*Brief Review*

Until now, the trends of BESS have been widely studied in several aspects. As explained in Table 4, a BESS is often applied to solve microgrid, grid-scale, and hybrid renewable energy system (HRES) problems. However, to obtain economical results, its sizing and siting was optimally analyzed with a significant dependence on the problem

to be solved. BESS is usually used to solve problems related to system flexibility, such as demand load shifting, loss of load, avoidance of RES curtailment, and RES peak shaving. As its research progresses, it becomes increasingly important to consider the impact on battery health, as well as the choice of battery technology used, which can affect the system and its economic value. Battery health needs to be considered to ensure it does not experience degradation, when the BESS needs to be replaced. In general, the battery degradation factors considered during the optimization process are SOC, DOD, cycle number, and battery lifetime. Furthermore, studies have also been developed on the use of recycled batteries from electric vehicles with BESS integrated into the microgrid system. Research on the effect of temperature on the optimization of BESS was also considered recently. The temperature factor that affects BESS consists of operating temperature and ambient temperature. However, little research has been carried out on the effect of BESS environmental temperature optimization. Yuhan Wu et al. [31] conducted research on optimizing BESS considering the ambient temperature. However, in this research the temperature variable was not explained in sufficient detail.

**Table 4.** Review of a recently published article on BESS optimization.



This review provides a discussion about the expansion planning with BESS optimization by considering battery degradation due to ambient temperature to fill in the research gaps. Figure 3 shows the mind map of BESS relating to the application, batteries energy storage technologies, battery degradation, objective function, design constraints, optimization algorithms, and challenges used in this review.

**Figure 3.** Mind map of BESS optimization.

#### **3. Expansion Planning Overview**

A combination of BESS technology and expansion planning is frequently adopted to overcome the issues of VRE integration. For example, generation expansion planning (GEP) tries to meet energy demands alongside several economic and technological restrictions. It determines the generating capacity of an ideal investment plan during a specific study period. Governments and decision-makers routinely utilize GEPs to select when and where to invest in generating technologies. Based on the decision factors, energy expansion approaches are broadly classified as GEP and transmission expansion planning (TEP). However, storage expansion planning (SEP) is widely used when dealing with BESS investment choices. In reality, creating, transmitting, and storing processes tend to be synchronized [5].

The main challenge of GEP is determining the appropriate capacity size, generating unit, and timing of a new facility's building to fulfill the electric power requirement, at least during the planning period. GEP models are made more versatile by considering numerous goal functions and constraints as shown in Table 5. Its goal function typically consists of two major components, namely, investment and operation. To establish an optimal GEP strategy, different restrictions that impact the execution of the plan must be considered. There are two types of constraints, namely required and discretionary. One of the relevant limitations is ensuring the balance of electricity demand. Therefore, there is a possibility that minimizing total expenditures for a GEP project is not an effective target function, especially if there are other fascinating aspects that compete for attention. Consequently, issues related to GEP are frequently posed as a multi-objective optimization process. This approach can handle the simultaneous compromising of multiple goal-planning functions to determine which alternative capacity is the most effective. Several of these goals are intertwined, such as incorporating DSM and RES in the generating mix, reducing pollution, reliability, fuel consumption, costs associated with the intermittent nature of RES, and the risk of fluctuations in energy expenditure. All these are carried out to improve the flexibility of the GEP model [40–42].


**Table 5.** Generic objective function, constraint, and uncertatnties in GEP [40–42].

*Batteries* **2022**, *8*, 290


**Table 5.** *Cont.*

SEP can be categorized by its storage capacity, geographical distribution, and mobility, in addition to the kind and quantity of BESS. Furthermore, energy storage systems are classified as either short or long-term, depending on their capacity. Short-term appliances, such as capacitors, flywheels, compressed air energy techniques, and BESS, stores energy from seconds to days. Certain long-term appliances, such as hydrogen storage and water reservoirs, can supply energy from one week to an entire season. BESS can also be classified as centralized or dispersed. When categorized by centralized, it refers to a single place. Even though most BESS are either centralized or dispersed, BESS can categorized by mobility such as on electric vehicles (EVs) [5].

The primary goal of decoupling is to ensure that cost-cutting initiatives are carried out by central planners (vertically integrated electrical firms) or politicians, as opposed to private investors. In the SEP model, reliability indices account for expected energy not served (EENS) or loss of load probability or expectation (LOLP/LOLE). There is also a possibility of adding any necessary technical constraints for unit commitment (UC) that are essential for scheduling the operation of the producing sector. These include minimal timeframes between turning on and off, beginning and shutting down, ramping up and down, as well as the least power outputs. There is a possibility that further operational reserve limits, such as the spinning types, alongside frequency and voltage support replacements, are imposed on the way the system operates [5].

#### **4. BESS Application Overview**

BESS delivers various services to network operators, DG plants, energy retailers, and consumers. Figure 4 categorizes its applications in in the grid based time scale. Additionally, BESS consumption is classified in accordance with the time scale of its deployment, which ranges from milliseconds to hours. Its applications in grids or microgrids tend to improve power quality, voltage management, peak shaving, load smoothing, frequency control, and energy arbitrage [43].

**Time Scale**

**Figure 4.** Application of BESS based on time scale [43,44].
