Optimizing performance attributes of electric power systems using chaotic salp swarm optimizer

This paper investigates the performance of a chaotic salp swarm optimization (CSSO) algorithm for solving optimal power flow (OPF) problems. The proposed CSSO-based method is applied on five different types of objective functions (OFs) which include generation costs minimization, environmental pollution/emission reduction, minimizing the transmission active power losses, enhancing the voltage profile, and upgrading system stability. Single and multi-objective frameworks are considered to attain various operational, economic, environmental and technical benefits. Initially, single OFs are used to formulate the optimization problem, and at later stage, simultaneous multiple objectives are optimized subject to a set of equality and inequality constraints. To prove the viability of the proposed CSSO-based OPF, standard IEEE 30-bus and 57-bus test systems via 16 studied cases are investigated. In addition, the subsequent cropped results are compared with other competing recent optimization methods in the literature. It can be reported that the cropped best fuel costs when they are optimized using the CSSO are 798.93 $/h and 41,666.66 $/h for the IEEE 30-bus and 57-bus test cases, respectively. The numerical results and performance tests along with comprehensive comparisons clearly indicate the superiority of the CSSO in achieving the given objectives.