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EN
Electric energy systems need constant modernization and updating to solve such problems as distributed management, self-sealing, improving the quality of electricity, demand management, and integration of renewable energy systems. Currently, energy systems need advanced and intelligent technologies to perform various system-level tasks. The purpose of this research is to analyze the existing control systems of electric energy systems, as well as to consider the possibility of using multi-agent systems to control electric energy systems. To achieve the aim of the research, the following scientific approaches were implemented: method of direct research, experimental method, questioning, comparative method, analysis method, and method of observation. The primary value of the research is in the novelty of the work and the fact, that functional components in multi-agent systems act as independent agents, which can interact with each other through a message communication system. This provides a simple connection between the components, which can benefit complex systems designed for an intelligent network. The intelligent network provides an efficient energy management system, and the modernization of the existing power system using a multi-agent system provides solutions to many problems. The best implementation of a multi-agent system can be achieved through the employment of fast and protected communication protocols. The authors of the research have conducted research and presented key statistical data on electricity usage in Kazakhstan over the past few years. The practical significance of the research is determined by the applied results, and their scientific significance, which is conditioned upon the use of deep, modern mathematical results and the development of an optimal control system. This research is a part of a universal model and optimal system of emergency quick response, conducting a quick preliminary prognosis as well as ensuring more lasting planning in electricity consumption.
EN
The purpose of the article is to create a concise nonlinear mathematical model for analyzing the growth of fixed assets in a specific industry. The emergence of chaotic behaviour in economic systems was explored, focusing on fluctuations. The study employed methods such as systems analysis, correlation analysis, nonlinear dynamics, and differential equations. It was identified that sharp technological innovations as the primary drivers of short-term fluctuations impacting fixed asset development. The resulting nonlinear dynamic model allowed for flexible operation, transitioning between equilibrium, periodic, and chaotic states based on coefficient values for asset growth rates and time constants reflecting economic system dynamics.
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