This paper develops a recent population-based of Equilibrium Optimizer Algorithm (EOA) for solving the optimal power flow (OPF) problem in hybrid AC/DC power grids. The proposed OPF problem handles several objective functions that reflect multi dimensions economic-technical and environmental operation requirements of modern power systems. The considered objectives incorporate minimizing the total generation costs, generation environmental emissions, total power losses, and the deviations of the bus voltages. These objectives are handled separately and simultaneously to provide the preference capability to the operator objectives. EOA has adaptive dynamic control parameters. It mimics the dynamic and equilibrium states related to the mass balance models where, each concentration of search agent is randomly updated in the sake of reaching the final optimal fitness. Finally, 12 case studies are carried out via the developed EOA, particle swarm optimizer and differential evolution for the modified IEEE 14-, 30-bus test systems and West Delta power system (WDPS) in Egypt. The second-order cone OPF model is considered. Also, adding wind units and their impacts on the OPF solution is assessed for the hybrid AC/DC grid. The simulation results demonstrate the great effectiveness of the developed EOA in solving the OPF in hybrid power systems. |
