Abstract
The paper is devoted to the statistical analysis of abruptly variable loads in industrial power supply systems in order to model an energy node with heterogeneous generation and determine the optimal plan for the development of its own electric energy sources. This load type, which is typical for energy-intensive industries, is determined by the specifics of technological processes. The nature of this load type imposes limitations when calculating the modes of power supply systems. The study is based on statistical processing of operational data obtained from various main step-down substation connections of a simulated industrial facility over a two-month period. According to the results of the study, it was revealed that traditional forecasting methods do not accurately describe non-stationary processes. Linear regression for predicting power showed low accuracy, which precluded its applicability in building a load model. Statistical verification of the hypothesis of empirical data correspondence to the Weibull–Gnedenko distribution using the Kolmogorov–Smirnov criterion showed its inapplicability. The analysis of statistical parameters showed that traditional forecasting methods do not provide an adequate description of the dynamic processes characteristic of this load type and confirmed the need to switch to private forecasting methods. To solve the tasks set, it is advisable to set the load model in the form of an equation system for active and reactive power with a user-defined step. This form of representation will make it possible to assess the stability of generators, determine their appropriate number, capacity and installation location in industrial power plants with a power shortage. The resulting load model is necessary for the prospective development of an engineering analysis and design system for industrial power supply systems with heterogeneous generation: in-plant power stations, small-scale generation and external electricity sources.
Keywords
electric power, power supply, small generation, variable load, power quality, industrial power supply systems, statistical analysis, distribution laws, mathematical model of load, main step-down substation
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