Genetic Algorithm Based Fuzzy Multi-Objective Approach to FACTS Devices Allocation in FARS Regional Electric Network


In this investigation, a novel approach is presented to find the optimum locations and capacity of Flexible AC Transmission Systems (FACTS) devices in a power system using a fuzzy multi-objective function. Maximising the fuzzy satisfaction allows the optimization algorithm to simultaneously consider the multiple objectives of the network to obtain active power loss reduction; i.e., new FACTS devices cost reduction, robustifying the security margin against voltage collapse, network loadability enhancement and a voltage deviation reduction of the power system. A Genetic Algorithm (GA) optimization technique is then implemented to solve the fuzzy multi-objective problem. Operational and control constraints, as well as load constraints, are considered for optimum device allocation. Also, an estimated annual load profile has been utilized in a Sequential Quadratic Programming (SQP) optimization sub-problem to find the optimum location and capacity of FACTS devices, accurately. A Thyristor Controlled Series Compensator (TCSC) and a Static Var Compensator (SVC) are utilized as series and shunt FACTS devices in this study. The Fars regional electric network is selected as a practical system to validate the performance and effectiveness of the proposed method.