Application of ACSA for solving multi-objective optimal power flow problem with load uncertainty

This paper presents a multi-objective adaptive Clonal selection algorithm (MOACSA) for solving optimal power flow (OPF) problem. OPF problem is formulated as a non-linear constrained multi-objective optimization problem in which different objectives and various constraints have been considered. Fast elitist non-dominated sorting and crowding distance techniques have been used to find and manage the Pareto optimal front. Finally, a fuzzy based mechanism has been used to select a best compromise solution from the Pareto set. The proposed MOACS algorithm has been tested on IEEE 30-bus test system with different objectives such as cost, loss and L-index. Simulation studies are carried out under both normal load and load uncertainty conditions for multi-objective optimal power flow problem with different cases. The results obtained with normal load condition are also compared with fast non-dominated sorting genetic algorithm (NSGA-II), multi-objective harmony search algorithm (MOHS) and multi-objective differential evolutionary algorithm (MODE) methods which are available in the literature.