ESSs are referred to using various terms depending on the nation and the application. In its technology standards and guidelines for electrical equipment, Japan refers to electromechanical devices that store electricity as "electrical storage systems." The corresponding word in the US is "rechargeable energy storage systems," which is described in its National Electrical Code (NEC)[1].

 
 [2]Degradation is not considered in the majority of power systems optimization problems, which makes their utilization impractical. Determining the ESSs' power and energy capacity without taking battery degradation into account can result in overstated profits and significantly decreased reliability. The application of precise models that account for battery aging factors reveals that the expected degradation rate in stationary systems of use, such as RES-based power systems, EV2G, is multiple times higher than that which is indicated by the manufacturers. A method for using batteries in these applications can be developed using aging prediction models, lowering the load on the batteries in a number of different ways. The non-linear nature of degradation, which is particularly evident in the second service cycle, which is characterized by an accelerated process of degradation, and cross-dependence of aging parameters make it challenging to determine the battery's actual capacity and project its service life. Stress conditions also affect different types of lithium-ion batteries in different ways. The creation of a precise battery deterioration model for the micro grid application is made more difficult by all of this. The majority of current models employ a streamlined method for cycling the battery, but because of stochastic generation and load, the batteries may be vulnerable to high rates of degradation brought on by repeated charging cycles.

Batteries are vulnerable to deep discharge in energy systems that rely heavily on renewable energy sources, and they are also vulnerable to strong impulse currents and frequent short cycles when frequency regulation is included. Many aging models include temperature as an unchanging aging factor, yet this element has a significant impact on the battery's degeneration in stationary systems as well as the depth of charge. Performance-degradation models, like semi-empirical models, are largely used to estimate the BESS service life. However, the quality and quantity of data used in the creation of such models has a significant impact on their accuracy. The data-driven models are more accurate and show greater promise, but using them is challenging due to the size of the training sample. The performance of these models has also declined due to the difficulties in acquiring prolonged data covering a variety of operating scenarios, including those involving various types of electrochemical batteries. However, independent laboratory testing cost a lot of money and take a lot of time. Therefore, the use of virtual models, such as a digital twin and a pseudo-two-dimensional (P2D) model, enables the simulation of different operating circumstances for real-world batteries in the form of a virtual physical system and offers enough information to enhance DDM.

 

References

https://cleantechnica.com/files/2020/01/Volkswagen-second-use-energy-storrage-system.jpg