Comparison of Genetic Algorithms and Particle Swarm Optimization for Optimal Power Flow Including FACTS devices

Abstract

This paper describes the performance of two population based search algorithms (Genetic Algorithms and Particle Swarm Optimization) when applied to Optimal Power Flow (OPF) including Static VAR Compensator (SVC) and Thyristor Controlled Series Compensator (TCSC) devices. The OPF optimizes a power system operating objective function, while satisfying a set of system operating constraints. The basic OPF solution is obtained with fuel cost minimization as the objective function and the optimal settings of the power system are determined. OPF can also be formulated for reactive power optimization, as minimization of system active power losses and improving the voltage stability in the system. In the present paper different objective functions that reflect Fuel cost minimization, System power loss minimization, Voltage Stability Enhancement (L-index minimization), Power loss minimization with SVC device and Power loss minimization with combined application of SVC and TCSC devices have been considered. To monitor and improve voltage stability in power system, minimization of sum of squared L-indices of all the load buses is considered as objective function in OPF. This index also guides the optimal location for VAR compensation. During normal operating conditions a planning engineer requires that all line flows and voltages are within limits while minimizing investment (including losses). While during outage conditions, line loading and voltages are again desired within limits while minimizing investment. It is important to obtain feasible solutions with in a minimal amount of engineering time.