MODEL FOR QUANTITATIVE ASSESSMENT OF CHANGES AND BALANCING CONDITIONS IN AN ENTERPRISE’S ENERGY SUPPLY WHEN MULTIPLE SOURCES ARE AVAILABLE

Abstract

This article examines the problem of quantitatively assessing changes in an enterprise’s energy supply structure under conditions where multiple electricity sources are in operation, including centralized power supply, on-site generation, backup power plants, and energy storage systems. The relevance of the study is determined by the need to formalize the balance conditions of an enterprise’s power system under conditions of an uneven load schedule, external power supply constraints, generation variability, and the need to ensure uninterrupted power supply to critical consumers. A mathematical model of the enterprise’s energy balance is proposed, based on representing the total system capacity as the sum of the available capacities of all sources, taking into account the time dependence of their operation. The model accounts for the variable nature of electricity consumption, the power limitations of each source, the charging and discharging modes of storage devices, efficiency coefficients, transmission losses, and power reserves to compensate for emergency and peak conditions. The balance condition is formulated as a functional relationship between generation volume, storage, external energy inflow, and the enterprise’s total demand at each discrete point in time. Based on the model, quantitative indicators for assessing the system’s energy status have been determined, including the load coverage level, the magnitude of the power deficit or surplus, the share of each source in achieving balance, and the enterprise’s energy stability coefficient. It has been demonstrated that the use of energy storage systems and load distribution control algorithms makes it possible to reduce the magnitude of imbalance, improve the reliability of power supply, and ensure the system’s adaptability to changes in external and internal operating parameters. The feasibility of using SCADA systems, Smart Grid technologies, and optimization algorithms for implementing real-time dynamic control of energy flows has been substantiated. The results obtained can be used to develop decision support systems for the design and operation of multi-source power supply systems for enterprises.