Study of the National Power System in the Context of Intelligent Systems Under Conditions of Increasing Renewable Energy Production and Electricity Savings

In power engineering, various mathematical models are used, for example, to study stability, forecasting, etc., obtained using analytical methods, machine learning, and artificial intelligence. The present authors pursue a novel direction in modeling the development of the power system as an intelligent control system using data from 1990–2024 under conditions including a growing level of renewable energy production and an increased level of electrical energy saving. As a result of the modeling carried out in the MATLAB and Simulink environment, two types of highly accurate development models were obtained: a regression machine learning ARX model and a multilayer perceptron (MLP) neural network. For the neural model, MAPE errors ranged from 0.73% to 3.37%, and the coefficient of determination R2 ranged from 0.9478 to 0.9868. The accuracy of the ARX models was close to 100%. Using an ARX model converted into a state-space (SS) model, it was observed that the subsystems of conventional electricity production and renewable energy were observable and controllable. The presented methodology is modern, enabling the study of large development systems using development models in terms of control and systems theory and artificial intelligence methods.