image: The project “Management of Renewable Systems with Storage and Converter Control to Contribute to the Operation of the Future Power System”, led by Professors Emilio Pérez Soler and Ignacio Peñarrocha Alós, members of the Electricity, Electronics and Automation Research Group, is progressing toward its goal of integrating renewable energies through the development of advanced control strategies and their experimental validation on a platform that allows real-time testing of the combined operation of batteries, converters and controllers. So far, the research team has developed predictive models related to the electricity market, such as daily market prices and services aimed at regulating the power system frequency. These models have been used to define a strategy based on deep reinforcement learning, enabling optimal participation of grid-connected storage systems in various electricity markets. Regarding lithium-ion batteries, new techniques have been developed to estimate their state of charge and health, improving both performance and lifespan. The group has also demonstrated that advanced control can achieve more reliable operation of renewable energy plants by maximizing the time they remain connected to the grid, thereby enhancing overall power system stability. The research is now entering its final phase, focused on the joint experimental validation of the proposed control strategies, which will take place throughout this year, coinciding with the project’s completion. Photo: Members of Electricity, Electronics and Automation Research Group.
Credit: Universitat Jaume I of Castellón
The balance between electricity supply and demand requires advanced technologies and precise management, especially given the growing presence of renewable sources. Researchers are working on new energy storage and control strategies to ensure a stable and secure energy supply, preventing blackouts like the one that occurred on April 28, 2025.
The project “Management of Renewable Systems with Storage and Converter Control to Contribute to the Operation of the Future Power System”, led by Professors Emilio Pérez Soler and Ignacio Peñarrocha Alós, members of the Electricity, Electronics and Automation Research Group, is progressing toward its goal of integrating renewable energies through the development of advanced control strategies and their experimental validation on a platform that allows real-time testing of the combined operation of batteries, converters and controllers.
So far, the research team has developed predictive models related to the electricity market, such as daily market prices and services aimed at regulating the power system frequency. These models have been used to define a strategy based on deep reinforcement learning, enabling optimal participation of grid-connected storage systems in various electricity markets. Regarding lithium-ion batteries, new techniques have been developed to estimate their state of charge and health, improving both performance and lifespan.
The group has also demonstrated that advanced control can achieve more reliable operation of renewable energy plants by maximizing the time they remain connected to the grid, thereby enhancing overall power system stability. The research is now entering its final phase, focused on the joint experimental validation of the proposed control strategies, which will take place throughout this year, coinciding with the project’s completion.
The project has also fostered synergies with specialists from the University of the Basque Country, the Tyndall National Institute in Cork (Ireland), and the Virtual Vehicle research centre in Graz (Austria), working respectively on renewable energy integration, smart grids and electric vehicles. Collaborations have also been established with the companies Abervian, specializing in applications for storage systems, and HESStec, specializing in providing synthetic inertia to renewable installations.
This research is part of project PID2021-125634OB-I00, funded by MICIU/AEI/10.13039/501100011033 and FEDER/EU under the 2021–2023 State Plan for Scientific, Technical and Innovation Research, aimed at boosting strategic sectors for recovery, such as health, ecological transition and digitalization.
Publications: https://repositori.uji.es/search?query=PID2021-125634OB-I00&spc.page=1&spc.rpp=20
Journal
Mathematics and Computers in Simulation
Method of Research
Computational simulation/modeling
Subject of Research
Not applicable
Article Title
Deep learning-based prediction models for spot electricity market prices in the Spanish market
Article Publication Date
16-Jul-2025