The Project
EP4H2: Tecnologías electrónicas avanzadas para la mejora de prestaciones de los electrolizadores: electrónica de potencia, control óptimo y conexión a red
- Lead Researchers: Asier Zubizarreta Picó - (01/04/2022 - 31/12/2023)
- Research Area: Intelligent manufacturing and industry
- Funding Body: Gobierno Vasco- Programa ELKARTEK de apoyo a la investigació
Abstract
The growing demand and the increase in global greenhouse gas emissions have increased the interest on new sources of renewable energy generation and cleaner fuels. Green hydrogen is positioned as an energy vector with the potential to drastically reduce our dependence of fossil fuels. The main goal of EP4H2 project is to provide innovative technologies that allow to increase efficiency and flexibility of hidrogen electrolyzers, while reducing their cost, which will utlimately allow to advance towards the production of Green Hydrogen. For this purpose, a consortium has been stablished, lead by Tecnalia R&I and in which other agents such as Ingeteam, Mondragon Unibertsitatea and the UPV/EHU take part. VISENS Research Group shares its know-how related to Artificial Intelligence and Model Predictive Control in this project, aimed to optimize the control systems of energy converters required for Electrolyzers. This consortium will work in innovative solutions that integrate IA to develop electrolyzers. Hybrid approaches will be integrated to manage optimally hydrogen production by using sun energy, so that this energy can be used for medium power applications.
Objectives
The main goal of EP4H2 project is to provide innovative technologies that allow to increase efficiency and flexibility of hidrogen electrolyzers, while reducing their cost, which will utlimately allow to advance towards the production of Green Hydrogen. The Consortium of the project is composed by Tecnalia R&I, Ingeteam, Mondragon Unibertsitatea and the UPV/EHU.
Achievements
Although the project has multiple achievements, the ones in which VISENS has taken part are:
-Development of a ANN-based methodology to approximate a high dimensional set of SHE modulation based firing angles.
-Development of a MPC-based control approach to optimize the harmonic content of an electric network making use of the previously defined ANN.
-Real Time implementation using hybrid hardware (CPU and FPGA) of the aforementioned developments.
Acknowledgements
This work has been funded by the Government of the Basque Country under project KK-2022/00039 (Elkartek program for Cooperative Research)