Green Hydrogen Cost-Potentials for Global Trade (2303.00314v2)

Abstract: Green hydrogen is expected to be traded globally in future greenhouse gas neutral energy systems. However, there is still a lack of temporally- and spatially-explicit cost-potentials for green hydrogen considering the full process chain, which are necessary for creating effective global strategies. Therefore, this study provides such detailed cost-potential-curves for 28 selected countries worldwide until 2050, using an optimizing energy systems approach based on open-field photovoltaics (PV) and onshore wind. The results reveal huge hydrogen potentials (>1,500 PWhLHV/a) and 79 PWhLHV/a at costs below 2.30 EUR/kg in 2050, dominated by solar-rich countries in Africa and the Middle East. Decentralized PV-based hydrogen production, even in wind-rich countries, is always preferred. Supplying sustainable water for hydrogen production is needed while having minor impact on hydrogen cost. Additional costs for imports from democratic regions are only total 7% higher. Hence, such regions could boost the geostrategic security of supply for greenhouse gas neutral energy systems.

• The paper presents a detailed modeling framework that optimizes the complete process from renewable generation to LH2 export using spatially and temporally explicit cost-potential curves.
• It finds that by 2050, selected countries can export over 1,540 PWh LHV/a of hydrogen, with certain regions achieving costs below 2.30 EUR/kg.
• The study emphasizes the importance of PV-dominated systems, integrated grid and liquefaction assessments, and technology mix optimization for effective global hydrogen trade.

This paper (2303.00314) provides a detailed analysis of the cost-potentials for green hydrogen production and export as liquid hydrogen (LH2) from selected countries worldwide, considering the full process chain from renewable energy generation to liquefaction and storage at an export harbor. The paper aims to fill a gap in existing research by providing spatially- and temporally-explicit cost-potential curves necessary for developing global trade strategies.

The methodology utilizes an optimizing energy systems approach based on open-field photovoltaics (PV) and onshore wind power. The core steps involve:

1. Country Selection: 28 countries with high solar and wind resources were selected, aiming for balanced global coverage across TIAM model regions.
2. Renewable Energy Potential Derivation: Land eligibility analysis using pyGRETA [40] based on exclusion criteria (slopes, settlements, land use conflicts, conservation areas) was performed. Hourly electricity time series data for 2019 from MERRA-2 weather data were generated and spatially resolved (250m x 250m for wind, 50km x 50km aggregated to 250m x 250m for solar). These potentials were clustered within GID-1 regions based on full load hours.
3. Energy System Modeling and Optimization: The Flexible Integrated Energy (FINE) framework [33, 34] was used to model and optimize 36 discrete energy systems per country (9 export demands across 4 years: 2020, 2030, 2040, 2050). The model minimizes total system costs, which equate to the LH2 cost at the harbor. The system considers onshore wind, open-field PV, PEM electrolysis, batteries, AC electric grid, gaseous hydrogen pipelines, LH2 liquefaction, and LH2 storage. A greenfield approach is used for grid infrastructure modeling. Export locations are assumed to be major industrial ports, except for Turkmenistan. Water usage for electrolysis is assumed to be from groundwater, with the minor cost impact of seawater desalination considered.
4. Local Transmission Grid Calculation: A post-processing step designs a local transmission grid connecting RES parks (clustered within a 5km radius) to electrolysis units or the GID-1 electrical grid using a minimum spanning tree approach. Costs for this local grid are added to the optimized system costs.
5. Cost Calculation: The total annual energy system cost from optimization ($TAC_{opt}$) plus the local grid cost ($C_{local\_grid}$) is divided by the annual exported hydrogen amount ($H_{2,export}$) to get the hydrogen export cost: $$c_{H2} = \frac{TAC_{opt} + C_{local\_grid}}{H_{2,export}}$$.
6. Cost-Potential Curve Generation: Discrete hydrogen export amounts (up to 95% of maximum technical potential) and their calculated costs are combined to form cost-potential curves for each country and year.

Key Findings:

• Potential: The studied countries have a combined exportable LH2 potential of over 1,540 PWh LHV/a by 2050, significantly exceeding current global energy consumption. About 79 PWh LHV/a is available at costs below 2.30 EUR/kg in 2050.
• Cost Drivers: Renewable energy sources and electrolysis are the main cost contributors, accounting for ~65% of total hydrogen costs.
• Cost Development: Costs are projected to drop significantly between 2020 and 2030, with the first countries reaching costs below 2.50 EUR/kg around 2040-2050.
• Optimal System Design:
  • Countries are grouped into three categories: Group I (cheap, large-scale solar), Group II (medium-to-high cost, large potential), and Group III (small potential).
  • Group I countries primarily utilize solar energy, achieving low, relatively stable costs across their potential, with curtailment and decentralized electrolysis being preferred flexibility options.
  • Group II and III countries often utilize a mix of wind and solar, leading to higher electrolysis full load hours but also higher costs due to transport and grid infrastructure needs. Batteries are used more in Group II, particularly when high transport costs and lower RES full load hours are present.
  • Decentralized hydrogen production via electrolysis located near RES generation is often preferred over transporting electricity.
• PV Dominance: Low-cost solutions (Group I) rely heavily on PV. Even wind-rich countries tend to utilize more PV capacity than wind to achieve lower hydrogen costs, suggesting PV is generally favored due to lower levelized costs and potentially higher full load hours in optimal locations compared to wind assumptions in this paper.
• Sensitivity: Hydrogen costs are sensitive to the investment costs of PV, wind, PEM electrolysis, and liquefaction. Countries heavily reliant on one technology (e.g., Oman on PV, Germany on wind) show higher sensitivity to its cost variations. Liquefaction costs significantly impact large-scale export costs, especially in Group I countries, and favorable liquefaction scaling (beyond the current largest LNG train size) can further reduce costs. High liquefaction costs can increase the economic viability of battery storage to ensure high liquefaction plant utilization.
• Geopolitical and Water Aspects:
  • Sourcing hydrogen from democratic countries imposes a cost premium (~7% at high export volumes, potentially higher with specific transport routes).
  • A significant portion of the cheap, solar-based hydrogen potential is located in water-stressed regions. While seawater desalination is a viable alternative with minimal cost impact (~0.01 EUR/kg H2) according to external studies, its sustainable implementation is crucial.

Practical Implementation and Application:

This research offers actionable insights for developers and policymakers:

• Identifying Investment Opportunities: The paper pinpoints regions (e.g., Oman, Namibia, parts of Australia, Saudi Arabia) with high potential for low-cost green hydrogen production, guiding investment strategies for project developers and international partnerships.
• Designing Hydrogen Export Projects: The model structure and findings provide a blueprint for designing comprehensive hydrogen export supply chains. It highlights the need to consider the full value chain costs (generation, collection grids, transport pipelines, liquefaction, storage) rather than just generation costs in isolation. The spatial resolution (GID-1 level and local grid modeling) is critical for accurate infrastructure cost estimation.
• Technology Selection and Sizing: The analysis of optimal system designs across different country groups informs technology choices (PV vs. Wind mix, decentralized vs. centralized electrolysis) and component sizing (electrolysis capacity, storage needs, grid requirements) based on local renewable resources and export targets.
• Energy System Modeling: The paper's use of the FINE framework and detailed modeling of system components (with specific techno-economic parameters provided in Table 2) can serve as a basis for developing or adapting energy system models for hydrogen applications. The clustering approach for RES potentials and the post-processing step for local grids are practical techniques for managing computational complexity while maintaining spatial detail.
• Risk Assessment: The consideration of political regimes and water stress adds crucial layers to risk assessment for international hydrogen trade projects. This informs sourcing diversification strategies and necessitates incorporating sustainable water management solutions (like desalination with best practices) into project planning.
• Market Analysis: The cost-potential curves provide essential data for market analysis and trade strategy development for both exporting and importing countries, allowing comparison of import costs from different regions and hydrogen carriers (although this paper focuses on LH2).

The paper's detailed breakdown of costs per technology (Table 3) and sensitivity analysis (Table 4, Figure 9) provide concrete data points for financial modeling and risk management in green hydrogen projects. The comparison with costs of hydrogen from fossil fuels with CCS highlights the potential cost-competitiveness of green hydrogen by 2040-2050, depending on gas prices.

Limitations, such as the dependence on exogenous cost assumptions and the focus on large-scale export, should be considered when applying these results. Future work incorporating endogenous learning rates, local demand, and other RES technologies would further refine the analysis.