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13 | Hadaeghi & Abdollahi

proposed method is demonstrated. Moreover, this method              test feeder. A new algorithm to solve the problem of distribution
achieves stable configurations that remained feasible over long     networks reconfiguration using Improved Selective Binary
periods of time without requiring further reconfigurations. These   Particle Swarm Optimization (IS-BPSO) has been proposed in
results indicate the need to include load uncertainties when        [13]. The proposed method demonstrates a new sigmoid
analyzing under realistic conditions. A feeder reconfiguration      function that can improve the control in the rate of change of the
problem in the presence of distributed generators has been          particles and improve the convergence of the results. The
presented in [7] to minimize the system power loss while            proposed algorithm is used to reduce power losses in distribution
satisfying operating constraints using Hyper Cube-Ant Colony        networks, this method is used on two test systems of 33 and 94
Optimization (HC-ACO) algorithm. In this study, loss                bus distributions. The simulation results show that the proposed
sensitivity analysis was used to identify the optimal location for  method is very efficient and guarantees the achievement of the
the installation of DG units. The simulations have been             global optimum. Reference [14] deals with the simultaneous
performed on a 33- bus radial distribution system at four           distributed generation (DG) planning and distribution network
different cases to confirm the efficiency of the proposed method    reconfiguration issue. The problem is formulized as an
compared to other methods in the articles. The results of this      optimization model which includes three types of variables such
method were very fast and effective. In [8] the Cuckoo Search       as DGs location as the integer variables, DGs operating point as
Algorithm (CSA) has solved the distribution system                  the continuous ones and switches open or close state as the
reconfiguration with the goal of reducing losses and improving      binary variables. A 3D-GSO method has also been introduced to
the voltage profile. In [9] A Modified Bacterial Foraging           cope with this issue. The proposed method is a general
Optimization Algorithm (MBFOA) is presented and the problem         optimization scheme applicable to all types of optimization
of distribution network reconfiguration is studied to minimize      problems which deal with an integer, continuous, and binary
power loss. In reference [10] with the help of multi-objective      variables at the same time. Five different scenarios at three load
distribution network reconfiguration, a method for optimizing       levels are also considered to cover all possible conditions. The
unbalanced distribution network to maintain voltage stability       proposed method is validated through comprehensive
using the Firefly algorithm is proposed. The objectives that        simulation studies on 33-bus and 69-bus test systems.
should be minimized are the total network power losses, the
deviation of bus voltage and load equalizing in the feeders. Each      In this paper, a new method based on the combination of the
goal is moved into the fuzzy domain utilizing its membership        Teaching-learning-based-optimization (TLBO) and Black-hole
function and fuzzy field independently. The proposed method         (BH) algorithm has been proposed to reconfiguration of
for network reconfiguration has been implemented in 25-node         distribution networks in order to minimize active power losses
and 19-node UDNs. The results obtained by the suggested             and improve voltage profiles in the presence of distributed
method of these two unbalanced networks have been compared          generation sources. The proposed model is simulated using 33
with that of obtained by Genetic algorithm (GA), ABC                IEEE radial bus networks and the results show the efficiency of
algorithm, PSO algorithm and GA-PSO algorithm using the             the proposed method.
same objective function. A Combined method with existing
methods is also presented. In [11] using Ant Colony                                    II. PROBLEM FORMULATION
Optimization (ACO) technique, a novel method is proposed for
simultaneous dynamic scheduling for distribution network               As already mentioned, the distribution network
reconfiguration in the presence of DG units with uncertain and      reconfiguration problem is actually an optimization problem
variant generations over time. This method is applicable to both    and, like any other optimization problem, has objective
smart and classic distribution systems. For the second case, state  functions and constraints, which are as follows:
estimation method should be used to estimate the loads at
different buses using a limited number of measurements. The         A. Minimizing the Active Power Losses
objective of this method is to minimize the total operational cost
of the grid, the cost of power purchase from the sub transmission      Minimizing the active power losses can be an objective
substation, cost of customer interruption penalties, Transformers   function for the optimization problem. This index is considered
Loss of Life expenses, and the switching costs. In [12] a           as follows [15]:
dynamic reconfiguration method for a three-phase unbalanced
distribution network is presented. The topology is optimized for    !!"## = ?')*(+ $$[('$#)% + ('$&)% - 2'$#'$& cos /$]  (1)
the predicted time periods and is adaptive to the time-varying
load demand and DG output while minimizing the daily power             Where '$# and '$& are the values of the voltage amplitude at
loss costs. To improve the calculation efficiency, several          the two ends of sending and receiving line m, respectively. $$	
linearization methods have been proposed to formulate the           is conductivity of line m, /$ is the phase difference between the
dynamic reconfiguration as a mixed-integer linear programming       two ends voltages of line m, and 23 is the number of lines.
problem. The effectiveness of the proposed method has been
verified by the test results obtained on a modified IEEE 33 node    B. Voltage Profile Improvement

                                                                       Voltage is one of the most important indicators of power
                                                                    quality, which its profile improvement can be considered as one
                                                                    of the objective functions in the optimization problem. This
                                                                    objective function can be expressed mathematically as the
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