As the world accelerates its decarbonization plans, renewable hydrogen and its derivatives offer a promising alternative to fossil fuels—but to date, there are still no gigascale renewable hydrogen production plants in operation (see sidebar “Renewable hydrogen and its derivatives”).
The demand for clean hydrogen is expected to increase significantly. New plants could be scaled to meet this growing demand, which could require investments of $700 billion to maintain the 2030 net-zero trajectory.1
Currently, hydrogen demand is driven largely by the fertilizer and refining industries. The majority of hydrogen produced is grey hydrogen and, to a lesser degree, blue.2 To support net-zero ambitions, developers and investors could use a trusted technical and financial blueprint that quantifies risk and may accelerate the scale-up of green hydrogen plants. Digital twins—which can simulate a physical plant from the planning stage before it is built to the end of its lifetime—could help reduce the risks of investment, save costs, and speed up project timelines.
In this article, we highlight the obstacles preventing the build-out of large-scale renewable hydrogen production plants and explore how developers could overcome—or at least help to address—these challenges by using digital twins.
Challenges facing renewable hydrogen production today
Renewable hydrogen is still an emerging industry, facing short-term headwinds to realize its long-term potential. Costly infrastructure and the necessary hydrogen storage—as well as the variable nature of renewable energy sources themselves—also put pressure on plant economics.3 We have recently seen developers struggle to confirm final grant and investment approvals, across Europe and other regions.4
Uncertainties surrounding the technology, interest rates, regulatory schema, offtake market, and electrolyzer availability all act as hurdles to achieving large-scale production:
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Electrolyzers are a core component of any renewable hydrogen project. Recently, the capital expenditure (capex) required for electrolyzers increased by around 70 percent, driven by financing costs, labor, and materials.5 Variations in availability, price, and performance across a suite of options increase the complexity of project planning.6
While there are lower-cost options available for electrolyzers, cheaper does not always mean better. For example, some of the first large plants in operation are now underperforming on their minimum loads. This means that if the power output falls below 50 percent, these plants have to turn off the electrolyzers and remove them for maintenance to solve the problem.7 In the future, this may change, but developers face tough choices. They need to consider the trade-off between capex and performance, as well as the size and sequencing of electrolyzers used in the plant.
- Investors require a high level of confidence to reach a final investment decision (FID) due to the risks associated with megascale projects. Developers stand to benefit if they can secure offtake early on in the process. Once there is guaranteed offtake—ideally a minimum of 50 to 70 percent of production—it becomes easier to secure finance and increase investors’ confidence. However, in an environment of increased interest rates, securing a sufficiently low cost of debt to enable attractive equity internal rate of returns (IRRs) has become more difficult. This has led to some developers seeking less debt and requiring a greater share of equity—which typically increases upfront equity commitment costs by 10 to 25 percent.8
- While recent legislations create a favorable environment for boosting renewable hydrogen demand, understanding multiple regulatory frameworks can be challenging for developers. Definitions and requirements for production—for instance, regarding carbon intensity and where production facilities can be located, and how they match output and electricity consumption—differ across regions, which compels hydrogen producers to consider a wide range of designs and operational plans to have a compliant product for multiple markets. Legislation to boost clean hydrogen production includes the 2021 US Bipartisan Infrastructure Law and the US Inflation Reduction Act of 2022 (IRA), both of which could require closer integration between renewable energy sources and the plant.9 Meanwhile, the EU’s Renewable Energy Directive (RED III) mandates renewable fuel of non-biological origin (RFNBO) compliant hydrogen, which introduces its own set of requirements on additionality of renewable energy and matching of renewable energy used and hydrogen production.
For megaprojects to get the green light for development, projects could be designed to ensure the lowest possible production costs over the lifetime of the plant, within some of the constraints describes above. A digital twin can help to achieve this as it can evaluate hundreds or thousands of options and combinations of components to optimize plant design and increase investor confidence. The bulk of lifetime plant operating costs are locked in during design, and a digital twin supports decision making early on in the process before the plant is built.
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How digital twins unlock value
What is a digital twin?
A digital twin is a virtual representation of a physical product, system, or process used to understand and predict the physical counterpart’s performance. Digital twins are used to simulate and optimize the product and production system before investing in physical prototypes or implementing an active plant (exhibit). This exhibit shows a high-level view of a digital twin to cover as many configurations as possible, but the power of the digital twin comes into effect when the details are added. Each step of the process can include endless details to get the most out of the digital twin and optimize the entire process.
Digital twins can be used to address the typical production cost challenge by testing and iterating input design parameters to optimize for cost (the levelized cost of “X” (LCOx) and net present value (NPV)) over the lifetime of the asset.1
Despite the challenges, hydrogen megaproject momentum is building as early movers transition to pre-FID design and engineering. Global developers are now looking for ways to solidify their business cases—and digital twins can play a significant role in this regard (see sidebar “What is a digital twin?”).
Reducing production costs
Leveraging probabilistic advanced analytics (AA) and generative-AI (gen AI) techniques, digital twins can help clarify project viability by quantifying the impact of external factors on the economic performance of a potential project design over its operating lifespan. By fully embracing digital twins, production costs (LCOx) could be reduced by 5 to 15 percent (Exhibit 1).
Some developers are already reining in production costs by using a digital twin to quantify risks and ensure that investments have the highest probability of delivering desired returns. For example, one global energy company with a series of megaprojects for renewable hydrogen and ammonia was able to identify $500 million of NPV improvement potential (see sidebar “Case study: How a global energy company optimized the design of its renewable ammonia plant”).
Optimizing plant design
The power of a digital twin lies in its ability to quickly evaluate plant complexities, identify optimized setups, and compare alternatives against a set of constraints (for example, regulatory requirements for green hydrogen). It can investigate all possible alternate design strategies, such as different storage sizes, multiple electrolyzers and their yields, and balance of plant (BoP) setups, and can explore a broader solution space by using AI optimization for process modeling.
For instance, digital twins can simulate and compare the performance of multiple types of electrolyzers under different conditions, which will increase the confidence in the planned design of the plant. For some projects, it might be beneficial to “oversize” the electrolyzer capacity while others may require more flexibility to balance electrolyzer capacity with storage capacity.
Digital twins can improve design where traditional approaches often fall short and can help to solve complex trade-offs inherent in e-plant design and engineering, such as power firmness, load factor, or subcomponent selection. Some examples are provided below.
Power firmness: Digital twins can help to solve complex trade-offs in power firmness by comparing the lowest-cost intermittent power sources with the need for energy storage or grid firming. This can achieve the balance of high-firmed output with an increased levelized cost of electricity (LCOE).
Load factor: Digital twins can help developers understand the electrolyzer stack degradation impact on the levelized cost of hydrogen (LCOH) while defining an operative strategy. This includes defining the load factor of the different modules or arrays of the electrolysis plant. This can then be fed directly into the operating facility and planned maintenance strategy.
Process design integration: When operating a large-scale industrial process (such as Direct Reduction of Iron, Fischer-Tropsch, Haber-Bosch, or Sabatier), high uptime and continuous process operation are critical for reactor stability and efficiency. However, renewables are inherently intermittent. Designing the end-to-end, power-to-molecules system that balances capex on renewable energy sources, buffer stores, and reactor uptime is the largest driver of LCOx of the plant. Digital twins can rapidly assess and optimize the potential sizing of all elements of the system, even factoring in probabilistic elements, such as weather patterns, equipment failure, and evacuation schedules.
Sub-component selection: BoP selection is a major driver of system capex and operating expenses (opex). Digital twins can help developers choose between equipment and component options. For example, modeling can show whether it is better for plant economics to pursue fewer, larger components (such as compressors or pumps) with concentrated failure points or to install many modules of smaller components and embed additional redundancy in the process.
Product storage size: Hydrogen storage is expensive and different options can be explored. For example, a developer can invest in a smaller hydrogen or ammonia storage capacity with a great risk of stoppage, or they could choose a larger storage buffer and bear the related cost. This can be modeled to integrate with offtake schedules and contractual negotiations.
Global Energy Perspective 2023: Hydrogen outlook
Maximizing the value of a digital twin
Digital twins can deliver benefits far beyond initial design and development, too. When properly set up during plant design, digital twins can form the basis of a variety of use cases throughout the plant’s life cycle.
Operations: Plant design and operations are initially considered and optimized together with a digital twin, providing greater clarity of the operation and maintenance needs of the plant to enhance planning. This technology can also provide greater cost certainty on the plant’s opex in advance.
Financing and investment: Having a reliable and proven digital twin could make financing and investors’ decisions less risky, creating a better position to enhance financing rates and negotiate offtake contracts.
Serial project delivery: Having an established digital twin and embedded capability can allow faster, lower-cost development of all future projects, and enable developers to quickly test whether project concepts are viable. For example, when prioritizing two projects, they can rapidly be virtually modeled and assessed. Developers can reuse their concepts for all future projects and can decide from very early on in the process which projects to drive forward and which ones to stop immediately.
Finance and management: A curated and customizable dashboard can be created to provide easy-to-access insights for a range of senior stakeholders, supporting negotiation and project stage gate reviews, as well as regular reporting once in operation.
Auditing: Digital twins can even be used as a basis for internal and external auditing, for example, by contrasting the real events on a plant’s unexpected cold stop with the model responses. They can also provide solid, future-looking economics for further operational choices.
Renewable hydrogen is a much-needed resource for helping the world achieve its decarbonization goals. With the move toward hydrogen megaprojects, it is critical to make sure that plants are designed in the most economical fashion to help secure investments and reach FID. Digital twin technology can not only help to reduce initial capex costs but also drive efficiency over the lifetime of the plant.
