Modeling Regulation of Economic Sustainability in Energy Systems with Diversified Resources

Abstract

The imperfection of theoretical and methodological approaches to regulate the jump process transition when combining differentiated energy resources is a pressing issue. The goal of this paper is to develop a theory and a method to regulate the integration-balancing processes of combining diversified resources. The concept of combining integration and balancing models has been substantiated by methods of transforming multidimensional space and approximating generalized functions that represent jump-like processes. Theoretical and operational-regulatory models of economic sustainability have been developed, substantiating new concepts, patterns, properties, dependencies and indicators of the dynamics of the processes of combination; the optimality conditions for the number of approximations of generalized functions, interpreting the effects of control functions of combining resources, are determined. New methods for solving problems have been developed: the organization of the energy technology complex of facilities for enhanced resource diversification and the Center for Sustainability, improving the quality of managing dynamic processes in terms of combining and diversifying resources.

Combined Approach to Analysis and Regulation of Thermodynamic Processes in the Energy Technology Complex

“…This makes it necessary to use piecewise linear functions that are widely used in electronics to represent signal transmission processes. These features determine the additional use of tools for deeper analysis and digital modeling of processes in data science [1]. It is based on providing realtime control and regulation using neural network algorithms for deep machine learning using artificial intelligence.…”

“…The problem is partially solved by poorly effective methods of traditional analysis based on big data, requiring data filtering. Therefore, task 2 is solved using the tools of deeper analysis and digital modeling in a comprehensive method for evaluating and modeling processes in data science [1,[5][6][7]. It is based on providing real-time management of complex systems using neural network algorithms for deep machine learning using artificial intelligence.…”