Optimizing a Hybrid Energy System with Photovoltaic-Wind-Battery Storage Using Meta-Heuristic Optimization Algorithms

This paper presents a comprehensive approach to the development of an economically viable, reliable, and environmentally sustainable hybrid photovoltaic-wind-battery system. Various optimization algorithms, including Particle Swarm Optimization (PSO), Moth Flame Optimization (MFO), Ant Lion Optimization (ALO), and Grey Wolf Optimization (GWO) are employed to minimize the Life Cycle Cost (LCC) and enhance reliability over a 25-year lifespan. These algorithms are utilized to optimize the system components such as PV panels, wind turbines, and Lithium-ion Batteries, taking into account investment, maintenance, repair, replacement costs and reliability measured by the Loss of Power Supply Probability (LPSP). Comparative evaluations with GWO demonstrate that the hybrid system incorporating all elements achieves a remarkable reduction in LCC from $3.039 million to $2.147 million and a reduced LPSP loss of 0.01 compared to other algorithms. The cost of energy (COE) is also notably reduced from $0.9019/kWh to $0.6372/kWh. The proposed algorithm methods outperform by effectively reducing LCC while enhancing reliability with superior convergence speed and accuracy. Furthermore, this study investigates the impact of component availability on overall design; reduced availability is shown to increase LCC and compromise reliability indices.