## 1 Introduction
### Literature Review
The authors in [24] examine a hybrid approach by proposing a coordination technique between PV inverters and battery energy storage systems to solve the voltage rise and dip. The control approach is evaluated on LV rural and urban scenarios, which have different R/X ratios. The droop-based approach is used to control the battery charging and discharging operation. This study shows that in the case of the urban scenario (low R/X ratio), the reactive compensation utilizing the PV inverters is enough to keep the voltage within the acceptable voltage limits. However, for the rural scenario (high R/X ratio), the coordination between PV inverters and BESS is required. On the other hand, the authors in [25] perform a two-step optimization approach to find the optimal size and location of PV and BESS. The first step is to identify the optimal placement of the PV systems that lead to minimum loss, cost, and voltage deviation in the distribution system. The second step is to determine the optimal sites for BESS that result in minimizing the previous objectives. In this study, the optimization problem was solved by GA. For the charging and discharging rates, an analytical method is used through peak load checking. Results indicate that by installing 20 batteries of a lithium-ion power pack battery system manufactured by the Tesla company (assumed to be the battery technology), the peak power losses in significantly minimized and the network voltage is enhanced. In addition, it is critical to re-evaluate the controlling approaches and analysis tools as the distribution networks become more dynamic as a result of continuing structural changes. The complexity and the sheer volume of data make the analysis of power systems using traditional methods difficult. In recent years, intelligent algorithms have experienced observable advancements and such modern approaches are being developed to better tackle engineering issues. Therefore, metaheuristic optimization algorithms, such as Particle Swarm Optimization [26], Genetic Algorithm [22], and Cuckoo Search Optimization (CSO) [27], have been applied for different distribution systems applications. These algorithms have demonstrated their ability to solve engineering problems with near-optimal solutions. The authors in [28], for example, use the Water Cycle Algorithm (WCA) to determine the size and location of the distributed generation and the optimal network reconfiguration. The IEEE 33-bus and IEEE 69-bus test feeders are used to validate the proposed method. Results show the effectiveness of the proposed approach and WCA displays its superiority over other algorithms, such as the Harmony Search Algorithm, Fireworks Algorithm, and CSO. On the other hand, the study in [29] employs the Harris Hawks Optimization (HHO) as the core optimizer of their proposed approach that tackles the reconfiguration problem in modern distribution networks. Results reveal that HHO outperforms other optimization algorithms, namely PSO and CSO. Nevertheless, there is still no solid evidence to choose a specific algorithm. Therefore, this paper introduces the use of a recent optimization algorithm, called the Neural Network Algorithm (NNA) [30]. The NNA utilizes search operators and the distinctive structure of artificial neural networks (ANN) to solve challenging optimization issues. One of the advantages of NNA is that it eliminates the challenge of tuning the initial parameters, which represent the most important obstacle that encounters other metaheuristic algorithms.