**1. Introduction**

Smart Grid research is the new, challenging area with grea<sup>t</sup> interest of research teams in the EU space [1,2]. Future advanced and smarter power network relies heavily on the possibility of independent permanence and self-sufficiency based on data and informed managemen<sup>t</sup> [3,4]. The development of the power network at the distribution level and the increasing integration of renewable energy sources (RES) makes the network heterogeneous and more diverse. The laws of network managemen<sup>t</sup> that were once applicable to the whole system become less valid in the era of changing power system and novel paradigms. Such system requires advanced methods of rapid analysis of a multitude of possible scenarios to achieve optimal power system management. This paper examines the scientific and engineering foundations of using computational intelligence (CI) methods for achieving optimal real-time or quasi-real time managemen<sup>t</sup> of Active Distribution Network (ADN). ADN is the first stage in the formation of more advanced Smart Grid which involves usual power distribution equipment along with information and communication technologies (ICT) and monitoring systems. The goal of Smart Grid is more reliable power supply and adequate power quality (PQ) in the ever-changing environment. The presented methods are currently oblivious to information technology and are focusing on electrical engineering principles since those are the foundations for every other application. Objectives observed

**Citation:** Vukobratovi´c, M.; Mari´c, P.; Horvat, G.; Balki´c, Z.; Suˇci´c, S. A Survey on Computational Intelligence Applications in Distribution Network Optimization. *Electronics* **2021**, *10*, 1247. https:// doi.org/10.3390/electronics10111247

Academic Editor: Osvaldo Gervasi

Received: 1 May 2021 Accepted: 20 May 2021 Published: 24 May 2021

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by this paper are energy balancing, losses reduction, reliability increase and policy definitions described in latest scientific achievements. Energy balancing is the most important part of Smart Grid and it can be a topic for itself, especially when market considerations become equally important to technical ones.

Although most scientific papers presented here focused on optimal Distributed Generation (DG) allocation and sizing in distribution network, objectives achieved by papers presented in this work can be used as an engineering foundation for ADN operational managemen<sup>t</sup> based on advanced CI optimization algorithms. The scope covered by Distribution Network Optimization is much larger than just the allocation of DG units and includes optimization of the topology and timetable planning of existing power plants to achieve optimal power flows [5,6]. Accordingly, this paper includes the papers that in a concise and precise way define the application of CI methods in solving modern challenges in the power system, especially the problem of utilization of a larger number of DG units. Real-time power flow optimization, as described Reference [5], comes only after a realworld software solution is set up at the planning level. This paper defines the principles that such a software solution should meet and describes the leading CI algorithms that can result in such a software solution. Subsequently, a new part of such a solution may be real-time optimization module for optimization of power flows, voltage profile and oscillation damping control, as given in Reference [5]. For such real-time power flow optimization with limited infrastructure for data acquisition in the distribution grid, a robust state estimation engine is crucial [6] to address uncertainties in the distribution network.

Scientific works observed by this paper can lead to the development of ADN managemen<sup>t</sup> solutions and the current activities of the authors confirm that hypothesis. Assumptions of ADN operational managemen<sup>t</sup> by CI is unequivocally and unanimously highlighted and validated by papers [7–9]. The challenges ahead in the field of Big Data collection and consumption monitoring using advanced metering infrastructure are certainly part of the development of such ADN managemen<sup>t</sup> solutions [6].

The rest of the paper is structured as follows: Section 2 provides the overview of the used methodology, sources of information, knowledge collection, synthesis and solution assessment criteria. Section 3 analyzes the scope of DG impact in the distribution network and what are physical constraints that need to be respected when evaluating applicability of any CI method. Section 3 presents most important papers that use CI for optimal allocation and optimal scheduling of DG units, while considering the physical and technical limitations of the considered ADN system. Selected papers are the result of described methodology and selection criteria. Moreover, by the opinion of the authors of this paper, selected papers can be utilized in market-oriented planning and scheduling scenarios in real-world systems nowadays. Section 4 describes papers that bring significant breakthroughs in the operational managemen<sup>t</sup> of the ADN with multiple DG units. Finally, the conclusion outlines observations and knowledge gathered by this paper.
