Sener and Ayesa apply quantum computing to optimise hydrogen production.
Sener and Ayesa have developed a proof of concept using a quantum solution to optimise the operational simulations of electrolysis plants, performed using Sener's SenHy digital tool. This solution enables all the processes involved in hydrogen production to be simulated using quantum algorithms, from the management of different power sources (solar generators, power purchase from the grid, battery storage) through to the operation and degradation management of electrolysis modules and the adjustment of hydrogen output flow to align it with demand forecasts.
The project aligns with the digitalisation goals of the IPCEI programme on which Sener is working, focused on advancing electrolysis technologies and reducing the levelised cost of hydrogen (LCOH). As part of this effort, the Sener Group has developed SenHy, an innovative operational simulation tool for electrolysis plants that can be used for different power supply profiles. The project will be showcased at the 6th meeting of the Basque Hydrogen Corridor (BH2C) Production Vertical on 25 September.
SenHy faces a significant technical challenge: solving a complex multiphysics optimisation problem every minute, based on the current and potential status of energy profiles and plant operating parameters. Given the limitations of traditional calculation tools, the model has had to be simplified to meet response times. To speed up this process and take full advantage of the complexity of the multiphysics model, Sener has conducted a proof of concept based on a quantum computing algorithm provided by Ayesa. This collaborative approach has succeeded in solving a simplified problem with the same degree of quality in one-tenth of the time, opening the door to simulating more complex cases and significantly improving the accuracy of the simulations.
This holistic vision, coupled with the scalability inherent in quantum logic, is enabling proposals for improved plant operation to be obtained in significantly reduced simulation times. In turn, this enables accurate planning over longer periods than what was previously possible with classical (non-quantum) methods.
Source: Ayesa
