Security Constrained Optimal Power Flow using Meta-Heuristic Algorithms

The main goal of this paper is to address the Security Constrained Optimal Power Flow (SCOPF) issue by employing a basic binary Genetic Algorithm (GA). Initially, the Optimal Power Flow (OPF) problem is formulated for normal operating conditions without any contingencies, such as line or generator failures and the optimal generation schedules are obtained using the binary GA and Particle Swarm Optimization (PSO) algorithms. Subsequently, generator outputs are adjusted following a line outage after identifying the worst outage by ranking. The primary objective is to minimize fuel costs while adhering to various constraints related to generator power outputs, bus voltages, shunt capacitors/ reactors, and transformer tap-settings. The binary GA is applied and optimal schedules of generation are found when the system is subjected to line outage i.e. SCOPF is evaluated on the IEEE 30-bus system and its performance is compared with PSO method. It is also evident from the results that increasing the number of bits in the binary string can lead to improved optimal solutions at the cost of computation.