Contribution of energy management automation at compressor stations to the reduction of greenhouse gas and local pollutant emissions in gas transport regions

Abstract

The article assesses the contribution of energy management automation at compressor stations (CS) to the reduction of greenhouse gas and local pollutant emissions, as well as to the decrease in specific fuel gas consumption, considering the transition from dispatch control to integrated automated energy management systems as a decarbonization practice for gas transportation infrastructure. The empirical base includes ten years of operation data from twelve compressor stations in Western Siberia (five years before and five years after implementation), stratified by types of gas pumping units; data sources include SCADA/on-board system archives, commercial fuel accounting, and instrumental emissions monitoring. The methodology applies normalization by specific metrics (m³ of fuel per thousand kWh and g per 1000 m³ of transported gas), correlation-regression analysis, paired t-tests, and emission modeling: CO₂—stoichiometrically, NOx—based on combustion mode characteristics, CH₄—by startup/shutdown and venting profiles considering meteorological factors; uncertainty was estimated using the bootstrap approach. Implementation of automated energy management (load distribution optimization, combustion stabilization, reduction in startup/shutdown frequency, integration of variable-frequency drives and model predictive control) led to an average reduction in specific fuel consumption by 6.87%, accompanied by decreases in CO₂ emissions by 6.95%, NOx by 11.43%, and CH₄ by 24.84%; the number of transient operations and ventings dropped by more than 40%. Economically, this corresponds to an average annual saving of about 12.87 million m³ of fuel gas per CS, a net effect of approximately 63 million rubles per year, and an average payback period of 3.4 years, while simultaneously reducing specific emissions per unit of transported volume. The discussion identifies key effect mechanisms (algorithmic mode smoothing, prevention of temperature peaks, synchronization of units) and study limitations (fleet heterogeneity, incompleteness of continuous measurements, meteorological confounding), and confirms the robustness of results for mixed fleets. It is concluded that energy management automation is a highly cost-effective measure for decarbonization and local risk reduction, forming the basis for further integration of digital twins, predictive maintenance, and intelligent mode planning in gas transportation regions.