Optimal Sizing of a Hybrid Power Supply for a Green Hydrogen Production Plant Using Metaheuristic Algorithms

Green hydrogen holds significant promise in promoting the sustainable development goals, particularly in providing affordable and clean energy, mitigating climate change, and fostering sustainable industrialization. However, its widespread adoption faces economic challenges, primarily due to the high costs associated with renewable energy systems required for its production via water electrolysis. Building upon previous research on assessing the viability of green hydrogen in which a hybrid system (combination of wind and solar power) was seen as the most viable, the focus of this study lies in determining the optimal contribution of each renewable energy source. To address this, various metaheuristic algorithms, including Genetic Algorithm, Particle Swarm Optimization (PSO), and Flower Pollination Algorithm (FPA), are employed. This study provides valuable insights into optimizing the selection of renewable energy sources for green hydrogen production revealing that a hybrid solution, comprising of a single 8MW offshore wind turbine augmented with 23 MW of solar, best satisfies the proposed plant requirements. Notably, all algorithms converge to similar values with minor variations, underscoring the robustness and consistency of the proposed approach.