Mathematical Foundations for Modeling a Zero-Carbon Electric Power System in Terms of Sustainability

Abstract

This article substantiates the relevance of mathematical methods and models for studying the management of the factorial parameters of regulating the decarbonization of regions of the Russian Federation. We present methods for the mathematical modeling of greenhouse gas emissions and for approximating functions for the study of processes in the thermal power industry and the economy. New models and methods are shown to increase the efficiency of designing electric power systems (EPSs). We establish that diverse companies must interact with institutions of education and science to achieve the main results of the study. This is achieved, firstly, by creating an EPS with a target of a zero-carbon footprint. Mathematical models of greenhouse gas emissions can be used to support this goal. We developed ways to account for carbon oxides and water streams. Stable interactions between systems in the innovation cycles of enterprises are ensured by methods combining a number of properties of the regulation of decarbonization. We describe methods to mathematically model greenhouse gas emissions and to approximate functions in the study of processes in the thermal power industry and economics. New research methods and techniques are proved to increase the efficiency of designing an EPS and can be used to reduce emissions. Digital twins should be modeled according to assessments on ensuring the stability of the balance area, with the goals of developing the EPS. Secondly, we substantiate methods for displaying singular processes of improving the balance of enterprise goals while coordinating the impact on the efficiency of standard and additional management functions. We additionally developed quality parameters for the use of additional functions in the foresight control of decarbonization goals. Thirdly, factorial parameters of additional control and regulation functions are implemented via a special system of technical accounting. This formed a big data database of new environmental quality and quality management indicators in the regulatory structure of industrial enterprises in the EPS. Additional functions of integration, combination, and acceleration of the impact of industrial enterprise quality indicators are organized on a digital platform to predict and plan indicators of integration and combination of these resources using neural networks.

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