Improved multi objective differential evolution algorithm for congestion management in restructured power systems

In Restructured Power Systems, Transmission Congestion Management (CM) is the critical and the most notable issue to maintain the system in secure state. The congestion in transmission line can be alleviated by rescheduling the active power generation. The Congestion Management problem is a Multi-objective optimization problem, in which the two conflicting objectives, namely, minimization of rescheduling cost of generators and minimization of active power production cost are considered here. Meanwhile it has to ensure the system security, voltage profile, minimize the transmission losses and so on. Multi-objective evolutionary computation techniques have been extensively used for solving such conflicting objectives. In this paper, an improved strategy of multi-objective differential evolution (MODE) with a Double Best Mutation Operator (DBMO) is presented. Dynamic Crowding Distance (DCD) is also incorporated with the existing MODE algorithm in order to maintain the diversity among the non-dominated solutions. The proposed strategies along with other well-known strategies of MODE were compared with the performance metrics. The suitability of the proposed method in solving CM problem is well demonstrated in the results and discussions.