Optimization of Real-Time Carbon Emission Monitoring on the Power Demand Side Based on System Dynamics

Against the backdrop of China’s “dual carbon” strategic goals, the precise monitoring of carbon emissions on the grid’s demand side is crucial for advancing the transformation of the energy structure and achieving low-carbon development. Current mainstream hourly-level monitoring methods have insufficient temporal resolution, coarse information granularity, and weak localization data support. These issues compromise the effectiveness of carbon reduction efforts and may even lead to secondary risks and economic losses. As a major province with high energy-consuming industries, Henan Province experiences frequent short-term high-energy consumption events in its heavy industry sector, where carbon emission characteristics are complex, making traditional monitoring methods inadequate. To address these challenges, this study focuses on thedemand side of the power grid in Henan’s heavy industry. It specifically analyzes the carbon emission characteristics of high-energy-consuming equipment and industrial electricity usage patterns during short-term high-energy events. Innovatively, a system dynamics model integrating STL, LightGBM, and a stream computing subsystem is constructed. This model not only achieves a technical upgrade from hourly to minute-level carbon emission monitoring but also fully considers the internal structures and coupling relationships among subsystems. Empirical research demonstrates that the model possesses the capability for minute-level data collection and dynamic updates of carbon emission factors, significantly improving the temporal resolution and response efficiency of the monitoring system. It enables high-frequency dynamic tracking of instantaneous load during industrial electricity consumption. These results provide robust data support for grid dispatch optimization and carbon market trading. The model enhances the accuracy of carbon emission predictions and offers reliable foundations and new optimization directions for exploring technological pathways toward energy conservation and emission reduction. Its application holds significant theoretical value and practical importance for promoting the modernization of regional energy governance, optimizing industrial structure, and formulating scientifically sound policies.