Grid2030 was a multi-year open collaborative initiative supported by EIT Innoenergy and Red Eléctrica and promoted by Elewit.
The three companies defined different challenges in innovation to find solutions to the problems of the electrical system as more renewables start to come online. These solutions include improving the system’s efficiency and flexibility, promoting the integration of renewables and optimising the performance and accessibility of assets in the transmission grid.
Entrepreneurs and innovators from public and private organisations, universities, research centres and European businesses participated in the development of the three projects.
The three projects include:
Flexible Smart Transformer:
An initiative of Red Eléctrica, Efacec (Portugal), and Circe (Spain). Grids need more flexibility and components with new functionality in the present energy transition. This project presents a modular prototype that uses power electronics and a transformer with magnetic coupling through a dielectric medium rather than a closed ferrite core and high-frequency switching. The combination of each module’s strong isolation capacity will enable the control of voltage levels and grid stability, which will increase the grids’ sustainability and efficiency.
Reduced Inertia Transient Stability Enhancement:
Developed by experts from the Supergrid Institute (France), the IMDEA Energy Institute (Spain), and Red Eléctrica, the project’s goal was to increase the electrical system’s flexibility in an environment where non-manageable energies are widely used. As tools for system operation, the project specifically created new stability resources and integrated controls to optimise the behaviour of HVDC-VSC links and storage systems.
Enigma:
A project between Red Eléctrica and Spanish businesses HI Iberia, Ingelectus, and Prysma, the project saw training for Artificial Intelligence (AI) agents to manage the energy that new renewable plants supply to the grid. The agents learned to respond to potential eventualities while keeping the frequency within the required margins using a single-node grid simulator and reinforcement learning techniques. The findings indicate a different approach to controlling these plants from a disruptive standpoint.
A total of €1.6 million ($1.7 million) was invested in the three projects within the framework of the programme, which had been running for four years.
