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Techno-economic optimization of renewable hydrogen infrastructure via AI-based dynamic pricing
Paul C Okonkwo1, Samuel Chukwujindu Nwokolo2, Edson L Meyer3
1Mechanical & Mechatronics Engineering Department, College of Engineering, Dhofar University, Salalah, Oman.
Scientific Reports
|August 27, 2025
Summary
Australia
Area of Science:
- Renewable Energy Systems
- Techno-economic Analysis
- Hydrogen Production
Background:
- Australia possesses significant green hydrogen potential.
- Decarbonized hydrogen production is crucial for energy transition.
- Hybrid renewable energy systems offer a viable solution.
Purpose of the Study:
- To conduct a techno-economic optimization of hydrogen production using hybrid wind-solar systems across six Australian cities.
- To identify optimal system configurations and locations for cost-effective green hydrogen generation.
- To evaluate the impact of advanced algorithms and demand-side management on system performance.
Main Methods:
- Techno-economic modeling of hybrid photovoltaic-wind-electrolyzer-hydrogen tank systems.
- Optimization using metaheuristic algorithms (Mayfly, Gray Wolf, Whale Optimization).
- Sensitivity analysis incorporating blockchain and reinforcement learning strategies.
Main Results:
- Perth, Australia, demonstrated the lowest Levelized Cost of Hydrogen (LCH) at $0.582/kg.
- All studied locations achieved a 100% renewable fraction for hydrogen production.
- Metaheuristic algorithms improved techno-economic metrics by 3-8% compared to standard models.
- Blockchain and AI-driven demand response offered 8-10% cost savings.
Conclusions:
- Perth is identified as the optimal hub for green hydrogen production in Australia.
- Regional resource availability and urbanization significantly impact hydrogen production costs.
- Further investment in advanced materials and offshore platforms is recommended for export markets.
- AI-augmented, region-specific systems are key to achieving Australia's hydrogen economy goals.
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