*7.4. Review of Existing Studies BESS*

A state-of-the-art review of BESS optimization considering battery degradation was conducted to discover new perspectives in terms of developing its models. Table 11 summarizes several selected studies that can be distinguished based on main objectives, design constraints, algorithms, and battery degradation factors. It is evident that the perspective of battery degradation in BESS optimization is getting deeper. Its factors vary, such as energy capacity fading, calendar, and cycling aging, battery lifetime, cycle battery, and temperature. The development of the BESS optimization model considering battery degradation due to temperature is an interesting and rare study. There are certain related studies [27,35] in terms of developing a battery degradation model for optimal BESS using a fixed value of battery temperature. Meanwhile, literature [31] tends to develop a degradation battery model due to ambient temperature with dynamic values during the winter. Based on the study of the optimal BESS, ambient temperature affects battery degradation, according to the literature [100] The capacity fade level drops significantly when the perimeter temperature exceeds 35 ◦C. Therefore, the development of a battery degradation model due to ambient temperature is a new perspective in optimizing BESS.

**Table 11.** Literature review of studies of the BESS optimization effect considering battery degradation.


#### **Table 11.** *Cont.*



#### **Table 11.** *Cont.*

CC, capacity constraint, CDC, charging and discharging constraint, PEBC, power and energy balance constraint, PELC, power and energy limit constraint, EC, environmental constraint, RCC, ramping capability, EFC; efficiency losses constraint, FC, financial constraint.

In addition, the battery degradation algorithm needs to be considered. Similar models are generally mathematical, physics-based, data-driven, and hybrid. Algorithm battery degradation affects the speed and convergence of BESS optimization. Therefore, several studies still utilize mathematical algorithm models because they are simple and exhibit rapid performance. However, data-driven models are flexible in modeling battery degradation due to several factors. Examples are piecewise linear approximation, least-squares fitting, and the rainflow-counting algorithm.

#### **8. Issues and Challenge BESS**

In terms of optimizing BESS sizing and location, several factors need to be considered by the expected operating objectives. To reduce the investment cost BESS not only makes it cost-effective. But, can be adjusted to boost reliability, power and voltage quality, peak shaving, load smoothing, frequency control, and energy arbitrage. One of the challenges of BESS optimization is battery degradation. The selection of battery technology is essential and BESS optimization solutions need to be assessed.
