Intelligent Process Optimization for Green Hydrogen Production Networks

Thesis Abstract

The transition to green hydrogen hinges on an integrated, ICT-enabled optimization of production networks that can accommodate variable renewable generation, storage constraints, and evolving market signals. This study addresses the bottleneck of suboptimal process coordination across electrolysis units, renewable supply forecasts, and pipeline and storage logistics, which collectively impede cost-effective decarbonization at scale. The aim is to develop an intelligent optimization framework that autonomously dispatches electrolysis capacity, storage, and outbound distribution to minimize levelized cost of hydrogen (LCOH) while meeting reliability and emission targets. Specific objectives are (i) to formulate a multi-objective optimization model that integrates dynamic electricity prices, electrolyzer degradation, and grid-imposed constraints; (ii) to embed predictive analytics for renewable availability and hydrogen demand using time-series forecasting and probabilistic scenario generation; (iii) to implement a hybrid decision-support architecture combining model-based optimization with data-driven reinforcement learning to adapt policies in real time; (iv) to evaluate robustness under uncertainty through stochastic programming and sensitivity analysis; and (v) to validate the framework on a representative regional network with 120 MW electrolysis capacity, 4 storage modes, and 3 demand zones. The methodology adopts a mixed-methods, ICT-driven, systems engineering approach anchored in operations research and control theory. The population comprises operating hydrogen production networks in regions with high renewable penetration. Sampling selects a realistic dataset from a regional energy system, consisting of 24 months of hourly data for electricity prices, solar and wind forecasts, electrolyzer availability, and hydrogen demand. Data collection instruments include SCADA-like process logs, public energy-market benchmarks, and structured interviews with operators to capture reliability constraints. The analytical core combines (i) a multi-objective mixed-integer linear programming (MILP) model for day-ahead and real-time dispatch; (ii) a stochastic programming layer to handle forecast uncertainty with scenario trees; (iii) a recurrent neural network (RNN) and extreme gradient boosting (XGBoost) ensemble for predictive analytics of renewables and demand; (iv) a reinforcement learning (RL) agent, integrating proximal policy optimization (PPO), to adapt control policies under unforeseen events; and (v) a degradation-aware electrolyte model to account for efficiency-loss and lifecycle impacts. Model validation employs back-testing against historical periods and stress-testing under extreme price spikes. Performance metrics include LCOH, capacity factor, peak shaving, emissions intensity, and reliability indices. Sensitivity analyses explore the impact of storage costs, transmission constraints, and policy variations. Expected findings indicate that the integrated optimization framework reduces LCOH by 12–18% relative to baseline heuristic scheduling, improves storage utilization efficiency by 25%, and increases electrolyzer capacity factor by 8–12% under high-renewable scenarios. The predictive analytics component is anticipated to achieve a 15–20% improvement in forecast accuracy for renewable generation and hydrogen demand, enabling more proactive dispatch decisions. The RL component is expected to offer adaptive policy improvements in response to unforeseen disruptions, with convergence within 200–300 episodes and stable performance under 95% confidence intervals across scenarios. The study also characterizes the trade-offs between capital expenditure on storage and dynamic operation costs, providing a lifecycle optimization pathway. Contributions to knowledge include (i) a novel, integrative ICT-enabled optimization architecture for green hydrogen networks that fuses MILP, stochastic programming, ML-driven forecasting, and RL-based control; (ii) a degradation-aware optimization framework linking electrolyzer service life to economic scheduling; (iii) a rigorously tested methodology for coordinating multi-technology assets (electrolysers, storage, and logistics) under uncertainty; and (iv) empirical insights into the value of predictive analytics in hydrogen supply chain optimization. The main conclusion indicates that ICT-driven intelligent optimization enables substantial reductions in production cost and emissions while enhancing system reliability in hydrogen networks. Recommendations advocate for standardized data interfaces across hydrogen assets, deployment of pilot projects in regions with strong renewable integration, and further research into uncertainty quantification and governance of autonomous decision systems in energy networks.