**2. Methodology**

The papers covered in this paper are partly part of the research during the preparation of the doctoral dissertation, and partly during the research phase of the project mentioned in the acknowledgment. Given the constant changes in technology, the criteria for including and excluding processed papers required fine-tuning throughout the research process. What is common to all the presented works is that the team of development experts, to a greater or lesser extent, managed to replicate the outcomes at the level of the model and the prototype solution. This was also a key criterion for the processing of individual work—high reliability that the proposed solution can be refined and developed for the needs of the actual system.

For the purpose of collecting valid information, journals indexed in the leading databases Web of Science, Scopus and Inspec were consulted. The advanced search considered more than 7000 papers. This huge amount of information has been reduced to a little over 1000 basic papers by thorough processing according to the thematic criteria

and observed challenges. Additionally, by the criteria of the number of mentions and the context in which they were mentioned, and a preliminary reading of a team of researchers, the selection was reduced to around 250 relevant papers. The additional thematic division and organization of a wider team of researchers quickly prototyped the relevant group and the choice remained on those papers mentioned in this paper. If the reader considers that some paper has been unfairly neglected, the authors are open to contact and correction in future papers.

Aware of the fact that the impossibility of repeating the outcome of a single paper does not immediately imply the unacceptability of the observed solution for processing in this paper, we consulted external domain experts. The covered domains are the field of artificial and computational intelligence, distribution network optimization techniques and development of monitoring and control solutions for distribution power networks. Such an interdisciplinary team provided a multi-perspective review of the observed solutions that was in some cases crucial to understanding what was presented.

Although researchers were given the opportunity to independently assess and organize individual development teams, it turned out that regular synchronization meetings could achieve more results in less time. By dynamically adapting the research team according to Agile principles [10,11], a larger amount of methods than originally conceived was processed.

The risk of bias and false applicability estimation was solved by following the next procedure:


Conclusions after the comprehensive review of papers are that there are scientific databases in which papers are generally published with more detail with traceable results, while at the same time there are scientific databases that nurture papers in a shorter reporting form which makes repeating the outcomes more challenging. For some papers, mathematical experts with deep knowledge of statistics had to be consulted, and for the purposes of mathematical synthesis, several mathematical experts were engaged.

The limitations of this methodology are certainly relying on a group of researchers without using any of the artificial intelligence (AI) methods. However, the authors are of the opinion that, given the specificity of the observed challenge and previous experiences, there is greater reliability if we are guided by the opinions of domain experts and long-term scientists, than by the algorithm. In the case of considering many scientific papers for development purposes, it is more reasonable to rely on natural intelligence.

Of course, it may seem that this is completely contradictory to the topic of this paper, but it should be considered that this paper observes applications of CI, a subset of AI that always relies on exact mathematical calculations and it is in fact specifically modeled by natural intelligence.

Within the space–time possibilities and with the available literature, this paper aims to shorten the cognitive process and provide an overview in one place for all those who are just starting or are currently dealing with the field of distribution network optimization using CI methods. For better understanding, the abbreviation list is provided in the Appendix A at the end of this paper. Of course, given the context in which this research is conducted, all papers are viewed from the point of view that they must be feasible as actual optimization systems or at least as a basis for the development of future managemen<sup>t</sup> procedures. With this work, science is viewed as a platform for launching innovative engineering solutions that should serve all of humanity.

#### **3. DG and the Distribution Network**

DG impact on the power grid is a complex mathematical problem, changing over time and depending on the parameters of each grid. Precisely because of the diversity of the system in which DG is integrated, there is no universal solution that can be magically applied to every case while respecting the laws of calculation such as real power balance equations, Jacobian Matrices and Newton—Raphson Power flow.

Current growing demand for energy in the world accompanied by an increase in energy prices, accelerated the technology development for RES utilization, which introduce additional variability. According to installed capacity, Viral et al. [12] classify RES and non-RES DG, such as micro DG with installed capacity up to 5 kW, small DG with installed capacity up to 5 MW, medium DG with installed capacity up to 50 MW and large DG with installed capacity from 50 to 300 MW. The same authors refer to the utilization of DG for the base and the peak load demand and capability to provide ancillary services to the system, while considering a power plant a DG only when it is connected to the distribution network. Similar findings on different types of DG technology and power can be found in Reference [13] in which the authors also refer to Viral et al. [12]. Sambaiah's [13] considerations of the method of optimal allocation are given in later sections. Although Reference [13] can be considered similar in its content, this paper is about reconstructed models and tested systems and is not a mere enumeration of what was read, but about presented scientific papers that the development team successfully reconstructed according to the guidelines for software development.

Considering integration location, DG is integrated in two ways: local level and endpoint level [12]. Local DG means independent production units in the distribution network, and the end-point DG is a production unit integrated with the consumer. According to power system management, DG based on technologies independent of the energy resource incidence are fundamental generation while solar and wind power plants with volatile production cannot be considered as fundamental generation in power system [14,15]. However, such systems can be enriched with an energy storage and in that case increase the efficiency of the distribution network and enable the balancing of part or all of the system [16].

When modeling and developing a specific CI method for ADN management, there is a big difference depending on which type of DG integration CI needs to be observed. Aggregating end-point production at the local level has proven to be a good entry point for a challenge formulation that CI can solve more successfully.

Technical advantages of DG can relate to a wide range of influences such as the power supply of the system peak load, voltage profile improvement, system losses reduction, power supply continuity, system reliability improvement and elimination of power supply quality issues [17–19].

Voltage dips are still a challenge in many distribution grids so Ipinnimo et al. [20] point on mutual coordination of a large number of DG units in order to reduce the voltage dips. Location and power of DG are two key factors in reducing system losses [12]. Voltage dips reduction and transient voltage reductions between 10% and 90% of the nominal effective root-mean square value lasting between half cycle and one minute [9] are recognized as a key issue in distribution network with high DG penetration level, as can be seen in References [20,21]. However, there are specific circumstances in which an optimization algorithm needs to be adapted, such as a Long Distribution environment that includes manufacturing, distribution centers, terminals, geographic units, suppliers and end users [22]. Review paper of Djafar et al. hits a very specific niche and it is important to emphasize it because it can be useful to those who are in a similar environment of complex power distribution systems, without being discouraged that perhaps such a case has not been processed in the scientific literature.

Common challenges of DG integration to distribution grid mentioned by the authors of References [21,23–27] are impossibility of reactive power production of some DG technologies, necessity of power system protection settings change, possible occurrence of DG island operation, high order harmonic generation of some DG technologies, influence on power system stability, possible excessive voltage increase, short-circuit currents increase. While DG strengthens the distribution grid resulting in number of dips decreasing, the transmission network may weaken as described in Reference [28] and enforcement of distribution grid is necessary.

DG units have a significant role in European objectives, as presented in Reference [29], where the possibility of control and connection point is described with centralization and decentralization trends in European area. Moreover, Reference [29] gives The Smart Grid Architecture Model (SGAM) Framework that is to be respected in order to address interoperability in a way described in Reference [30]. Concise description of the SGAM Framework is given by Panda and Das [31], who address the still open questions of the SGAM and provide an insight of the Smart Grid environment in the year 2050.

## *3.1. SGAM Framework*

SGAM Framework introduces six functional zones for specific purposes and functionalities, respecting user requirements and possibilities. First zone, Process, implies equipment that participates in energy conversion and transmission. Field zone, the second one, implies monitoring and protection equipment responsible for data acquisition. Aggregation level is in third zone, Station, in which substation automation supervises the first two layers. ADN managemen<sup>t</sup> processed by this paper can be part of third zone, or, even better, part of fourth zone, called Operation.

Operation handles multiple Energy managemen<sup>t</sup> systems (EMS), Distribution Managemen<sup>t</sup> Systems (DMS), multiple microgrids, aggregation of various type RES into Virtual Power Plants (VPP) and electric vehicles (EV). The fifth zone, Enterprise, includes utilities, service providers, energy traders, customer relationships, asset managemen<sup>t</sup> and procurement [29]. Market operations are in the sixth zone, Market, where energy trading and mass market occurs.

SGAM Framework also proposes the methodology which was used for this research to examine the validity and applicability of multiple CI methods in real-world power systems. The methodology within this research has seven crucial principles of validation:


framework is needed to clearly understand the level in the topology in which any solution can be applied to.


By combining Station and Operation zones with Communication, Information and Function layers in SGAM, it is possible to reach an enclosure in which there is an exceptional need to apply methods presented in this paper.

As mentioned above, in bullet 4, the good starting point for integration of CI-based methods for ADN managemen<sup>t</sup> is Station or Operation zone in distribution system, DER and Customer environment. The bounding section for CI applications in ADN is given by Figure 1, where the application zone observed by this paper is shaded in red.

**Figure 1.** SGAM reference architecture with emphasis of CI applications observed by this paper.

Process and Field zones are most often oriented to smaller systems that can be explicitly mathematically described and that depend on a finite set of known parameters. Such systems are micro-grids of a local character or even small micro-grids at the end-point [33,34].

Large centralized generation connected to the transmission network is a separate category by itself, regardless of the zone of application. The laws of system managemen<sup>t</sup> at these levels are completely different from the established methodologies of managing the distribution system, as can be understood from comprehensive overview given in Reference [35]. Interconnected power grids are multi-layered system where CI can be useful as a distributed optimization platform that acts as one part of the overall algorithm. This concept is explained in a little more detail later in this paper when one of the most applicable optimization methods is described.

Enterprise and Market zones can benefit from the solutions defined in the Station and Operation zones because such security of the right solutions will enable a different market presence and even develop new market possibilities, most often consequently with greater RES integration [36].

Considering that this paper is the result of the project that respects the SGAM architecture and according to the basic principles of the described methodology, it is possible to imagine the trend of developing inventive solutions for the Smart Grid environment. Such inventive solutions must by their nature react quickly enough to be taken into account and precisely enough for their application to come to life at all.

#### *3.2. DG Impact in ADN*

When considering CI methods for the application in system solutions complied with SGAM Framework, the properties of DG and the background mathematical models of each observed technology should be considered. The mathematical models of DG and the mathematical background of the power system represent a system of complex equations that are not easily solvable by conventional methods. A clear and comprehensive overview of the essential characteristics of different DG allocation methodologies types is given in Table 1.


#### **Table 1.** Comparison of main content of selected papers.


**Table 1.** *Cont.*

It is necessary to consider the fact that the data from the paper [12] published in 2012 have been changed today in the field of the installation cost. Furthermore, distributed generation allows reactive compensation control voltage level, can contribute to the regulation of frequency and can be used as spinning reserve during faults if the technology of production units permits [12,25–27,39,42,48,49].

Beside the technical advantages of the distributed generation, the authors of Reference [12] also provide an additional, indirect economic benefits expressed through the possibility of delaying investment in power system components and reductions of different power system managemen<sup>t</sup> costs, while papers [12,48] refer reducing investment cost for the network equipment upgrade, reducing the operating costs of transmission and distribution systems, increasing the security of supply at critical loads, reducing the cost of mandatory reserves in the system, etc. The benefits of integrating DG units are reduction of losses, improvement of voltage profile and voltage stability, increased quality of electricity

and efficiency of energy use, but direct current flows certainly have a negative impact on the reliability of the system [13].

Distributed generation in a whole with a consumption becomes a micro grid, while more micro grids make an active distribution network [50,51]. It can be concluded that DG change the technical properties of distribution network making this network an active distribution network—a precondition for development of Smart Grid where the distribution network has the ability to supply the consumers from distribution generation and upper voltage level network together while maintaining an optimal operational [50].

Distributed generation is the most useful when placed as close to consumers as possible to ensure the greatest impact on DG benefits. A terminal in the distribution network where DG has the greatest beneficial impact is called an optimal location. Selection of the optimal location is crucial for the planning of the distributed generation.

#### **4. Optimal DG Planning and Scheduling**

ADN Management is a real-time and operational planning problem when it is following functional criteria. Solutions for allocation problem described in the technical and scientific literature and papers can be used as scheduling problem if functionality, modeling constraints and performance rules are obliged. That interest requires the identification of papers which present solutions that enable development of a potentially new method for DG operational managemen<sup>t</sup> planning in Smart Grid. Optimal ADN managemen<sup>t</sup> with an increased share of RES-based and non-RES-based DG technologies is a modern challenge for the distribution system operators since most of the current distribution grids are not designed for the new, bidirectional operating conditions. ADN operational planning represents the necessary precondition to ensure the most favorable effects of distributed generation in Smart Grid environment [12,48,52]. Proper operations in Smart Grid manage power flows in a most efficient and reliable manner, but require integration of multiple technologies described in Reference [53].
