Parallel computing for reducing time in security constrained optimal power flow analysis

This paper presents a novel approach for solving the security-constrained optimal power flow (SCOPF) optimization problem using parallel Computing. In this approach, switched shunt banks, generation power ramp, and demand response are considered in the SCOPF by maximizing the market surplus during regular operation and for a set of contingencies of branches and generators. The optimization problem is solved using the Nonlinear Interior Point Method. The contingency assessment is paralleled in multiple CPU cores to decrease the computation time. Additionally, the test systems used in ARPA-GO competition were used and compared with the ARPA benchmark results to assess the proposed algorithm. The numerical results show this method is suitable for fast SCOPF using paralleling Computing.