Optimal power flow with emission controlled using firefly algorithm

Herbadji, Ouafa; Nadhir, Ketfi; Slimani, Linda; Bouktir, Tarek

doi:10.1109/icmsao.2013.6552559

Cited by 12 publications

(9 citation statements)

References 8 publications

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“…Recently, many metaheuristic optimization algorithms such as particle swarm optimization (PSO) [83], ant colony optimization (ACO) [84], shuffled frog leaping (SFL) [85], differential evolution (DE) [86], biogeography-based optimization (BBO) [87], gravitational search algorithm (GSA) [88], firefly algorithm (FA) [89], teaching-learning-based optimization (TLBO) [90], grey wolf optimizer (GWO) [91], ant lion optimizer (ALO) [92], moth-flame optimization (MFO) [93], crow search algorithm (CSA) [94], salp swarm algorithm (SSA) [95], Levy spiral flight equilibrium optimizer (LSFEO) [96], and jellyfish search optimizer (JS) [97], have been applied as significant problem solvers to cope with the weaknesses of the traditional algorithms in solving the OPF problem benchmarks. Moreover, many researchers applied metaheuristic algorithms to solve real power systems [98,99].…”

Section: Related Workmentioning

confidence: 99%

Hybridizing of Whale and Moth-Flame Optimization Algorithms to Solve Diverse Scales of Optimal Power Flow Problem

et al. 2022

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The optimal power flow (OPF) is a practical problem in a power system with complex characteristics such as a large number of control parameters and also multi-modal and non-convex objective functions with inequality and nonlinear constraints. Thus, tackling the OPF problem is becoming a major priority for power engineers and researchers. Many metaheuristic algorithms with different search strategies have been developed to solve the OPF problem. Although, the majority of them suffer from stagnation, premature convergence, and local optima trapping during the optimization process, which results in producing low solution qualities, especially for real-world problems. This study is devoted to proposing an effective hybridizing of whale optimization algorithm (WOA) and a modified moth-flame optimization algorithm (MFO) named WMFO to solve the OPF problem. In the proposed WMFO, the WOA and the modified MFO cooperate to effectively discover the promising areas and provide high-quality solutions. A randomized boundary handling is used to return the solutions that have violated the permissible boundaries of search space. Moreover, a greedy selection operator is defined to assess the acceptance criteria of new solutions. Ultimately, the performance of the WMFO is scrutinized on single and multi-objective cases of different OPF problems including standard IEEE 14-bus, IEEE 30-bus, IEEE 39-bus, IEEE 57-bus, and IEEE118-bus test systems. The obtained results corroborate that the proposed algorithm outperforms the contender algorithms for solving the OPF problem.

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Section: Related Workmentioning

confidence: 99%

Hybridizing of Whale and Moth-Flame Optimization Algorithms to Solve Diverse Scales of Optimal Power Flow Problem

et al. 2022

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“…The process of incorporating the firefly algorithm FA into optimal power flow is summarized in Figure 3 Where each firefly represents the values of the active power generated [22].…”

Section: Firefly Algorithm For Optimal Power Flowmentioning

confidence: 99%

Optimal Power Flow Using Firefly Algorithm with Consideration of FACTS Devices "UPFC"

Herbadji¹,

Bouktir²

2015

ijeei

Self Cite

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This paper present solution of optimal power flow problem using a firefly algorithm (FA) with consideration of FACTS devices "UPFC". The objective is to minimize the total fuel cost of generation and also maintain an acceptable system performance in terms of limits on generator real power and reactive power outputs, bus voltages and power flow of transmission lines. In order to maximize the relief of congestion in power system, to reduce the total system real power loss and improves the loadability of the system we propose also the optimization of the placement of FACTS devices in the power system (UPFC). The proposed method is tested on IEEE 30-bus system and the Algerian electrical network. The result of this method is compared with those obtained by biogeography based optimization (BBO), genetic algorithm (GA), and artificial bee colony (ABC) algorithm.

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“…It is inspired by the flashing lights of fireflies in nature. Previous studies show that the FA can perform superiorly, compared with genetic algorithm and particle swarm optimization [2,3], and it is applicable for mixed variable and engineering optimization [4][5][6][7][8][9][10]. In [11], it presented FA to minimize the real power losses and to improve the voltage profile.…”

Section: Introductionmentioning

confidence: 97%

Enhancing firefly algorithm using generalized opposition-based learning

Zhu²,

et al. 2015

Computing

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Firefly algorithm has been shown to yield good performance for solving various optimization problems. However, under some conditions, FA may converge prematurely and thus may be trapped in local optima due to loss of population diversity. To overcome this defect, inspired by the concept of opposition-based learning, a strategy to increase the performance of firefly algorithm is proposed. The idea is to replace the worst firefly with a new constructed firefly. This new constructed firefly is created by taken some elements from the opposition number of the worst firefly or the position of the brightest firefly. After this operation, the worst firefly is forced to escape from the normal path and can help it to escape from local optima. Experiments on 16 standard benchmark functions show that our method can improve accuracy of the basic firefly algorithm.

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Optimal power flow with emission controlled using firefly algorithm

Cited by 12 publications

References 8 publications

Hybridizing of Whale and Moth-Flame Optimization Algorithms to Solve Diverse Scales of Optimal Power Flow Problem

Hybridizing of Whale and Moth-Flame Optimization Algorithms to Solve Diverse Scales of Optimal Power Flow Problem

Optimal Power Flow Using Firefly Algorithm with Consideration of FACTS Devices "UPFC"

Enhancing firefly algorithm using generalized opposition-based learning

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