Multi objective genetic algorithm for congestion management in deregulated power system

Deregulation aims at bringing innovative service practices, reduced cost of electricity, private participation in investments etc. and also brings new issues which are unheard of or not critical in the regulated environment of power system. Congestion management becomes the major issue for the Independent System Operator to handle as the transmission corridor will be exploited by both the buyers and sellers for their own interests. Generation from different power utility will be contracted to flow in the transmission network as per different buyers' requirements, which may create overloading of some of the transmission resources. Congestion thus created or created while planning for any contingency is mitigated by generation rescheduling or load curtailment or both. In this work congestion is mitigated by both generation rescheduling and selected load shedding. Minimization of fuel cost and minimization of congestion cost are taken as two objectives and solved using multi objective genetic algorithm (MOGA). Simulation results based on MOGA is presented for IEEE 30 bus system.