Rank analysis and system modeling for optimization of electricity consumption and sustainable development of energy facilities in mountain areas

Introduction. Rising electricity costs and the complexity of power supply in mountainous areas require new forecasting and optimization methods. A technocenological approach based on rank distributions allows us to consider technical facilities as a holistic system. The purpose of the research: To develop and test a methodology for rank analysis of electricity consumption in a power plant (hydrogen production) to improve energy efficiency and sustainable development in mountainous areas. Objects and methods. The facility is a power plant with FV-500M electrolyzers, calcining furnaces, gas blowers, and compressors. Rank statistical analysis, hyperbolic H-distributions, exponential-cosine approximation, confidence intervals, and regression models were used. Results and discussions. It was found that the share of electrolyzers in consumption reaches 92%. The forecast model showed an error of 20.1% (R2 = 0.67), indicating the need to take into account mountain climate and technological factors. Rank surfaces allowed us to identify anomalous years of consumption. Conclusion. Rank analysis is effective for structural diagnostics of technocenoses, but requires further refinement of the forecast models. Prospects for further research. Integration of machine learning methods, creation of digital twins, and adaptive control algorithms for mountain energy systems

Electricity · Electricity generation · Energy consumption · Energy modeling · Energy planning · Mains electricity · Sustainable development · Advanced Power Generation Technologies · Hybrid Renewable Energy Systems · Industrial Engineering and Technologies