties associated with the unreliability of iterative computational processes. To overcome these difficulties, the application of artificial intelligence methods is considered essential. A comparative analysis of their performance was conducted using a representative case study. The results demonstrate that the Particle Swarm Optimization (PSO), Differential Evolution (DE), Artificial Bee Colony (ABC), and Ant Colony Optimization (ACO) algorithms exhibit approximately similar performance and show higher efficiency compared to the other considered methods.