Improving optimal control of grid-connected lithium-ion batteries through more accurate battery and degradation modelling
For researchers and investors in grid-connected battery storage, this work demonstrates that simplistic models significantly underestimate the business case, providing a more accurate framework for techno-economic assessments.
This paper quantifies the annual benefits of grid-connected batteries using realistic physical dynamics and nonlinear electrochemical degradation, showing that a sophisticated model improves profit by 175% over the simplest model and reduces RMS capacity prediction error from 11% to 5%.
The increased deployment of intermittent renewable energy generators opens up opportunities for grid-connected energy storage. Batteries offer significant flexibility but are relatively expensive at present. Battery lifetime is a key factor in the business case, and it depends on usage, but most techno-economic analyses do not account for this. For the first time, this paper quantifies the annual benefits of grid-connected batteries including realistic physical dynamics and nonlinear electrochemical degradation. Three lithium-ion battery models of increasing realism are formulated, and the predicted degradation of each is compared with a large-scale experimental degradation data set (Mat4Bat). A respective improvement in RMS capacity prediction error from 11\% to 5\% is found by increasing the model accuracy. The three models are then used within an optimal control algorithm to perform price arbitrage over one year, including degradation. Results show that the revenue can be increased substantially while degradation can be reduced by using more realistic models. The estimated best case profit using a sophisticated model is a 175% improvement compared with the simplest model. This illustrates that using a simplistic battery model in a techno-economic assessment of grid-connected batteries might substantially underestimate the business case and lead to erroneous conclusions.