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.
Highlights
Renewable energy sources such as wind and photovoltaic (PV) have attracted the attention of many countries all over the world because fossil fuels are expected to last only a few more decades
The results showed that the manta ray foraging optimizer (MRFO) algorithm converges faster than all other five soft computing algorithms followed by particle swarm optimization (PSO), and grasshopper optimization algorithm (GOA), respectively
MRFO, PSO, and GOA can follow the optimal global solution while the Harris hawks optimization (HHO), genetic algorithm (GA) and BBBC may fall into the local solution follow the optimal global solution while the HHO, GA and BBBC may fall into the local solution and take longer time to converge
Summary
Renewable energy sources such as wind and photovoltaic (PV) have attracted the attention of many countries all over the world because fossil fuels are expected to last only a few more decades. The genetic algorithm (GA) was employed to design/size a hybrid PV/WTGs/diesel energy system based on the ASC minimization considering LPSP as a constraint in order to electrify an off-grid area in Italy in [20]. In Reference [26], PSO was used for the optimal sizing of an HES including PV/WTGs/FC based on the minimization of NPC and LPSP in order to cover the load demand of an off-grid region in Mexico. In Reference [2], the cuckoo search (CS), GA, and PSO were employed for the optimal design of three different hybrid energy systems; PV/battery, WTGs/battery, and PV/WTGs/battery based on ASC minimization considering the seasonal load variation. In Reference [31], the optimal sizing of a HES including PV/WTGs/Tidal/battery were attained using crow search algorithm (CSA) based on minimizing the NPC considering the ELF as a constraint.
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