Differential Evolution based Multi-objective Optimization of a Deregulated Power Network under Contingent State

This paper proposes a methodology to limit the variations in generation cost in a power system under normal and contingent state using Differential Evolution (DE) optimization technique keeping consumer welfare in view. The aim of this proposed methodology is to minimize the deviations of generation cost, during contingency, from a preferred value by rescheduling of the generation with a controlled load curtailment technique and hence relieving the lines from overloading for congestion management. A comparative study between rescheduling with and without load curtailment has also been presented in this paper. Numerical results on test system, namely IEEE 30 Bus System, are presented for illustration purpose and the same has been verified by a well esteemed optimization technique, Particle Swarm Optimization (PSO). The comprehensive simulation results establish that the developed method not only reduces the economic variations of the market but also ensures the voltage stability of the system during contingency.