Data-driven smart grid carbon emission control methods using graph-based power flow computing

What is it about?

To address the challenges of large-scale power grid complexity and the high resource demand for calculating electricity carbon factors, this paper proposes an efficient algorithm for modeling grid topology and implementing the "One Electricity Carbon Chart." By leveraging graph computing components, the method enables graph modeling, distributed storage, and high-performance calculation of the electric carbon factor using data from the grid's topology, such as power grid tables and AC line segments. The approach allows for rapid modeling of EMS dispatch data, facilitating intuitive visualization and dynamic study of grid structures and power flows. Additionally, the algorithm performs distributed parallel calculations of the electric carbon factor in ring networks, overcoming the challenges of analyzing large-scale grid topologies. Experiments on real datasets validate the method's effectiveness. Furthermore, the research redefines carbon emission calculations at the plant level based on grid power flows, offering a new perspective on carbon responsibility sharing within the power system.

Featured Image