Techno-economic-environmental analysis of off-grid hybrid energy systems using honey badger optimizer

This study explores the performance evaluation of recently developed metaheuristic optimization techniques used to address the sizing problem for five different stand-alone hybrid renewable energy systems applied to a rural community in India. The algorithms include the honey badger optimization algorithm (HBO), golden jackal optimization (GJO), and arithmetic optimization algorithms (AOA), intending to minimize the total annual cost (TAC) of the system while maintaining an acceptable loss of power supply probability and renewable fraction. The adopted algorithm has been simulated for 25 independent runs to determine its superiority. The outcomes of simulations were compared with popular algorithms such as cuckoo search (CS), the grey wolf algorithm (GWO), and HOMER software. According to the findings, a hybrid system integrating solar power (116.4 kW), battery storage (148.5 kW), and a diesel generator (15.7 kW) is the most cost-effective system. While comparing all techniques, the TAC of HBO ($33797) with a standard deviation (SD) of 2.29 and GWO ($33799) with a SD of 2.76 are the most economical. However, while comparing these, HBO obtained the least SD, which indicates a lower oscillation rate and a better trade-off exploration-exploitation balance than GWO and affirmed that HBO is well suited for solving this optimization problems.