Aug-2021

Optimising green hydrogen production using system simulation

Using system simulation can help significantly to address the challenges of green hydrogen production and distribution.

Patrice Montaland
Siemens Digital Industries

Article Summary

In the very near future, hydrogen will play a key role in decarbonising the transportation, energy and industry sectors. Hence, many countries have already established policies to support development of a hydrogen infrastructure, and more and more industry stakeholders are investing in hydrogen projects. Despite this, hydrogen will only really emerge if it can be produced and distributed efficiently and safely at a competitive cost.

Addressing the challenge using system simulation
In order to help address these challenges, physics-based simulation can play a critical role to capture the complete process of green hydrogen from production to distribution. As demonstrated in this article, system simulation makes it possible to build the digital twin and predict the performances of a system combining wind turbines, solar panels and a wave converter with an electrolyser and a hydrogen compression system. Using simulation makes it possible to quickly evaluate different system architectures, components sizing and control strategies to produce hydrogen and green energy with the best global efficiency, system reliability and return on investment (ROI).

Green hydrogen production system model
In order to generate carbon-neutral energy, several sources can be used, such as wind turbines, solar panels, wave converters. These energy sources can be combined to improve performance and reliability. However, the energy produced fluctuates due to uncontrollable conditions and does not correspond continuously to the power demand. Energy storage is, therefore, mandatory. Using water electrolysis, this energy can be stored as hydrogen. Since hydrogen has a low density in ambient conditions, it is compressed before storage in high-pressure tanks. When the power demand exceeds the power produced by the solar panels, the wind turbine and the wave converter, a fuel cell generates the surplus electric power.

This kind of system involves a multitude of complex physics. That is a typical case where a system model (see Figure 1) can help better understand physical phenomenon and interactions between various subsystems and components. Hence, evaluating virtual design options makes it possible to better size components, integrate them in the best architecture and better control them to select the right design on the first attempt.

Off-the-shelf and validated models of the system simulation software tools are based on lumped parameters. So simulations can precede the detailed design of the components and be performed very early in the system design stages. Moreover, that makes simulation run very fast, enabling you to evaluate many configurations and scenarios, including ones with long time-range simulations. Further into the project, when the subsystems are better defined, models can still be refined, integrating more physics and capturing more dynamics. That makes it possible to improve simulation accuracy, allowing you to focus more on the component’s design.

The application case of this article is based on the following assumptions regarding the definition of the system:

- Wind turbines: Wind turbines have a 50 m radius. Their pitch is controlled, depending on the wind orientation. The wind orientation is variable. Its speed is also variable, considering the seasons (up to 8 m/s). The wind turbines can produce up to 350 kW of electrical power.
- Solar panels: The system is based on 62,500 cells of 16 cm2 each. It can deliver 20 kW of electrical power. The solar azimuth and altitude are dependent on localisation and time. They are supposed to be localised in Lyon (France). The impact of clouds is also variable. Add power output.
- Wave converter: The wave converter uses a float actuated by the waves to actuate a piston. The piston diameter is 235 mm. Then a hydraulic system combined with a generator is producing electric power. The waves have a variable amplitude (up to 3 m) and frequency (up to 0.07 Hz). What can be noticed here is that a first wave converter detailed model has been initially developed to evaluate the design and performances of this component. Then, to perform annual simulations, the model was used to train and generate a neural network model, reproducing the same simulation results but significantly reduced simulation time.
- Electrolyser: The electrolyser is made of 100 cells (300 cm2 each). With a maximal electric power of 330 kW, it can produce 6 kg/h of hydrogen.
- Hydrogen storage: The hydrogen can be stored in three different tanks of 0.785 m3 each. The maximum pressure is 750 bars. A control strategy activates some valves and selects the tank to be filled in or emptied.

A case study with system simulation
The simulation of the system, reproducing a one-year scenario, requires less than 15 minutes using a conventional laptop. After running a simulation, advanced pre- and post-processing tools help to better visualise and analyse the simulation results. Optimisation studies can also be performed.

Running the simulation makes, for instance, it possible to analyse the fluctuation of the energy produced by the solar panels, the wind turbines and the wave converter all year long. On a monthly analysis (Figure 2), we can visualise that the system is able to produce much more power during winter than during summer. Moreover, it can be noticed that, in the system, wind energy is the most predominant source, with 71% of the produced energy (22% for the wave converter, 7% for the wind turbines).

In these conditions, hydrogen production fluctuates a lot during the year (Figure 3). This reveals that, especially during summertime, it would be necessary to use electric power from the grid when the hydrogen content in the storage system reaches a low threshold.

Hydrogen compression to 750 bars also requires a significant power consumption. Thanks to simulation results, we can evaluate that compressing the gas consumes 6% of the electric power generated by the solar panels, the wind turbine and the wave converter. Using the model, it has also been evaluated that another architecture using two smaller compressors instead of a single one could help to save 1% of this global power production. Depending on the flow of H2 to be compressed, one or two compressors can be actuated, improving system efficiency.

Then, it can be interesting to use simulation to evaluate different design choices and rate them using criteria. Ratings can aim to:

- Reduce the system cost
- Reduce CO2 emissions considering the equivalent CO2 rejection of power sources
- Reduce the quantity of power exported to the grid
- Reduce the quantity of power imported from the grid or reduce the size of the hydrogen storage system

Simulation results (Figure 4) show that one of the best compromises can be made when we increase the size of the hydrogen storage and solar panels, and integrate smaller wind turbines.