Multi-objective congestion management using hybrid differential evolution in a deregulated power system

This paper presents a new hybrid differential evolution algorithm to address the problems of congestion management in a deregulated energy market. With the introduction of deregulation in the energy market, more and more generating utilities (GENCOs) are participating in power transactions in order to satisfy the increased demand. Due to the introduction of competition among the market players, the power systems are put under heavy stress leading to a cascading effect of line overload, interruption of power flow and finally system collapse. The whole situation is addressed as power system congestion. The problem of congestion management is formulated as a multi-objective one considering cost of reallocation of generators, active power loss, overloading of line and generator sensitivity. Several methods including differential evolution technique have been applied to solve this problem. But sometimes due to wrong parameter selection differential evolution may lead to premature convergence. To avoid premature convergence, a new hybrid differential evolution algorithm using chaotic sequence is proposed in this paper. The proposed technique is applied on IEEE 30 bus test system and it is found that the proposed method is capable of producing better results.