Unit commitment based on modified firefly algorithm

Hussein, Balasim M.; Jaber, Aqeel S.

doi:10.1177/0020294019890630

Cited by 20 publications

(12 citation statements)

References 21 publications

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“…The following enhancements were made to the FA to achieve the proposed M-FA [23], for every 2 mutations, 3 crossover processes are considered. The overall generations are to be pushed toward optimal (either local or global) [24], [25]. In each iteration, the modification must be done to complete the following two steps:…”

Section: The Proposed Algorithmmentioning

confidence: 99%

Evolutionary algorithm solution for economic dispatch problems

Hussein

2022

IJECE

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<span>A modified firefly algorithm (FA) was presented in this paper for finding a solution to the economic dispatch (ED) problem. ED is considered a difficult topic in the field of power systems due to the complexity of calculating the optimal generation schedule that will satisfy the demand for electric power at the lowest fuel costs while satisfying all the other constraints. Furthermore, the ED problems are associated with objective functions that have both quality and inequality constraints; these include the practical operation constraints of the generators (such as the forbidden working areas, nonlinear limits, and generation limits) that makes the calculation of the global optimal solutions of ED a difficult task. The proposed approach in this study was evaluated in the IEEE 30-Bus test-bed; the evaluation showed that the proposed FA-based approach performed optimally in comparison with the performance of the other existing optimizers, such as the traditional FA and particle swarm optimization. The results show the high performance of the modified firefly algorithm compared to the other methods.</span>

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Section: The Proposed Algorithmmentioning

confidence: 99%

Evolutionary algorithm solution for economic dispatch problems

Hussein

2022

IJECE

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“…Numerous researchers have examined an assortment of evolutionary optimization techniques, related to UC problem, in diverse dimensions. This paper proposes to present a complete review of the UC problem, integrated with numerous types of evolutionary optimization techniques like UC problem incorporated with GA [133]- [159], UC problem incorporated with PSO [160]- [170], UC problem incorporated with EA [171]- [177], UC problem incorporated with EP [178]- [183], UC problem incorporated with DE [184]- [190], UC problem incorporated with SFLA [191]- [193], UC problem incorporated with FA [194]- [199], UC problem incorporated with other evolutionary optimization techniques like BFA [200], FSA [201], [202] and CSA [203], UC problem incorporated with Hybrid evolutionary optimization techniques [204]- [244], in the subsequent sections. Distinctive features of the references, connected to evolutionary optimization techniques, are captured and showed in Table 4, including the year of the publication.…”

Section: Review Of Uc Problem Dealt With By Evolutionary Optimization...mentioning

confidence: 99%

“…A combination of PL and modified FA was proposed by Hussein and Jaber, to solve the UC problem, in two steps, using PL (on/off cases of units provided in the first step), combined with modified FA (load scheduling between units provided in the second step) [199]. In this approach, the randomization parameter was not kept constant and it can be decreased linearly with iterations, with respect to their initial and final values.…”

Section: G Uc Problem Incorporated With Famentioning

confidence: 99%

A Comprehensive Review on Evolutionary Optimization Techniques Applied for Unit Commitment Problem

Muralikrishnan¹,

Jebaraj²,

Rajan

2020

IEEE Access

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Unit Commitment (UC) is a key task in electric power system operation, aiming at minimizing the total cost of power generation. It is essential to monitor wide range of activities and practices of UC necessary to determine the operating plan of generating units. The UC problem is particularly crucial, when the behavior of loads at every hour interval, is oscillatory and with different operational constraints and environments. Many works have been proposed, with different optimization methods to solve the UC problem. This paper gives a detailed review of the evolutionary optimization techniques, employed for solving UC problem, by collecting them from lots of peer reviewed published papers. This review was carried out under many sections, based on various evolutionary optimization techniques, to help new researchers, dealing with modern UC problem solutions, under different situations of power system.

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“…Table 3 shows the successful parameters for PSO, FA algorithms reported by other researchers. 32,43,51 In the present work, for fair comparison, the same parameters were selected and tested with the proposed Garra Rufa method.…”

Section: Famentioning

confidence: 99%

Garra Rufa‐inspired optimization technique

Jaber¹,

Abdulbari

Shalash³

et al. 2020

Int J Intell Syst

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Natural selection has inspired researchers to develop and apply several intelligent optimization techniques in the past few decades. Generally, in artificial intelligence optimization, the particles follow a local or global best particle until finding an acceptable solution. In well‐ developed optimization techniques, such as swarm optimization (PSO) and the firefly algorithm (FA), getting around the initial optimal value of the group and randomly checking the effect of the surrounding points may lead to a better solution than the initial optimal value. The present work was inspired by the fascinating movement of Garra Rufa fish between two immersed legs during a regular “fish massage session.” A new optimization approach is proposed and modeled based on the movements of Garra Rufa fish, in which the particles are separated into groups, and the best optimal value leads each group for the group. Also, some of these particles are allowed to change groups depending on the fitness of the leaders of the groups. The suggested strategy is then compared with PSO and FA using multiple test optimization functions, such as the Ackley, Hartmann, Michalewicz, Shubert, Easom, Bohachevsky, and Rastrigin functions. Also, a multiobjective real issue in power system is tested using the proposed methods where the objectives were cumulative voltage deviation and power losses of three weight sets during the selection allocation of distribution generators. The results show that the proposed method provides good data and greater convergence to the optimal point compared with the classical methods for most of the functions tested.

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Unit commitment based on modified firefly algorithm

Cited by 20 publications

References 21 publications

Evolutionary algorithm solution for economic dispatch problems

Evolutionary algorithm solution for economic dispatch problems

A Comprehensive Review on Evolutionary Optimization Techniques Applied for Unit Commitment Problem

Garra Rufa‐inspired optimization technique

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