A powerful metaheuristic algorithm to solve static optimal power flow problems: Symbiotic organisms search

Abstract

This piece of work deals with implementing a new meta-heuristic algorithm symbiotic organisms search to address multi-objective optimal power flow (OPF) problems in power systems considering several operational constraints. The algorithm has been implemented on IEEE 30 and IEEE 118 bus test systems for various single objective and bi-objective functions to assess its efficacy in solving the OPF problem and its ability to handle large systems. A comparative study of the results, predominantly considering those obtained using quasi oppositional teaching learning optimization(QOTLBO), teaching learning optimization (TLBO), multiobjective harmony search algorithm (MOHS), nondominated sorting genetic algorithm II (NSGA-II) from the literature are detailed in this paper. Investigation of the results reveal that the algorithm is successful in producing superior results for both the systems and its performance is also encouraging in solving conflicting objectives.