Novel Genetic Algorithm Based Solutions for Optimal Power Flow under Contingency Conditions

Power system throughout the world is undergoing tremendous changes and developments due to rapid Restructuring, Deregulation and Open-access policies. Greater liberalization, larger market and increasing dependency on the electricity lead to the system operators to work on limited spinning reserve and to operate on vicinities to maximize the economy compromising on the reliability and security of the system for greater profits, which lead to establishment of a monitoring authority and accurate electronic system to prevent any untoward incidents like Blackouts. In any power system, unexpected outages of lines or transformers occur due to faults or other disturbances. These events may cause significant overloading of transmission lines or transformers, which in turn may lead to a viability crisis of the power system. The main role of power system control is to maintain a secure system state, i.e., to prevent the power system, moving from secure state into emergency state over the widest range of operating conditions. Security Constrained Optimal Power Flow (SCOPF) is major tool used to improve the security of the system. In this work, Genetic algorithm has been used to solve the OPF and SCOPF problems. As initial effort conventional GA (binary coded) based OPF and SCOPF is going to be attempted. The difficulties of binary coded GA in handling continuous search space lead to the evolution of real coded GA‟s. Solutions obtained using both the algorithms are compared. Case studies are made on the IEEE30 bus test system to demonstrate the ability of real coded GA in solving the OPF and SCOPF problems. keywordsOptimal Power Flow(OPF), Security Constrained Optimal Power Flow(SCOPF), Genetic Algorithm(GA).