For the demand on fast power flow calculation for large-scale power grid analysis and energy management systems

a sparse matrix factorization algorithm for graphic processing unit (GPU) based on stratified path trees is proposed. By referring to the algorithm

the Newton-Raphson method for solving the power-flow problem is implemented on GPU. To improve the efficiency

the calculation of the right-hand side of equation and the Jacobian matrix

LU decomposition and forward-back substitution are implemented on GPU

which reduces the data transfer time between GPU and central processing unit (CPU). Then

considering the multi-level memory architecture with registers

cache and global memory

a special data structure is designed to decrease the latency of data access. Moreover

considering the feature of threads arrangement on GPU

the task allocation is optimized

while the efficiency of parallelism is improved. Finally

compared with the power flow calculation module of power system analysis software package (PSASP) ran on CPU

numerical simulation test is conducted. The results show that the more the nodes

the more advantages GPU will get. In the test on a system with 43 602 nodes

the program is able to provide a 5.172 times speed-up ratio

which proves the effectiveness and practicality of the algorithm proposed.