An improved genetic algorithm approach for security constrained optimal power flow with corrective action

Abstract

Security constrained optimal power flow (SCOPF) is a special type of optimal power flow (OPF) programme in which the optimum value of the objective function is computed while satisfying the constraints, both under normal and contingency conditions. This paper presents an improved genetic algorithm (IGA) approach to solve the corrective SCOPF problem which takes into account the system corrective capabilities in the post contingency state. The resulting schedule will have the same security level as the SCOPF, but with lower operating costs. The IGA uses strings of integers and floating point numbers to represent candidate solutions instead of binary strings. Crossover and mutation operators which can directly deal with the floating point number are used for a more effective genetic operation. Computer simulation is carried out on IEEE 30-bus and IEEE 118-bus systems using the proposed approach and the results are compared with the other approaches. Simulation results show the superiority of the proposed approach over the other approaches in solving the SCOPF problem.