Carbon flow tracking methods for power systems in energy conservation and emission reduction environments

A carbon flow tracking method for the power system in an energy-saving and emission reducing environment is studied in order to accurately track the carbon flow of the power system and reduce the carbon footprint error rate. Firstly, carbon emission data is collected and features using Pearson correlation coefficients are extracted. Then, a carbon emission factor prediction model is established through neural networks, and the MDI method is used to calculate the carbon emission intensity of the power system. Finally, a DC power flow model is introduced with carbon emission intensity as input to achieve carbon flow tracking. The experimental results show that the carbon footprint error rate of the method proposed in this paper is 5.2%, the cost-effectiveness ratio of emission reduction is 80 yuan/ton of CO2, and it has strong anti-interference ability against data noise, demonstrating good carbon flow tracking performance.