Project Description

Project PID2021-126001OB-C31 funded by MCIN/AEI/10.13039/501100011033/ and by "ERDF A way of making Europe"

Redox Flow Batteries are reversible electrochemical devices with a promising role in renewable power generation systems, where they can fulfil important functions, such as load balancing. In the current decarbonization context, they are expected to become a competitive solution with excellent trade-off between cost and lifetime, especially suitable for long-time large-scale energy storage. A main difference between Redox Flow Batteries and others types of batteries is that they are active elements and consequently a control system is required. The main subproject objective is the development of control systems that increase the system performance and efficiency in comparison with the results reported in the bibliography. At the same time, the control and monitoring systems of the prototypes manufactured and assembled within the project will be developed in this subproject, which will make it possible to test and characterize them in the laboratory and in their final applications.

In order to obtain better controllers, new dynamic control-oriented models that describe all the RFB relevant phenomena will be developed, including the causes of imbalance in both the amount of vanadium and the volume of electrolyte in the two half cells. The convenience of moving from lumped parameter models to distributed models will be deeply analyzed. Furthermore, experimental procedures and algorithms required for the fitting of the model parameters will be described. Based on these models, the use of additional actuators for dynamic control will be analyzed, and new controllers will be designed. One of the main issues for an optimal operation is the difficulty of knowing the RFB internal state, e.g., its state of charge or its state of health. This knowledge would enable restoration actions that improve performance and prevent accelerated degradation of components. For this reason, methods based on the use of state observers and nonlinear on-line parameter estimation techniques will be designed. Additionally, these techniques will be combined with artificial intelligence methods to improve their performance. The resulting estimation systems will be integrated with the controllers, implemented and validated experimentally with a commercial stack in the groups laboratory first, and with the project prototypes, later. At a higher level, the subproject will also develop supervisory controllers for the optimal power management in power plants that integrate VRFBs. Systems based on hydraulic machines an RFB will be especially studied because of the expected interesting synergies between these elements. Specifically, the RFB can enlarge the efficiency and useful life of the hydraulic turbine.

The subproject will contribute to the VRFBs performance improvement and that of the renewable generation plants that integrate them, what will impact positively the penetration of sustainable energy sources. It will help determining the VRFB limits and their most suitable application conditions and finally, it will advance the technological readiness of VRFB, giving to the productive sector the necessary knowledge for their production and commercialization.