Intelligent Optimization Algorithm to Synthesize Renewable Energy and Hydrogen Deployment Strategies

Toward Zero-Net Emissions Hybrid renewable energy systems (HRES) have emerged as a pivotal technology for mitigating greenhouse gas emissions, minimizing electricity consumption costs, and addressing the ever-growing demand for electric power. Consequently, reducing grid dependence has become a critical challenge in recent years for achieving net-zero emissions. This paper proposes an intelligent optimization algorithm based on a mixed-integer linear programming (MILP) approach for synthesizing and optimizing renewable energy deployment strategies. This method aims to minimize electricity costs and greenhouse gas emissions for various applications, including buildings, houses, and hospitals. The system incorporates wind turbines, photovoltaic solar panels, hydrogen fuel cells, and hydrogen storage, operating in both grid-connected and off-grid modes, allowing for a comparison between both. The MILP optimization not only determines the optimal energy source combination and quantity but also manages energy flow, including power exchange with the grid during surplus production. Additionally, the method integrates these processes within an energy semantic network. The results demonstrate that the proposed MILP approach effectively reduces total power costs and greenhouse gas emissions. It also identifies the optimal contribution from each renewable source, favors longer-lasting hydrogen batteries over traditional batteries, and utilizes the combined capabilities of hydrogen batteries and fuel cells to address the inherent variability of wind and solar energy.