Optimum utilization of grid-connected hybrid power system using hybrid particle swarm optimization/whale optimization algorithm

Network management and optimization play a crucial role in integrating renewable energy sources on the distribution network. This paper presents the optimization of the grid-connected photovoltaic/small hydropower hybrid power system using a hybrid particle swarm optimization-whale optimization algorithm. The exploration strength of the particle swarm optimization is hybridized with the exploitation capability of the whale optimization algorithm for improved convergence, speed, and optimal network performance. The objective function is to minimize the feeder voltage deviation and power loss. The effectiveness of the proposed approach is verified on the standard 118-bus distribution system. The simulation is carried out with step changes in irradiation and loading conditions. The optimization result from the two hybridized algorithms is compared to its standalone form, simulated under the same conditions. The results show that the proposed method is superior in the optimal solution and convergence property. The results confirm the technique's contribution to improved network management and grid-connected hybrid renewable power systems. It significantly reduced the voltage deviation by 62.58% on multiple locations of renewable sources. The voltage is achieved within the statutory range at all hours of the daily simulation. The technique is envisaged to be very useful in distribution network operators.