Graph Computing based Parallel Power Flow Algorithm and Graph Visualization for Power Distribution Networks

With the emergence of the graph database and the graph computing technologies, the power system is facing a new era of technology development. Meanwhile, the fast-growing power distribution systems with increased size and complexity require more efficient data management systems and faster power flow solving algorithm. These challenges could be well solved by applying the graph data model (GDM) and graph computing technologies as the GDM provides more efficient data management methods and the graph computing is suitable for the node based parallel computing. This paper proposes a GDM based power distribution network modeling approach. Then a graph computing based parallel power flow algorithm and a graph visualization based power flow software have been developed based on it. The proposed parallel power flow algorithm is implemented on a graph database platform and the simulation results show that it is able to effectively reduce the computing time of the power flow with large test systems. Moreover, the power flow software is able to provide vivid data visualization and perform flexible data analysis and data management functions.