Analyzing a massive number of Power Flow (PF) equations even on almost identical or
similar network topology is a highly time-consuming process for large-scale power
systems. The major computation time is hoarded by the iterative linear solving process
to solve nonlinear equations until convergence is achieved. This is a paramount concern
for any PF analysis methods. This thesis presents a sparse matrix-based power flow
solver that is fast enough to be implemented in the real-time analysis of largescale power
systems. It uses KLU, a sparse matrix solver, for PF analysis. It also implements parallel
processing of CPU and GPU which enables the simultaneous computation of multiple
blocks in the algorithm leading to faster execution. It runs 1000 times and 200 times faster
than newton raphson method for DC and AC power system respectively. On average, it
is around 10 times faster than MATPOWER for both AC and DC power system
