Firefly algorithm for optimal allocation and sizing of Distributed Generation in radial distribution system for loss minimization

Nadhir, Ketfi; Djabali, Chabane; Bouktir, T.

doi:10.1109/codit.2013.6689549

Cited by 13 publications

(11 citation statements)

References 4 publications

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“…The optimization using Firefly algorithm (FA) technique has been chosen in this research work for solving the ideal settings of the coordinated voltage control methods in the power dispersal network connected to distributed generations (DGs). The implementation of Firefly algorithm method has been developed and simulated in MATLAB in finding the ideal settings of the coordinated voltage control selected [23,24]. At the same time using the Newton-Raphson load flow, the objective function in decreasing the voltage deviation and power losses is simulated in the MATLAB environment.…”

Section: Implementation Of Firefly Algorithm For Finding the Ideal Sementioning

confidence: 99%

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Coordinated and optimal voltage control for voltage regulation using firefly algorithm

Kamarudin

Hashim

2019

IJEECS

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The operation and control of electricity in distribution networks has faced great challenges as a large number of distributed generations (DGs) are integrated. Connection of distributed generations (DGs) in the distribution system offers advantages in terms of reducing distribution and transmission costs as well as encouraging the use of renewable energy sources. The power flow in the distribution systems is no longer moving in a single direction and this resulted the system to become as active distribution networks (ADN). One of the main problems in ADN is the voltage regulation issue which is to maintain the voltage to be within its permissible limits. Several methods of voltage control methods are available and focus is given in finding the optimal voltage control using artificial intelligence techniques. This paper presents an optimal and coordinated voltage control method while minimizing losses and voltage deviation of the network. The optimal and coordinated voltage control scheme is implemented on an IEEE 13 bus distribution network for loss and voltage deviation minimization in the networks. Firefly Algorithm (FA) which is a known heuristic optimization technique for finding the optimal solution is used in this work. The results are compared with another optimization method known as Backtracking Search Algorithm (BSA) for identifying the best setting for solving the voltage regulation problem. In order to solve the multi-objective optimization issue, the MATPOWER load flow simulation is integrated in the MATLAB environment with the optimization algorithm.

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Section: Implementation Of Firefly Algorithm For Finding the Ideal Sementioning

confidence: 99%

“…Indonesian J Elec Eng & Comp Sci ISSN: 2502-4752  Coordinated and optimal voltage control for voltage regulation using .... (Muhamad Najib Kamarudin)571 Flowchart of the ideal settings of voltage control with DGs using Firefly Algorithm[24] …”

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confidence: 99%

Coordinated and optimal voltage control for voltage regulation using firefly algorithm

Kamarudin

Hashim

2019

IJEECS

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“…For this problem, size of DG is considered as control variable which is denoted by gx g� = min + rand(l)x( maxmin) (4) Where, x varies from 1 to number of population and min and max are the minimum and maximum values of size of DGs.Teacher will put maximum effort to increases the knowledge level of the whole class. By this effort the gx variable is modified as: gnew =g ol d + Diff_Mean (5) Where, DifC M e an=rx(g teacher-(Tf ) xM ) , glcachcr is the best solution among. In this case, best solution means the size of DG which gives minimum losses.…”

Section: ) Teacher Phasementioning

confidence: 99%

“…There are a number of approaches used for placement and sizing of DG units in distribution network. Many techniques have been developed for solving this difficult combinatorial problem: such as genetic algorithm [4] , firefly algorithm [5] , artificial bee colony [6] , analytical based methods [7] - [9], heuristic algorithms and meta heuristic algorithms developed based on the swarm intelligence in nature like PSO [10].…”

Section: Iintroductionmentioning

confidence: 99%

Loss allocation in radial distribution system with multiple DG placement using TLBO

Patel

Mistry

Roy

2015

2015 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT)

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The distribution network is one of the most important part of the electrical system, as it is the interface between transmission system and consumer. A customer has to be provided with good quality of power having a good voltage profile with minimum power loss. The benefits associated with the Distributed Generation (DG) placement are reduction in power loss, reduction in power demand, improve efficiency, improve reliability as well as improvement in voltage profile. The penetration of DG increasing day by day due to growth in demand,due to this the importance of energy loss allocation has also increased.In this paper four different Loss Allocation techniques are studied. The optimal location and capacity for multiple DGs are found out for minimization of active power loss. For finding optimal location of multiple DGs a Teaching and Learning Based Optimization (TLBO) algorithmis used. IEEE standard30-noderadial distribution system is studied for power loss allocation with and without DG.

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“…In [5] the authors use an artificial bee colony algorithm for the optimal allocation of DG seeking power loss minimization; a similar methodology is presented in [6] using particle swarm optimization. Other metaheuristic techniques such as firefly algorithm [7] and tabu search [8] have also been applied to the optimal location of DG. The technical literature referring to DG studies is very extensive.…”

Section: Introductionmentioning

confidence: 99%

Weighting transmission loading relief nodal indexes for the optimal allocation of distributed generation in power systems

Sierra¹,

Giraldo²,

López-Lezama³

2018

ces

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This paper presents a methodology for the optimal location of distributed generation (DG) in power systems using the Weighted Transmission Loading Relief (WTLR) nodal indexes. These indexes are used to guide a GRASP metaheuristic in charge of the optimal allocation of different DG units in a transmission network. The objective consists on minimizing the most negative WTLR indexes, which results in an increase of the system security. Several tests are presented in a representative prototype of the Colombian power system, evidencing the effectiveness of the proposed methodology. Results show that a proper allocation of DG units minimizes system overloads both in normal operation and under contingencies.

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Firefly algorithm for optimal allocation and sizing of Distributed Generation in radial distribution system for loss minimization

Cited by 13 publications

References 4 publications

Coordinated and optimal voltage control for voltage regulation using firefly algorithm

Coordinated and optimal voltage control for voltage regulation using firefly algorithm

Loss allocation in radial distribution system with multiple DG placement using TLBO

Weighting transmission loading relief nodal indexes for the optimal allocation of distributed generation in power systems

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