Hybrid multi-objective method based on ant colony optimization and firefly algorithm for renewable energy sources

Abstract

This work presents a hybrid multi-objective optimization technique (HMO-ACO/FA) based on ant colony optimization (ACO) and firefly algorithm to address multi-objective optimum power flow (MOOPF) difficulties (FA) by eliminating certain operating restraints. In HMO-ACO/FA, an objective function (OF) vector with a vector of colonies has been modelled to be solved by an ACO, but the spare solutions in the search space have been projected to be found using a local search technique. Besides, FA has been projected to enhance the enactment and to obstacle the unfeasible solutions directly by presenting two moves for the fireflies. HMO-ACO/ The IEEE 30-bus standard and the Indian technical 62-bus scheme are both used in India have started FA to address three bi-objective characteristics: energy lower cost and power reducing losses; energy cost and power reduction; and power cost and energy loss reduction and L-index, as well as reductions in energy costs and pollutant emissions, as well as a tri-objective function that simultaneously reduces energy costs, reduces power losses, and improves voltage regulation. The findings were obtained by introducing two renewable energy sources (RES) into the test program, such as solar photovoltaic (PV) and wind farms. The findings show that HMO-ACO/FA can generate precise, MOOPF-free solutions with a well-distributed Pareto optimal distribution. The results of other published techniques in the literature were similar to those of the HMO-ACO/FA algorithms. The analysis' results show that HMO-ACO/FA performs better than other strategies in terms of discrete and compromise responses.