A Hybrid Framework Integrating Meta-Analytic Evidence, Physics-Informed Deep Learning, and Monte Carlo Simulation for Risk and Optimization of Green Hydrogen Production via Hybrid Renewable Systems

Abstract

Green hydrogen supplied by variable solar and wind resources is exposed to coupled technical, economic, environmental, and operational uncertainty that deterministic optimization can conceal. This study develops a four-layer decision framework with explicit data contracts: structured evidence synthesis produces parameter priors, bounds, correlations, and provenance; a physics-informed deep-learning (PIDL) surrogate maps operating states to physically admissible electrolyzer-specific electricity consumption; Latin-hypercube Monte Carlo simulation propagates the joint priors through an hourly rule-based dispatch model; and Conditional Value-at-Risk (CVaR) ranks a predefined set of candidate designs by expected and tail cost. The numerical demonstration uses one synthetic renewable year, literature-derived cost distributions, and synthetic PIDL observations; it therefore verifies internal behavior rather than real-world or site-specific performance. Within this illustrative benchmark, PIDL reduced test RMSE from 0.70 to 0.21 kWh kg-1 and eliminated out-of-domain test predictions. Across 10,000 scenarios, the lowest-cost candidate in the tested set achieved a mean LCOH of USD 4.62 kg-1 and a 95% CVaR of USD 6.49 kg-1, compared with USD 5.08 and USD 7.16 kg-1 for the designated risk-neutral case. Financing costs, electrolyzer capital costs, and wind resource uncertainty dominated the variability. Because these costs remain above typical unabated fossil-hydrogen benchmarks, early projects would require a combination of de-risked finance, long-term offtake, demand creation, production support, and/or carbon-value instruments. The results do not include plant validation, battery aging, or explicit logistics for hydrogen storage, transport, and delivery.