4.3.2. PSO-Based Approaches

PSO is a population-based, biologically inspired method with a limited search space, that can repeatedly result in usable optimization results. The intrinsic feature of the method is the re-evaluation of an already past search space. Each possible solution in PSO is called particle and it is described by position, mass and velocity. Particle has its own memory of personal best solution and communicates with the group of particles to examine group's best solutions. PSO initialization implies random particles at random places and with random parameters that are updated with each iteration. Particles follow their own fitness

criteria, called personal best or pbest, but comply with the group's fitness goals, called group best or gbest. Depending on the fitness value, the particle follows the group, or vice versa, as described by Figure 5.

**Figure 5.** Particle following in PSO.

There are cases where particles take the entire population in the wrong direction or where a population converges toward a solution that is not globally best. This most often occurs when the number of particles and parameterization in the population does not correspond to the observed problem. When observing a challenge in a Smart Grid environment, it is possible to easily solve wrong convergence by determining the number and parameters of particles according to the observed electric power system. This means that the number of particles in each generation should at least correspond to the number of observed cases, and their random parameters should be limited to the uniqueness of the search space.

Figure 6 gives a general overview of the PSO method process. At first it seems that the PSO has more steps than GA, but given that PSO evolves around single swarm representation, it shows better performance in most cases. Random swarm generation should in most cases consist of at least same number of particles as there are discrete possible solutions—in case of ADN and DG that number represents number of buses. Each particle has its own velocity, which is encoded as power generation. When a swarm identifies a spot, or a bus in distribution network, in which the DG is most necessary, that becomes a new swarm orientation. Similarly, when a particle finds a most appropriate power is more that becomes the global information. Particles are then updated with the new information and the search space gets explored by the complete swarm.

In Section 3.1 of this paper, the possibility of distributed optimization platform was mentioned for large power systems. Each CI-based solution in an interconnected multilayer power grid can be represented as a single particle of an overall PSO-based power system managemen<sup>t</sup> solution. Of course, at the time of writing this paper, this idea is only a far theoretical consideration, but such were once the ideas and concepts discussed in this paper, and today they are the basis for the development of advanced software solutions. It will certainly be interesting to witness the development of modern power engineering and the growing representation of CI methods in modern solutions.

A novel approach for solving problem of multiple DG units distribution sugges<sup>t</sup> the authors Alrashidi et al. [42] with the PSO algorithm as a basis of their method. The proposed method is confirmed on the model of the distribution network with several feeders and ten nodes where the optimal distribution of DG units have been determined through the multiple scenarios with the power losses reduction objective according to (16).

$$\min P\_{\text{Losses}} = \frac{1}{2} \sum\_{i=1}^{NB} \sum\_{j=1}^{NB} \Re \left\{ y\_{ij} \right\} \left[ \left| V\_i \right|^2 + \left| V\_j \right|^2 - 2 \left| V\_i \right| \left| V\_j \right| \cos \delta\_{ij} \right] \tag{16}$$

**Figure 6.** General PSO process diagram.

The constraints of the algorithm of the authors of Reference [42] define through the voltage constraints, load-flow limitations, size of the transformers and maximum permitted number of DG units. The authors especially emphasize that their method requires consideration of only one DG per node and only time-invariant production and invariant consumers can be. This paper, together with Reference [85], represents a framework of the development of a new method based on the Particle Swarm intelligence algorithm that will solve the problem of multiple DG multiple per node by considering the time-varying feature of loads and RES-based DG units. El-Zonkoly [85] defines the algebraic indicators for the objective function used for multi-objective optimization problem solution. Such indicators in the observed system are the influence of DG on active (ILP) and reactive (ILQ) power, the influence of DG on the voltage conditions, the possibility of DG dispatch in dependence of DG location and consumer location (IC), the influence of DG on short-circuit currents (ISC). For each defined indicator, the author assigns the weight factor σ whose variation opens the possibility to orientate the set of solutions to system managemen<sup>t</sup> solutions or to system development adapted solutions [86–88]. Multi-objective function (MOF) thus becomes the sum of the indicator's multiplications and the corresponding weight factors according to expression (17).

$$\text{MOF} = (\sigma\_1 \cdot \text{ILP} + \sigma\_2 \cdot \text{ILQ} + \sigma\_3 \cdot \text{IC} + \sigma\_4 \cdot \text{IVD} + \sigma\_5 \cdot \text{ISC}) + \text{MVA}\_{\text{sys}(pu)}\tag{17}$$

Such an approach is common in the scientific literature and represents the combination of several individual functions into one objective function. In that way, a modeled optimization problem can be considered both single-objective and multi-objective optimization. Actual multi-objective optimization implies a vector of functions and the existence of opposing solutions.

In the second paper of the same author [88], the problem of the system load peak following is solved by the optimal DG allocation using the method based on the artificial bee colony algorithm, a subset of PSO algorithm. The proposed algorithm addresses the DG distribution by considering the criterion that the most demanding consumers are designated as primary for the DG integration. In this paper, the author considers three DG

units in a system with 45 nodes or three DG units in a 33-node system while the installed DG capacity does not exceed 30% of total system load. A significant advantage of this paper is the consideration of two types of DG technology, one time-varying and unmanageable and other manageable DG. Moreover, the author anticipates the use of electricity storage system to reduce the required commitment of manageable DG.

Authors Gomez-Gonzalez et al. [43] represents an integration of load-flow calculation with an algorithm based on Particle Swarm intelligence to determine the optimal distribution of DG units, as presented by Figure 7. The authors of Reference [43] used the frog-jumping algorithm (18), which belongs to the group of PSO algorithms. Because of its specificities during particle coding, the algorithm is better adapted for the optimization of discrete values.

$$P\_{\omega}^{t} = p\_{\omega, \max} - \frac{(t-1) \cdot (p\_{\omega, \max} - p\_{\omega, \min})}{(t\_{\max} - 1)} t = 1, 2, \dots, \ t\_{\max} \tag{18}$$

**Figure 7.** Simplified illustration of PSO and Power Flow co-simulation by Gomez-Gonzalez et al.

Significant contribution of this paper is a determination of an optimal number of DG units which must result in the ability of the observed system self-sufficiency. This emphasizes the value of this paper compared to previous papers in which the location and the capacity of only one DG unit were considered.

Moradi and Abedini [44] produced a new method based on a synergy of GA and PSO. The authors propose the genetic algorithm for optimal DG location determination and Particle Swarm Optimization technique for adequate DG capacity. This paper wisely used GA's possibility to perform better for integer-based, binary encoded, problems and continuous orientation towards the best solutions specific for the PSO algorithm.

Interestingly, the authors examined three separate cases, one with PSO only, one with GA only, and one with the proposed GA/PSO combination. According to the results presented in their work, the highest value of the objective function was achieved by a separate GA, while PSO and GA/PSO gave equal values of the objective function. However, the variance of the solution in PSO is many times less than in GA, while in the combination of GA/PSO variances in the objective function are negligible compared to any other algorithm.

Simplified flowchart of the presented hybrid method is given by Figure 8 by which the statement how the discrete values of the buses in the power system is a mitigatory circumstance is confirmed. The authors Moradi and Abedini used GA for DG location observations; since the DG location can be only on a system bus, it cannot be somewhere in between, and PSO is used for continuous optimization of DG power once the bus is identified.

The fulfillment of the technical conditions is defined by the algorithm constraints that are integrated into a common function according to (19). The function *f* 1 represents the objective of real power losses reduction, the function *f* 2 represents the objective of the voltage profile improvement while the function *f* 3 is the radial feeder voltage stability index taken from the literature. Coefficients *k*1, *k*2, *β*1 and *β*2 represent the penalizing coefficients of the corresponding expressions. The presented objective function considers the values of the voltage (Vni) and the apparent power (Sni) on the observed radial feeder busbars.

$$f = \operatorname{Min} \left( \left( \delta\_1 + k\_1 f\_2 + k\_2 f\_3 \right) + \delta\_1 \underbrace{\sum\_{i \in \text{Woc}} \left[ \max(V\_{ui} - V\_{ui}^{\text{max}}, 0) + \max\left(V\_{ui}^{\text{min}} - V\_{ui}, 0\right) \right]}\_{\text{(DG position)}} + \delta\_2 \underbrace{\sum\_{i \in \text{N}} \max(|S\_{ui}| - |S\_{ui}^{\text{max}}|, 0)}\_{\text{(DG power)}} \right) \tag{19}$$

$$\underbrace{\begin{pmatrix} \text{Initial random} \\ \text{DG power} \end{pmatrix}}\_{\text{(DG power)}} \underbrace{\begin{pmatrix} \text{Initial random} \\ \text{DG power} \end{pmatrix}}\_{\text{(DG power)}} \underbrace{\begin{pmatrix} \text{Initial power} \\ \text{Flow} \end{pmatrix}}\_{\text{(DGG power)}} \underbrace{\begin{pmatrix} \text{FSO for} \\ \text{DG power} \end{pmatrix}}\_{\text{(DG power)}} \underbrace{\begin{pmatrix} \text{FSO for} \\ \text{DG power} \end{pmatrix}}\_{\text{(DGG power)}} \underbrace{\begin{pmatrix} \text{FSO for} \\ \text{DG power} \end{pmatrix}}\_{\text{(DGG power)}} \underbrace{\begin{pmatrix} \text{FSO for} \\ \text{DG power} \end{pmatrix}}\_{\text{(DGG power)}} \underbrace{\begin{pmatrix} \text{FSO for} \\ \text{DG power} \end{pmatrix}}\_{\text{(DGG power)}} \end{pmatrix}\_{\text{(DGG power)}}$$

**Figure 8.** Simplified illustration of PSO and GA hybrid method by Moradi and Abedini.

According to the authors, the proposed method shows a significant increase in the accuracy and repeatability of the results, but the authors conclude that more time is needed for the method implementation compared with using one of the methods separately.

An innovative approach to optimization is offered by Saif et al. [46] defining a simulation-optimization process where the technical validity of the optimization is evaluated by a simulation check instead of the optimization process limited by equality or inequality. The developed method, which authors call dual layer simulation optimization because of its simultaneous performance in the MATLAB and the GAMS programming tool, uses the load-flow calculation for all simulation cases and Particle Swarm method to determine the distribution of different types of unmanageable DG with objective to increase the reliability of the system supply and to reduce operating and investment costs. The developed method is successfully implemented on the part of the real power system. Surprisingly, among the optimal DG distribution solutions solar power plants and electricity storage systems are not included, but only a wind power plants which authors attribute to the availability of primary energy in the observed area. The conclusion of their work is to meet the energy needs of the observed network with multiple radial feeders using unmanageable DG thus creating the precondition for the island operation of a future Smart Grid.

Two papers by Aman et al. [49,89] describe the use of the PSO algorithm with the objective of scheduling multiple DG units. The authors introduce new criteria for describing the impact of a DG unit on the observed network—the criteria of possible increase of load and index of feeder stability. By additional checks and comparisons of the proposed method with the analytical method and the search algorithm, the authors confirm the correctness of the use of the PSO algorithm to solve the DG scheduling problem with losses reduction objective thus achieving the most useful impact of DG on the observed network.

Comparison of optimization methods developed on the basis of the PSO algorithm and the analytical approach were presented in the paper of Kansal et al. [47] which addresses the problem of distribution of different DG technologies with objective of reducing losses according to (20).

$$Min\,\mathrm{P\_L} = \sum\_{i=1}^{N} \sum\_{j=1}^{N} \left[ a\_{ij} (P\_i P\_j + Q\_i Q\_j) + \beta\_{ij} (Q\_i P\_j - P\_i Q\_j) \right] \tag{20}$$

Authors define four DG types, considering the possibility of generating active and reactive power and sugges<sup>t</sup> integration of one of them with respect to the results of the simulation. The results obtained with different optimization approaches are identical in the analysis of the optimal DG location and vary up to 5% for the optimal DG power dispatch.

In the leading scientific bases exist several scientific papers that use biologically inspired optimization algorithms. Niknam et al. [90–92] published several papers where honey-bee mating algorithm is used to solve the multi-objective problem of DG distribution in the distribution network [90] or to solve the problem of change of distribution network topology [91,92]. Equations from Reference [90] are given by (21)–(24):

$$F\_1(X) = \min \sum\_{i=1}^{N\_{f\varepsilon}} \mathbb{C}\_{f\varepsilon} \left( P\_{fc} \right) + \sum\_{i=1}^{N\_{wind}} \mathbb{C}\_{wind}(P\_{wind}) + \sum\_{i=1}^{N\_{pv}} \mathbb{C}\_{pv} \left( P\_{pv} \right) + \mathbb{C}ost\_{sub} \tag{21}$$

$$\,\_2F\_2(X) = \min \sum\_{i=1}^{N\_{\text{bus}}} \frac{\left| V\_{Rating} - V\_i \right|}{V\_{Rating}} \tag{22}$$

$$F\_3(X) = \min \sum\_{t=1}^{N\_d} \sum\_{i=1}^{N\_{lr}} \left( R\_i \times |I\_i|^2 \times \Delta t \right) \tag{23}$$

$$F\_4(X) = \min E\_{t\_{fc}} + E\_{t\_{wind}} + E\_{t\_{pv}} \tag{24}$$

In the papers authored by Niknam, according to the principle of selection of the queen bee among the bees, the best candidate for the survival of a population in the power engineering represents the DG or network topology.

The usefulness and efficacy of the PSO algorithm is further clarified in References [70,93–99], where this algorithm has been used to solve optimization problems or prediction problems and algorithms of evolution strategy have been compared with this algorithm.

Authors Kumari et al. [70] compared the GA, the improved GA and PSO algorithm for optimizing static VAR compensators in the network with the objective of reducing losses and achieving optimum load flows in the network. The improved GA suggested by authors is highlighted by the implementation of five additional gene crossing operators, in addition to the three commonly used ones, and according to their results, it provides the best optimization results.

Authors Yin et al. [93] used the PSO algorithm to optimize the allocation of computer processes to distributed processors, and this work, although in the field of computer science, thoroughly clarifies the application of the used algorithm. With the hybrid method which arose from the combining of the PSO algorithm and fuzzy logic systems authors Zhang et al. [94] solve the problem optimal reactive power flows to maintain voltage in transmission networks. The best solutions of each iteration of the Particle Swarm intelligence algorithm in this paper have been used as a measure of the feature parameters for the next iteration.

The adjusted PSO algorithm that addresses the problem of optimal DG allocation with the objective of losses reduction in the observed system is presented by Ashari et al. [95] while the usual Particle Swarm intelligence algorithm approach to solve the same problem is used by Bhumkittipich et al. [96] for the similar problem. A complete review of all derivatives of the PSO algorithm that have been known to authors Zhang et al. [97] and which can result in a number of best solutions, points to the usefulness of the PSO algorithm to solve many optimization problems. The apparent similarity of the algorithms in the papers of various authors [44,98] suggests that it is possible to achieve somewhat different approach and custom algorithm with exceptionally small variations in the principles of performance. That is also warned by authors [59] who advocate for a different development process of meta-heuristic methods and more significant changes.

A hybrid approach for addressing the DG distribution with the objective of total losses reduction and maintenance of a complex distribution network voltage profile, based on a unified GA and artificial neural networks, along with the GA and power flow calculation, is presented in Reference [100]. In this paper, the genetic algorithm determines the optimal location of one DG in a distribution network while the DG impact on the total losses is determined by the artificial neural network integrated into the GA. Described approach has not resulted in valid solutions when addressing the DG distribution problem in ring topology of distribution networks and a new hybrid approach has been developed by combining the genetic algorithm and the power flow calculation. In the same paper is presented a hybrid algorithm developed on the unification of two artificial neural networks with operating principles based on the search algorithms, but the authors point to the disadvantages of such an approach.

The need for active distribution network managemen<sup>t</sup> is mentioned for the first time in the scientific paper of authors Soares et al. [45], who recognized the need for answering to consumer demands and the periodicity of some energy resources. The authors presented a special form of signaled PSO algorithm that enabled particles to change the speed parameter during the optimization process to gain the most feasible combination of wind, photovoltaic, fuel cell and energy storage. Change of speed was defined by given rules and observed with demand response and optimal storage charge/discharge. The proposed method was successfully tested on a model of Portugal's power system. Use of such precisely designed meta-heuristic optimization methods in market environment and reallife conditions was proven, and the direction of future scientific research was given in the form of one-day division in 24 independent simulations, which later became the paradigm of all future research. Although the proposed method did not provide the most economical result, but the second of six different methods, the most efficient method mixed-integer nonlinear programming (MINLP) is not suitable for larger systems and is not fully usable in production [45]. The authors presented with grea<sup>t</sup> quality a mathematical model of contemporary challenges in the power system, given by expression (25).

$$\begin{array}{ll} \mathop{\rm N\_{\rm PU}}\_{\rm PUmin} & \mathop{\rm N\_{\rm PU}}\_{\rm PUm1} \\ & \mathop{\rm N\_{\rm PU}}\_{\rm PUm1} + \mathop{\rm P\_{\rm PU}}\_{\rm PUm1} \\ & & + \sum\_{\begin{subarray}{c} \text{N\_{\rm CU}} \\ \text{N} \in \text{I} \end{subarray}}^{\rm N\_{\rm PU}} P\_{\rm Einf\{\rm I(\mathcal{I},t) \}} \\ & & + \sum\_{\begin{subarray}{c} \text{N\_{\rm U}} \\ \text{S} \in \text{I} \end{subarray}}^{\rm N\_{\rm U}} P\_{\rm Einf{\rm I(\mathcal{I},t) \}} \\ & & + \sum\_{\begin{subarray}{c} \text{N\_{\rm U}} \\ \text{S} \end{subarray}}^{\rm N\_{\rm U}} P\_{\rm Einf{\rm I(\mathcal{I},t) \}} + \sum\_{\begin{subarray}{c} \text{N\_{\rm U}} \\ \text{S} \end{subarray}}^{\rm N\_{\rm U}} P\_{\rm Einf{\rm I(\mathcal{I},t) \}} + \sum\_{\begin{subarray}{c} \text{N\_{\rm U}} \\ \text{S} \in \text{I} \end{subarray}}^{\rm N\_{\rm U}} P\_{\rm Einf{\rm I(\mathcal{I},t) \}} + \sum\_{\begin{subarray}{c} \text{N\_{\rm U}} \\ \text{S} \in \text{I} \end{subarray}}^{\rm N\_{\rm U}} P\_{\rm Einf{\rm I(\mathcal{I},t) \}} \\ \end{array} \tag{25}$$

Expression (21) lacks the observability of power losses in power equipment, such as cables and transformers, that can be described as *NPL* ∑ *PL*=1 *PPowerLosses*(*PL*,*<sup>t</sup>*) and added to the right side of the equation for the part of the Smart Grid to be self-sufficient. Observed units *PV*, *FC*, *S*, *LC* and *L* present appropriate technologies used in period *t*.

Presented scientific papers are clear indicator of the possibility for the development of robust intelligent solutions that can solve a certain set of complex problems in the power engineering, but without needed criticism, approaches based on soft computing methods should not be accepted as general solutions for absolutely all cases [101]. The authors also gave a mathematical model of technical limitations of the power system as inequalities that consider the specifics of individual technologies.

Reliability of distributed generation supply was introduced by Saif et el. [46] who presented double-layer simulation optimization, using load flow for all simulations and Particle Swarm Optimization in order to increase reliability of observed system. The authors optimized the given problem by aiming for operating and investment cost reduction. Developed method was successfully tested on a British power system model and the results were compared to other optimization methods. The authors of Reference [46]

identified conditions for isolated distribution network operation. Aman et al. [38,49] used standard Particle Swarm Optimization algorithm, but introduced new criteria for describing the impact of distributed generation such as increasing the load capacity of observed system and the stability of network feeder.

The presented papers clearly point to the logical conclusion that, the once advanced and sometimes obscure methods known only to the scientific community, with today's computing power, can be used effectively in real production and be integrated into tools to optimize the distribution network in real time. Detailed mathematical models with metaheuristic methods supported by iterative calculations with thoroughly examined features and shortcomings of individual methods promise a new advanced Smart Grid in the future.

#### 4.3.3. GA and PSO Comparison

GA and PSO share many common features, such as random start, fitness values, population update based on randomness and necessity for fine-tuning in order to find global optimum [57]. However, PSO does not have crossover and mutation operators and particles have memory and velocity. Main difference between two methods is that GA comes with new solutions with struggle among individuals, while PSO nurtures social interaction between particles.

Social interaction in PSO defines natural leaders that spread information to others, while in GA chromosomes share information with each other and guide the whole group towards distinct area, leaving only elite individuals in each iteration. Moreover, population in GA gets smaller with each iteration since some of the individuals become removed, while in PSO population is constant and particles always have purpose in fine tuning the possible solution.

PSO is derivative-free, robust and flexible method, but prone to premature convergence if tuning parameters are not correct. However, particle parameters can be enhanced with solid mathematical equations that improve analysis and provide realistic convergence conditions. Self-organization of PSO with bottom-up approach integrated in the method prove wide applicability in many scientific areas.

Global perspective of each particle in PSO improves clustering efficiency and enables PSO algorithm to test multiple areas of the search space at the same time. Instead of competition-based GA, PSO values the cooperation of particles and the exchange of values according to objective criteria.

For greater overview and comparison Table 1 gives a perspective on solved challenges in ADN and SG environments. In accordance with the Chapter I—Introduction of this paper, Table 1 clearly gives a standpoint how the Distribution Network Optimization includes many topics and issues identified in the modern distribution networks, such as feeder reconfiguration, island operation, pricing and energy market, voltage profile improvements, etc.

#### **5. Optimal Smart Grid Management**

Intuitive thinking about the purposefulness of presented papers and research is confirmed by studying features in the field of research in current European and world research activities. Since 2009, the Working Group for Advanced Power Grids of the European Commission Smart Grids Task Force has been continuously issuing guidelines for the development of modern power systems [102].

Advanced power systems imply a change in paradigm by enabling DG, but also the application of technologies for monitoring, operational management, regulation of production and consumption using information technologies that are not the subject of this scientific research. The Joint Research Center for Smart Electricity Systems and Interoperability leads regulatory, technical and economic research at the level of the European Union. According to the relationship matrix [103] of the Joint Research Center, the countries most involved in research into advanced power systems are Spain, France, Italy, Germany, the

United Kingdom, Belgium and the Netherlands. Looking at the overview of the identified trends of the Joint Research Center [104] and the reviews of European distribution system operators [105], it is easy to conclude that innovative scientific research should provide answers to the challenges observed in the literature.

The issue of active distribution network managemen<sup>t</sup> is observed in recent scientific papers, mostly continuously for the past two years, and factors that can contribute to significant DG integration are identified and structured as multi-objective planning and active managemen<sup>t</sup> [50,106–114]. Modern grids imply multiple technologies such as advanced metering infrastructure, EMS, DMS, Big Data solutions and data management, pricing mechanisms on a day ahead market and inner day markets, various paradigms such as demand response, fault classification and DG scheduling, and horizontal and/or vertical control possibilities [53,115–117]. Colmenar-Santos et al. [75] detailed the challenges raised from increased DG integration in distribution network identifying advantages and technology characteristics of active managemen<sup>t</sup> network with DG operational optimization. In the fourth section of their paper authors distinguish optimization techniques based on Reference [54] and give comprehensive overview of advantages and disadvantages of different DG optimization techniques focusing on hybrid methods, thus becoming basis of future research by many scientists. Multi-objective planning, controllable loads, dispatched DG, stochastic DG and demand response become more prominent in Reference [75] as the report progresses. The authors touch upon regulatory and policy barriers preventing significant DG integration in the fifth and sixth sections of their paper, identifying the need for policy change according to the example of Germany, Denmark and Spain [75].

Power System of the future will enable justified use of energy, fairness in energy sharing, security of data and data governance, user own control and autonomy of the household while participating in a fair manner in energy community and virtual energy hubs [118]. The transition of the power industry will not go smoothly and synergy of all actors, legal and technical, regulatory and market, is needed for the transition to succeed [119], but when it does, the benefit will be for everyone included. As mentioned in Section 2, shifting the Smart Grid towards Cloud computing enables smoother transition, and the emergence of the Edge Computing paradigm enables the power industry to flourish [120].

Future of distribution networks are discussed by Bayod-Rújula et al. [50] and authors conclude that active distribution network with total control represents the first stage towards more advanced Smart Grid. According to the authors, second stage of distribution network development represents the microgrid system, a fully controlled entity that may provide ancillary services to the main system; third stage are virtual utilities with operational energy managemen<sup>t</sup> system yielding optimization opportunities; demand-side managemen<sup>t</sup> and demand response techniques become available once the previous prerequisites are met hence paving the way towards information and communication technology implementation [50].

In the wake of such information is paper by Shi et al. [109] where authors show operational energy managemen<sup>t</sup> in microgrid system consisting of dispatchable and nondispatchable DG, interruptible and deferrable loads and energy storages. Simplified overview of the described system is given by Figure 9, and similar models can be used to validate any other control solution intended for production purposes, as it was the case for the validation purposes of this paper.

Authors comply their proposed managemen<sup>t</sup> solution with IEC 61850, a communication standard for Smart Grids [121] and present output schedules for grid-connected and islanded modes of operation, respecting bus voltages while minimizing network requirements. Operational energy managemen<sup>t</sup> in distribution network is identified as the central problem of future distribution networks, microgrids and Smart Grids. Importance of this paper is evident in direct application of the scientific method in real-life microgrid environment. The authors present the results on a daily diagram, divided in 24 intervals,

and give operating schedules for PV system, Wind power plant, Diesel generator, battery storage and controllable load.

**Figure 9.** Simplified model of microgrid used for validation in this paper, inspired by Reference [109].

The system is controlled by Micro-Grid Central Controller (MGCC) with Exact, Convex Programming–based algorithm for Nonlinear Systems and Models based on predictor corrector proximal multiplier algorithm and optimal power flow formulation [109].

Operational microgrid energy managemen<sup>t</sup> based on mixed-integer linear programming solution considering wind power and PV system generation volatile characteristics, aided by storage system and grid connection can be found in the paper by Umeozor and Trifkovic [122]. The authors collected meteorological data to eliminate forecasting errors and schedule DG production accordingly. Different pricing scenarios were considered, and the authors concluded that economic circumstances of observed system greatly affect the output parameters.

The authors of Reference [123] represent the hybrid method emerged by combination of sorting GA without dominant solutions and Rough Set Theory tests. The method is used for solving the problem of optimal energy managemen<sup>t</sup> in a distribution network that consists of 123 nodes, 7 micro-grid systems and 9 distributed energy sources. The authors sugges<sup>t</sup> a game theory interactive matrix described by 10 mathematical expressions for solving the interaction between different sources observed through the point of common coupling (PCC). Multiple sources are observed during the operative control of one or multiple micro-grid systems inside of distribution network with L number of nodes. Interactive matrix is optimized by the minimization of three objective functions, (26)–(28), using the adapted hierarchically arranged GA, and the proposed method is tested on the IEEE 33 node test network in which the authors integrate three microgrid systems. In expressions (22)–(24), *Ul*,*<sup>t</sup>* implies voltage at node *l* within a network consisting of nodes

set L; *PPCC l*,*t* stands for PCC power exchange of microgrid at node *l*; PLOSS t represents power loss of ADN and *θ* is power exchange level of the observed system.

$$\min G\_1 = \sum\_{t=1}^{T} \sqrt{\sum\_{l \in L} (\mathcal{U}\_{l,t} - 1)^2 / L} \tag{26}$$

$$\text{min}G\_2 = \sum\_{t=1}^{T} \sqrt{\sum\_{l \in L} \left( P\_{l,t}^{\text{PCC}} - \theta \right)^2 / T} \tag{27}$$

$$\text{minG}\_3 = \sum\_{t=1}^{T} P\_t^{LOSS} \tag{28}$$

By comparing the results from the soft computing optimization methods with the results from the classical mathematical approach, the authors conclude that proposed approach yields betters results during the optimization of more complex systems with multiple DG units. The authors emphasize the specific achievement of the paper in the efficient methodology for solving complex optimization problems by using the computer intelligence, while the additional procedures and other hybrid solutions must be explored.

Automatic generation control (AGC) of distribution network with high share of DG and electric vehicles based on framework established for transmission systems is presented in the work by Batisetelli et al. [113]. The authors scaled the known AGC hierarchy to control DG, local distribution and load subsystems and logically divided active distribution network to virtual power isles managed by centralized control. Electric vehicles may contain both generation and load characteristics therefore authors deal with both problems simultaneously through linear programming model optimized by CPLEX optimizer that can be implemented in energy managemen<sup>t</sup> system of active distribution network. The model described in Reference [113] was tested and confirmed on a realistic four-feeder system from which the "smart user grid (SUG)" paradigm is developed by integrating multi-agent system procedures and entities of different functionalities. Extremely revealing contribution of the authors [113] is evident in "criteria for implementation and coordination of automatic generation control", comprising the communication necessary between observed levels, similar to the one presented in Reference [29]. Discrete 24-segment simulations representing 24-hour schedule is the methodology of most authors, including the one in Reference [113]. Although not the typical computational intelligence approach, paper considered important aspects of ADN managemen<sup>t</sup> with increased ratio of various types of DG. The absence of advanced optimization techniques is completely justified by an excellent principle demonstration that was clearly the goal of Reference [113], while the practicality and robustness of optimization solution developed on this research basis should consider more complex methods.

Scientific work in the wake of the abovementioned paper can be found in paper by Jun et al. [114] where authors solved the multi-agent optimization problem by means of integer programming dividing the day planning in 24 segments. EMS has a significant role in valid operation, optimization, control and balancing of "hybrid renewable energy generation system", as stated by the authors, and specific challenges of managemen<sup>t</sup> scheme are described in this paper. Complex unified modeling language diagram, solved in Java Agent Development Framework, of multi-agent system is presented, showing system classes, attributes and relationships between them. The authors introduce new parameters computed by individual agen<sup>t</sup> that indicate the attention of observed agen<sup>t</sup> as load, or as DG. These parameters sugges<sup>t</sup> the economic and power qualities of each agen<sup>t</sup> integrating the stochastic nature of some RES. Detailed analysis of EMS conclude the structural requirements of future communication, behavior and optimizations objectives.

Significant papers indicate the importance of considering meteorological dependencies of some RES-based DGs along with independencies of other types of non-RES DG units. Gu et al. [124] thoroughly defined factors of the future managemen<sup>t</sup> service consisting of demand side management, operation objectives, energy pricing, weather forecast, energy management, maintenance scheduling, physical limitations of equipment involved, operation scheduling and interaction with main supply grid via PCC. The authors define the perspective of energy managemen<sup>t</sup> by achieving higher level of efficiency and lower emission levels while maintaining power quality and providing ancillary services.

From their work, one can ge<sup>t</sup> a clear idea of what a future energy managemen<sup>t</sup> system for the distribution network should look like and what should be taken into account, as illustrated by Figure 10. The authors thus state how to respect consumers, plan according to meteorological indicators, consider equipment limitations and the situation on the energy market, all in order to plan the optimal timetable of DG.

**Figure 10.** Energy managemen<sup>t</sup> system of the future distribution grids, inspired by Gu et al.

Zhang et al. [125] concluded that operational managemen<sup>t</sup> can reduce line losses and improve voltage profiles which will benefit DG developers as well as distribution network operators. The authors define the traditional distribution network planning and operation as "fit and forget" methodology and conclude that this principle will need to change in order to allow higher penetration levels and development of Smart Grid. Mohamed and Mohammed [126] developed effective mathematical algorithm for distribution grid managemen<sup>t</sup> in a Smart Grid form by giving priority to renewable energy resources while maintaining least possible cost and satisfying load demand. The authors predicted PV generation by historical data and statistical smoothing techniques, while wind generation and load data were modeled by non-linear regression modeling. The main objective achieved by Reference [126] is minimizing the power obtained from parent grid while retaining additional power for sudden demand changes. A case study of Smart Grid managemen<sup>t</sup> in Japan is presented in Reference [127] from a policy perspective, and an essential requirement for managemen<sup>t</sup> services in order to achieve maximal benefits is highlighted in this paper.

Significant achievement towards real-time economic dispatch and operational managemen<sup>t</sup> of active distribution network is evident in paper by Kellerer et al. [128] in which authors propose new method based on statistical inference method—a probabilistic graphical model method. The authors prove algorithm functionality in global optimal search, irrelevant of network size, and with the assumptions that 70% of consumer nodes have

randomly integrated RES-based DG units. The main subject of their observations is in solving the economic dispatch problems without having a full knowledge of all component models, for which they assume is difficult in competitive market environment. The proposed algorithm is successfully tested on a network whose topology is based on real Smart Grid data in Southern Germany.

With the objective of reducing CO2 emissions Lamadrid et al. [129] proposed a novel optimization solution that favors RES in power dispatch and scheduling, if the technology permits planning. From the perspective of system operator and in compliance with network policies, authors present their optimization formulation respecting technical and economic limitations and prove the usefulness of their method by testing it on 279-bus transmission network in Texas, USA. The authors have taken into consideration a stochastic nature of some RES and gave them priority in dispatch schedule. Although modeling is complex, authors managed to perform it correctly by using mathematical correct workaround solutions. By proposed solution authors managed to simulate a future 24-hour period which is adequate for system schedule and operation in market conditions. For wind power modeling, authors used combination of historical and calculated data. This paper is very important due to the complexity of observed problem, and although the authors themselves say that improvements are needed, this paper presents state-of-the-art in operational managemen<sup>t</sup> and DG scheduling.

A modified self-adaptive PSO algorithm serviceability for multi-objective optimal operation managemen<sup>t</sup> of a distribution network containing a fuel-cell power plant was proven in paper by Niknam et al. [130], and a computational intelligence principle was described that contained fuzzy-controlled decision-making. The authors developed a practical 24-segment algorithm, providing Pareto solutions. A similar fuzzy controlled approach can be found in paper by Elamine et al. [131] where wind speed is determined by PSO optimized ANN based on small historical set and fuzzy agen<sup>t</sup> for battery storage management.

Previous scientific works have successfully identified key issues and constraints that need to be addressed when creating a widely applicable solution for active distribution network operational managemen<sup>t</sup> and scheduling. The biggest disadvantage of presented papers is the need for a complex mathematical model of the observed system which is usually not sensitive to structural changes in the same system, hence not applicable to distribution grids. Accordingly, universal applicability of the proposed solutions is not always achieved, and each observed case is at the same time the only case observed and researched by a group of scientists. Issues in Smart Grid application can be compartmentalized into five categories [132]: concept presentation—advantages and limitations of renewable energy distributed generation; technology adoption for hybrid energy system; optimal allocation problems and technical characteristics; forecasting, pricing and policy issues; Smart Grid–integration challenges.

With the development of active distribution networks, which aspire to become Smart Grids, the emphasis of scientists in this area will tend to be on the development of widely applicable and robust solutions for which it will be necessary to consume and respect the knowledge and achievements displayed in all of the so-far listed scientific works.
