In this research work, a renewable and clean energy system comprising of wind power, solar power and battery storage is designed with the primary objective of optimizing the total annual operating cost. Artificial Hummingbird Algorithm (AHA) is applied to size the system in such a way that it reduces the operating cost while satisfying the load demand and the regulatory constraints. A publicly available dataset mimicking the load demand, wind speed and solar insolation of an off-shore small island is used as simulation parameters and three test variants are defined to challenge AHA in solving the optimization problem. The simulation results revealed that the AHA has reduced the overall expenditure while fulfilling the system requirements and constraints to a level of \$6691.88/annum or \$0.39/kWh, which is proven to be superior when compared to other renowned algorithms like Particle Swarm Optimization, Grey Wolf Optimization and Artificial Bee Colony. The attained simulation results also highlight the efficacy and potential of AHA in solving complex power system design problems.