Multi-agent Based Optimal Sizing of Hybrid Renewable Energy Systems and their Significance in Sustainable Energy Development

This paper delves into the enhancement and optimization of on-grid renewable energy systems using a variety of renewable energy sources, with a particular focus on large-scale applications designed to meet the energy demand of a certain load. As global concerns surrounding climate change continue to mount, the urgency of replacing traditional fossil fuel-based power generation with cleaner, more cost-effective and dependable alternatives becomes increasingly apparent. In this context, a comprehensive investigation is conducted on grid connected hybrid energy system that combines photovoltaic, wind, and fuel cell technologies. The study employs three state-of-the-art optimization algorithms, namely Walrus Optimization Algorithm (WaOA), Coati Optimization Algorithm (COA), and Osprey Optimization Algorithm (OOA) to determine the optimal system size and energy management strategies, all aimed at minimizing the cost of energy (COE) for grid-based electricity. The results of the optimization process are compared with the results obtained from the utilization of the Particle swarm optimization (PSO) and Grey Wolf optimizer (GWO). The findings of this study underscore both the practical feasibility and the critical importance of adopting on-grid renewable energy systems to decrease the dependence on traditional energy sources within the grid. The proposed WaOA succeeded to reach the optimal solution of the optimal design process with a COE of 0.51758129611 $//kwh while keeping the loss of power supply probability (LPSP), the reliability index, at 7.303681e-19. The practical recommendations and forwardlooking insights provided within this research hold the potential to foster sustainable development and effectively mitigate carbon emissions in the future.