Page 17 - IJEEE-2023-Vol19-ISSUE-1

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