Computational intelligence for sustainable hydrogen microgrids: A nonlinear optimization framework

Sustainable hydrogen-centric carbon-free microgrids constitute complex adaptive energy systems characterized by strong nonlinear couplings, nonconvex operational constraints, and multi-scale temporal dynamics. Scheduling such systems therefore represents a high-dimensional nonlinear optimization problem in which classical deterministic methods often fail to provide scalable and robust solutions. This paper proposes an intelligent decision-support framework for the optimal scheduling of hydrogen-based carbon-free microgrids, integrating electrolyzers, fuel cells, renewable energy sources, batteries, electric vehicles, demand response, and hydrogen storage. The resulting system exhibits emergent operational behavior arising from the nonlinear interactions between electrical and hydrogen subsystems. To address this complexity, an Enhanced Adaptive Golf Optimization Algorithm (EAGOA) is employed to navigate the nonconvex and multimodal search space efficiently. Benchmark comparisons demonstrate that EAGOA achieves superior convergence reliability and solution quality compared to Particle Swarm Optimization, Genetic Algorithms, JAYA, and Grey Wolf Optimizer. The proposed framework achieves a minimum system cost of 102 M$, with a convergence time of 207 s, representing a reduction in computational time and improved convergence reliability compared to benchmark methods. Furthermore, the model demonstrates scalability across planning horizons ranging from 7 to 365 days. Sensitivity and robustness analyses further reveal nonlinear system responses to uncertainty, component degradation, and renewable variability, highlighting the effectiveness of the proposed approach in capturing complex system dynamics. These results confirm the suitability of nonlinear evolutionary optimization for managing large-scale hydrogen-based microgrids and contribute new insights into the nonlinear behavior of carbon-free energy systems.