Techno-economic optimization of small-scale hybrid energy systems using manta ray foraging optimizer

Abstract

Hybrid energy systems (HESs) are becoming popular for electrifying remote and rural regions to overcome the conventional energy generation system shortcomings and attain favorable technical and economic benefits. An optimal sizing of an autonomous HES consisting of photovoltaic technology, wind turbine generator, battery bank, and diesel generator is achieved by employing a new soft computing/metaheuristic algorithm called manta ray foraging optimizer (MRFO). This optimization problem is implemented and solved by employing MRFO based on minimizing the annualized system cost (ASC) and enhancing the system reliability in order to supply an off-grid northern area in Saudi Arabia. The hourly wind speed, solar irradiance, and load behavior over one year are used in this optimization problem. As for result verification, the MRFO is compared with five other soft computing algorithms, which are particle swarm optimization (PSO), genetic algorithm (GA), grasshopper optimization algorithm (GOA), big-bang-big-crunch (BBBC) algorithm, and Harris hawks optimization (HHO). The findings showed that the MRFO algorithm converges faster than all other five soft computing algorithms followed by PSO, and GOA, respectively. In addition, MRFO, PSO, and GOA can follow the optimal global solution while the HHO, GA and BBBC may fall into the local solution and take a longer time to converge. The MRFO provided the optimum sizing of the HES at the lowest ASC (USD 104,324.1), followed by GOA (USD 104,347.7) and PSO (USD 104,342.2) for a 0% loss of power supply probability. These optimization findings confirmed the supremacy of the MRFO algorithm over the other five soft computing techniques in terms of global solution capture and the convergence time.