Forecasting Battery Energy Storage System Capacity

Accurately forecasting the degradation of lithium-ion batteries is of critical importance for the successful deployment and operation of grid-scale energy storage systems. As the adoption of renewable energy sources continues to grow, the role of energy storage in stabilizing the grid and providing load-balancing capabilities becomes increasingly vital. Lithium-ion cells have emerged as a leading technology for grid-scale storage due to their high energy density, fast response times, and declining costs. However, to effectively utilise lithium-ion cells in Battery Energy Storage System (BESS), the degradation of the cell’s chemistry over time must be carefully managed as it presents a significant impact on the long-term viability and performance of such assets.

From a due diligence perspective, prior to the construction of a BESS, having a reliable model to forecast battery degradation is essential. This model can help project the lifetime of the battery system, informing critical investment decisions and risk assessments. By understanding the expected rate of capacity fade over the lifetime, developers and project stakeholders can make more informed choices regarding the chosen supplier, system architecture, and operational parameters. Additionally, equipped with such information, performance guaranties can be appropriately sized, and business models can be validated.

Furthermore, from an asset management perspective, a robust battery degradation forecasting model is invaluable during the operational phase of a BESS site. By continuously monitoring the performance of the battery system and comparing it to the predicted degradation profile, operators can make data-driven decisions regarding capacity augmentation strategies, and support potential warranty claims. From a practical perspective, the ability to forecast BESS degradation is crucial as it could support the implementation of predictive maintenance, ensuring proactive battery management, reducing unexpected downtime, and leading to improved system reliability.

Considering the importance of capacity degradation, Greensolver developed its forecasting model to support its BESS projects. Focusing on LFP, the most popular lithium chemistries for energy storage applications, Greensolver’s model was internally tested against several systems and achieved an With the aid of this forecasting model, Greensolver provides greater value to its project partners by optimizing project contracts to maximize the potential of energy storage assets.

Greensolver’s model – Forecasting BESS Capacity and State of Health

Therefore, in summary, the development and implementation of degradation forecasting models were found to be crucial for the successful deployment and operation of grid-scale energy storage systems. Such a model was found to inform critical investment decisions, optimise technical designs, and support data-driven asset management strategies – contributing to the long-term viability of energy storage assets.

We invite you to contact the Asset Management Team for more information: contact@greensolver.net

Written by the Asset Management Team, Nikko Talplacido, David Roissé