A Data-Driven Method for Deriving the Dynamic Characteristics of Marginal Carbon Emissions for Power Systems

Understanding the dynamic carbon emission status is vital for turning a power system into a low-carbon system. However, the existing research has normally considered the average carbon emissions as the indicator for the operation and planning of power systems. Detailed carbon emission responsibility is not well allocated to different demands within power systems, leading to inefficient emission control. To address this problem, this paper develops a data-driven method for accurately finding the characteristics of the nodal marginal emission factor without the requirement of real-time optimal power flow (OPF) simulation. First, the nodal marginal emission factor system is derived based on actual data covering a timespan of one year on top of the IEEE 118 system. Then, a Graphical Neural Network (GNN) is adopted to map both the spatial and temporal relationship between nodal marginal emission and other features, thereby identifying the marginal emission characteristics for different nodes of power transmission systems. Through case studies, fine-tuned GNNs estimate all nodal marginal emission factor (NMEF) values for power systems without the requirement of OPF simulation and achieve a 5.75% Normalized Root Mean Squared Error (nRMSE) and 2.52% Normalized Mean Absolute Error (nMAE). Last but not least, this paper brings a new finding: a strong inclination to reduce marginal emission rates would compromise economic operation for power systems.