*4.3. Promising Intelligence Search Methods*

Promising intelligence search methods are the additional optimization algorithms developed to effectively solve distributed generation optimization problems. Some of these methods are as stated [2,57,59].

#### 4.3.1. Artificial Bee Colony Algorithm

The artificial bee colony (ABC) algorithm was developed from the searching behaviour of a swarm of honeybees. Khasanov et al. [16] proposed an application of hybrid teachinglearning and artificial bee colony (TLABC) technique for determining the optimal allocation of PV-based distributed generation and battery energy storage units in a distribution system with the aim of minimising the total power losses. ABC algorithms are applied in Mohandas et al. [92] and Dixit et al. [93] to find optimal DGs locations and sizes with the objective of minimising power losses and of improving voltage stability of the network. In Abu-Mouti and El-Hawary [94], the authors proposed an algorithm of ABC to adjust the control inputs, iteration number and colony size in the DG allocation optimization. El-Zonkoly and Kefayat et al. [95,96] utilized ABC algorithms to solve distribution expansion planning problems and to obtain optimal reinforcement and commitment scheduling for PVDG allocation. Padma Lalitha et al. [97] presented and compared the ABC and PSO algorithms. The authors observed that the ABC algorithm outperformed PSO, having better solutions and convergence. Notwithstanding, the works discussed here do not provide indices to evaluate harmonic contents and dynamic stabilities of the systems.

#### 4.3.2. Ant Colony Algorithm

The ant colony (AC) algorithm is adapted from ants' social behaviours in searching for the shortest route to obtain food. The AC algorithm process begins with random solutions obtained from the ants' random searches in their movements. Ants share information about their movements by leaving chromosome trails behind during their movements. Consequently, a path with trail density becomes the shorter path. This knowledge is utilized in the optimization search to obtain feasible solutions [57]. The advantages of AC algorithms are the ability to discover good solutions and guarantee convergence and the ability to search among a population simultaneously and adapt to changes such as new distances. However, AC optimization algorithms are weak in changing probability distribution, uncertainty of convergence time, sequences of random decisions and theoretical analysis, since they are highly experimental researches. These algorithms are variously used in

the literature for optimal allocation of DGs [6,56]. In Gomez et al. [98], Vlachogiannis et al. [99], Wang and Singh [100] and Amohadi and Fotuhi-Firuzabad [101], the variant of AC and ant colony system (ACS) algorithms were presented. They found optimal sizes of DGs, locations of DGs and re-closers in the radial DNs with an objective to use the composite reliability index. Transient stability and reliability of the distribution systems were evaluated to validate the proposed methods. ACS algorithms were observed to be more satisfactory in many engineering applications. However, these works did not include the installation of renewable DGs and could not access the impacts of integrating BESS/PVdistributed generations on the harmonic distortion and oscillation of the networks.

#### 4.3.3. Artificial Immune System Algorithm

The artificial immune system (AIS) algorithm is adapted from immunology, the importance of the immune system and their values in the natural world [102]. The immune system is an indispensable defence against self-approach to protect human health from pathogens such as viruses and microbes. The procedure differentiates between self-cells and non-self-cells. Thereafter, the immune system effects immune actions to destroy the non-self-cells [103–105]. To apply the AIS optimization process in solving DG allocation problems, the instructions in the search area (objective functions, design variables, constraints, etc.) are encrypted in an antigen population of an AIS algorithm. AIS algorithms are proposed in Aghaebrahimi et al. [106] and Hatata et al. [107] to find the optimal locations and sizes of the DGs, with the objective to minimise the power losses of the DN considering bus voltage limits and line current. Souza et al. [108] proposed an AIS algorithm in expansion planning to allocate DG units into distribution network considering the uncertainty of load demands.

#### *4.4. Probable Hybrid Intelligence Search Methods*

Hybrid optimization methods are a useful combination or collaboration of more than one different intelligence search method. These approaches extract the benefits of the component methods to obtain an optimum solution for a specific planning problem. The allocation expansion planning of BESS/PVDGs problems is multi-objective in nature. Hence, applying a hybrid method in their investigation begets an excellent planning objective and a suitable alternative algorithm to solve the problems that involve better understanding of the methods.

A summary of the various optimization techniques that are developed and applied by the researchers for BESS/PVDGs allocation is presented in Table 2.


**Table 2.** Summary of optimization methods.



#### *4.5. Commercial Software Applications for Allocation of (BESS/PV) Hybrid DG Systems*

Several software applications have been developed and applied for the sizing of hybrid renewable energy systems (HRESs) such as HOMER [109–111], HYBRIDS [112], HYBRID 2 [113], RET Screen [114], TRNSYS [115] and IHOA [116].

Comparatively, HOMER has a significant application in optimal sizing of HRESs because of its capacity to quickly obtain optimal sizes of energy systems. In addition, it is useful in investigating sensitivity analyses of some uncertainty parameters and changing factors related to the HRESs. However, the mentioned software tools are incapacitated to investigate major network system issues related to the integration of distributed HRESs (DHRESs) such as harmonics and small signal and transient stabilities. A list of commercially available software for the planning of HRES is presented in Table 3.

**Table 3.** Software applications for optimizing BESS/PVDGs.


#### **5. Results and Discussion**

The increasing needs for energy and the resultant environmental issues arising from fossil energy utilization have encouraged the extensive study of renewable energy technologies in place of traditional fossil fuels. Precisely, hybrid distributed generations, which have been described as a collaboration of renewable energies and support systems, are a significant alternative to confront the concerns over sustainability of energy demands and environmental safety. The planning and optimization of hybrid distributed power systems can meet the essential requirements of a geographical location in terms of availability of

potential energy resources, area topography and various kinds of energy demands. Consequently, the optimal allocation of renewable energy sources and storage systems relating to environmentally friendly hybrid distributed systems considerably improves the technical and economic aspects of the power supply system. The addition of storage technologies in the allocation of distributed generations can smoothen output power and reduce REHDG intermittent effects in the network. Including storage devices in the DGs allocation problems provides supporting services to the optimal solutions by eliminating the effects of intermittency in the renewable sources power output. Several allocation methodologies have been proposed to determine the best hybrid renewable energy system with respect to the economy and technology. Determining the optimal allocation of hybrid battery storage and PV-distributed generation systems and other hybrid renewable energy systems is important to increase the technical and economic efficiency of the power distribution system and to encourage the extensive use of environmentally friendly resources.

Various allocation methodologies presented in the recent literature with different optimization algorithms are reviewed here. The GA, PSO, SA and AIS are some of the feasible artificial intelligence algorithms used to investigate the planning and optimization of DG allocation problems. The most important benefit of GAs are the ordered capability to find the global optimal and the ease of achieving a local minimum when used in hybrid system allocation. Another advantage that makes GA suitable for allocation planning studies is code-ability because it is not accessible in other methods such as PSO. For instance, when at most three parameters are to be coded such as in a wind/PV/BESS system, both GA and PSO can perform effectively. However, when more than three elements are involved, only the GA method would be more capable of obtaining optimal solutions. Some other times, PSO has some advantages over GA, although both are very effective in utilizing the same repeatable search approach. Moreover, employing SA in hybrid distributed systems is not as common as GA and PSO methods, but presently, SA is generating more research interest in some approved areas of application. The ACS algorithms have been presented to reduce power losses and to improve power system factors of a radial distributed system. Similar to GA, the AIS optimization algorithm has "collection" and "transformation" operatives which improve the probability of the algorithm to find the global optimum point.

AIS is bound to have a high application in sizing studies because it is similar to GA and can be effective in finding the global optimum in difficult problems. However, GA has greater application than AIS, especially in addressing a large number of parameters. In addition, conventional methods such as LP, MILP and NLP are still being applied in existing studies to detail the features of any physical system into a mathematical model formulation. Often, hybrid optimization methods are applied by combining two or more methods to take beneficial advantage of them in terms of their convergence time during the optimization process. Hybrid methods are characterized due to their dynamic flexibility during the allocation process. Hence, they are the most applied allocation methods.

The intermittent nature of photovoltaic and wind output power and the high voltage rise and fall from BESS cause harmonic distortions which have a negative impact on the power quality, reliability and stability of the distribution networks. The majority of the current works do not include the uncertainties of the renewable and battery storage power sources in their formulation models. They did not combine all the associated investment, technical, safety, DG capacity, network stability, power quality and reliability constraints in the formulation models for the DG allocation problems. In most of these works, the minimum harmonic level and dynamic stability of the network are not constrained but are only assumed, while the constraints for the right of way are neglected for the required buses. All these necessary and associated constraints need to be incorporated to obtain a practical solution from the REHDG allocation models. In essence, future research studies should give adequate consideration to modelling of the impacts of renewable energy intermittencies and the resulting variable output power to culminate in more feasible solutions to BESS/PVDG optimization problems.

In addition, the operations of hybrid DG systems are dynamic. Hence, the planning and design of optimal sizes and placement of RERDGs should be optimized on dynamic networks but not on static ones, as they are mostly performed in the existing planning models. The dynamical issues such as harmonic and system instabilities are very visible while using dynamic networks, since the real power networks are dynamic networks whose load profile periods are estimated hourly during a dynamic planning horizon. Future research needs to focus on the use of dynamic networks to entirely incorporate the intrinsic characteristics of the distribution network such as the harmonic components and dynamic stability of the network.

Moreover, the sizes and locations of battery energy storage, photovoltaic and wind DG units in the distribution network (DN) affect the network harmonic contents by having either positive or negative impacts on the magnitude of the current and voltage harmonics of the networks.

### **6. Conclusions**

This study presents a review of prior research on the optimization methodologies for designing and planning hybrid renewable energy resource distributed generation such as hybrid battery energy storage–photovoltaic DG and other hybrid distributed systems. This paper reviewed more than one hundred papers published by renowned referenced journals on battery energy storage systems and renewable energy resources as well as on robust and efficient optimization methods for solving hybrid DG allocation planning problems. Optimization studies, in the last decade, on DG allocation planning using conventional and intelligence search methods have been analysed, and hybrid optimization algorithms have been presented.

Intelligence search methods have been mostly used in the last decade due to their capacity for shorter computation times, and because they provide better accuracy and have better convergence than the conventional methods. In conclusion, at the beginning, this study investigated a number of research works that have applied optimization methods to solve renewable energy DG allocation problems, including solar, wind and battery energy systems. Many research works use intelligence search methods, most especially GA, PSO and AIS, to solve these allocation problems. Notwithstanding, conventional methods, especially LP and MILP and different configurations of NLP methods are still being used in current studies. In the case of curtailing harmonic distortions of the DNs, which indicate the strength of this study, an optimal planning model is yet to be developed for optimal sizing, placement and timing of renewable DGs and battery energy storage systems. Although, in most cases, the optimal sizing and placement of BESS/REDGs may have attained a minimum cost, the requirements for minimum harmonic levels are yet to be achieved. These requirements are merely presumed in the existing works. Further research is required in this regard to improve the current expansion planning model to obtain optimal allocation of BESS and renewable energy DGs and to constrain the decision variables related to harmonic distortions to a required level. A more comprehensive expansion planning model together with an efficient intelligence search algorithm that has that capability to obtain a global optimum solution is an important approach towards solving optimal BESS/PVDG allocation problems and towards reducing harmonic components of distribution systems during the integration of hybrid battery energy storage systems and photovoltaic DGs.

**Author Contributions:** The mathematical formulations and programming in this work were developed within the framework of the doctorate of A.O. He is supervised by Y.H. and is co-supervised by J.M. The written manuscript was extensively discussed with the supervisors. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data sharing is not applicable to this article.

**Acknowledgments:** The authors would like to acknowledge the research support received from Tshwane University of Technology (TUT), Pretoria, South Africa.

**Conflicts of Interest:** The authors declare no conflict of interest.
