The FULLEST project aims to increase the reliability of batteries for grid balancing and energy security. With the exponential implementation of renewable energy assets foreseen in the coming five years, grid congestion and imbalance will become a daily reality, leading to losses from an economic perspective and creating a barrier to energy security.

Storage assets can act as grid service to overcome these challenges, and the decarbonization strategy of Belgium includes a 6 significant growth in battery parks deployment in the next years for utility support. Although there are many ongoing developments and deployment plans, there is still little knowledge about the long-term operations and performance optimization of these assets.

The FULLEST consortium will develop a digital twin that predicts the performance of batteries and provides insights into the optimal application of utility-scale batteries, which is vital for the Belgian government to confidently roll out the deployment of batteries on a large scale. The optimal operation of utility-scale storage applications is an essential driver in the energy transition, and FULLEST caters to that global objective.

• Objective 1 (O1): Categorization, logging and mapping profiles of anomalies in real-life operations. To construct a matrix for supporting the decision planning in energy systems, with ambition on assisting to future anomaly detection and data standardization in utility scale battery performance.
• Objective 2 (O2): Model for prediction of utility-scale battery lifetime, allowing to consolidate warranty decisions. Thanks to an accurate way to predict battery lifetime, second-life batteries are expected to enter the market more easily since they are often excluded due to warranty constraints.
• Objective 3 (O3): Operational decisions during battery lifetime minimizing downtime and extending the energy storage system's overall effectiveness and lifespan by scheduling proactive maintenance.
• Objective 4 (O4): Identification of the impact of the different grid balancing application profiles on the battery lifetime. By accurately predicting the impact of the different grid balancing services on the battery’s lifetime, asset owners can implement a more efficient battery steering strategy, leading to a better business case for utility-scale battery systems.
• Objective 5 (O5): An economic model for prediction of the battery degradation costs, to be considered in the dimensioning phase of a project.