Optimal power flow using moth swarm algorithm

Mohamed, Al-Attar Ali; Mossa, Mahmoud A.; El-Gaafary, Ahmed A. M.; Hemeida, Ashraf Mohamed

doi:10.1016/j.epsr.2016.09.025

Cited by 405 publications

(364 citation statements)

References 44 publications

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“…The results of minimum cost, mean cost, worst cost and N FES , and the best cost improvement of the proposed method compared to others are reported in Table 14. It can be seen from BCI that MSA [43] is the best method with the best cost improvement of 0.93% compared to the proposed method. On the other hand, the proposed method can improve the best cost from 1.7% (compared to COA) to 10.08% (compared to PSO); meanwhile, its N FES is equal to N FES of PSO and PG-CF-PSO, and much lower than that of all remaining methods.…”

Section: Comparisons Of Test Casementioning

confidence: 96%

“…The ant lion optimization algorithm (ALOA) [31] was also a new method with few applications, and the method in the study has not shown its potential search ability persuasively due to simple test employment. In addition, many new methods have been developed for solving the problem such as modified symbiotic organisms search algorithm (MSOSA) [32], mine blast algorithm (MBA) [33], clonal algorithm (CA) [34], mathematical programming algorithm (MPA) [35], improved quantuminspired evolutionary algorithm (IQIEA) [36], cuckoo optimization algorithm (COA) [37], improved colliding bodies optimization algorithm (ICBOA) [38], flower pollination algorithm (FPA) [39], natural updated harmony search (NUHS) [40], lightning flash algorithm (LFA) [41,42], moth swarm algorithm (MSA) [43], and orthogonal learning competitive swarm optimization algorithm (OLCSOA) [44]. [45].…”

Section: Introductionmentioning

confidence: 99%

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Improved Firefly Algorithm: A Novel Method for Optimal Operation of Thermal Generating Units

2018

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This paper presents a novel improved firefly algorithm (IFA) to deal the problem of the optimal operation of thermal generating units (OOTGU) with the purpose of reducing the total electricity generation fuel cost. The proposed IFA is developed based on combining three improvements. The first is to be based on the radius between two solutions, the second is updated step size for each considered solution based on different new equations, and the third is to slightly modify a formula producing new solutions by using normally distributed random numbers and canceling uniform random numbers of conventional firefly algorithm (FA). The effect of each proposed improvement on IFA is investigated by executing five benchmark functions and two different systems. The performance of IFA is investigated on six other study cases consisting of different types of objective function and complex level of constraints. The objective function considers single fuel with quadratic form and nonconvex form, and multifuels with the sum of several quadratic and nonconvex functions while a set of constraints taken into account are power loss, prohibited zone, ramp rate limit, spinning reserve, and all constraints in transmission power networks. The obtained results indicate the proposed improvements in terms of high optimal solution quality, stabilization of search ability, and fast convergence compared with FA. In addition, the comparisons with other methods also lead to a conclusion that the proposed method is a very promising optimization tool for systems with quadratic fuel cost function and with complicated constraints.

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Section: Comparisons Of Test Casementioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Improved Firefly Algorithm: A Novel Method for Optimal Operation of Thermal Generating Units

2018

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“…The key variables corresponding to the best fitness function yielded by the proposed method for case 3 are given in Appendix A. [18] 799.56 --6000 HIGA [19] 799.56 799.6497 0.0406 4560 HIGA-BM [20] 800.0435 800.122 0.0385 12,000 DE [21] 801.23 801.282 0.0663 -DE [22] 799.2891 --25,000 PSO [23] 800.41 ---EADPSO [24] 800.2276 800.2625 0.0303 12,500 BBOA [26] 799.1116 799.1985 -10,000 (15,000) ARCBBOA [27] 800.5159 800.6412 -10,000 TLBO [28] 800.7257 --25,000 ABCA [31] 800.6600 800.8715 --GWO [32] 799.5585 ---MELMA [33] 799.1821 ---MCBOA [34] 799.0353 --25,000 (45,000) MSA [35] 800 [22] 799.2891 --25,000 PSO [23] 800.41 ---EADPSO [24] 800.2276 800.2625 0.0303 12,500 BBOA [26] 799.1116 799.1985 -10,000 (15,000) ARCBBOA [27] 800.5159 800.6412 -10,000 TLBO [28] 800.7257 --25,000 ABCA [31] 800.6600 800.8715 --GWO [32] 799.5585 ---MELMA [33] 799.1821 ---MCBOA [34] 799.0353 --25,000 (45,000) MSA [35] 800 …”

Section: Case 3: Ieee-30 Bus Power Systemmentioning

confidence: 99%

“…Thus, the proposed method is more powerful and stronger than CCSA for searching optimal solutions. [22] 650.8224 --25,000 PSO [23] 647.69 647.73 --EADPSO [24] 629.4692 629.6470 0.1159 12,500 BBOA [26] 647.7437 647.7645 -10,000 (15,000) ABCA [31] 649.0855 654.0784 --MELMA [33] 649.6309 ---MCBOA [34] 645.1668 --25,000 (45,000) MSA [35] 646 …”

Section: Case 4: Ieee-57 Bus Power Systemmentioning

confidence: 99%

“…Most methods are the family of meta-heuristic algorithms in which conventional methods, modified methods, combination of two different methods, and hybrid methods have been developed widely. In fact, there have been a huge number of applied methods such as the integration of improved genetic algorithm and effective decoupled quadratic load flow (IGA-EDQLF) [18], hybrid IGA with incremental power flow model (HIGA) [19], HIGA with boundary method (HIGA-BM) [20], differential evolution [21,22], conventional PSO [23], Evolving ant direction particle swarm optimization (EADPSO) [24], PSO with Pseudo-Gradient and constriction factor (PG-CF-PSO) [25], Biogeography-based optimization algorithm (BBOAA) [26] and adaptive real-coded biogeography-based optimization algorithm (ARCBBOA) [27], teaching-learning-based optimization algorithm (TLBO) [28], improved TLBO (ITLBO) [29], gravitational search algorithm (GSA) [30], Artificial bee colony algorithm (ABCA) [31], Grey wolf optimizer (GWO) [32], modified electromagnetism-like mechanism algorithm (MELMA) [33], modified Colliding Bodies Optimization algorithm (MCBOA) [34], moth swarm algorithm (MSA) [35], improved imperialist competitive algorithm (IICA) [36], cuckoo optimization algorithm (COA) [37], Gaussian bare-bones imperialist competitive algorithm (GBBICA) [38], and mathematical programming algorithm (MPA) [39]. In [18][19][20], different variants of GA have been developed in which GA has been improved first and then combined with another method for handling constraints of OPF problem.…”

Section: Introductionmentioning

confidence: 99%

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Modified Cuckoo Search Algorithm: A Novel Method to Minimize the Fuel Cost

Nguyen

Quynh

et al. 2018

Energies

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Economic load dispatch (ELD) is an important optimization problem for operating and controlling modern power systems, and if ELD is effectively executed, power systems work stably and economically. The main objective of this paper is to develop a novel method to solve the ELD with the purpose of minimizing the total fuel cost of all available generating units while requirements are to satisfy all constraints regarding thermal units, generators, and transmission power networks. The proposed high performance cuckoo search algorithm (HPCSA) is developed from the efficient technique for the second new solution generation of conventional cuckoo search algorithm (CCSA), called adaptive mutation technique. This proposed technique diversifies the local search ability based on a new comparison criterion. The HPCSA is verified on difference systems under special conditions, namely the 10-unit system with multi fuels, 15-unit system considering prohibited operating zones, and three IEEE systems with 30, 57, and 118 buses considering transmission power network constraints. The specific evaluation of the HPCSA is compared to that of Lagrange optimization-based methods (LMS), neural network-based methods (NNMS), CCSA, and other popular methods such as Particle swarm optimization (PSO) variants, Differential evolution (DE) variants, Genetic Algorithm (GA) variants, and state-of-the-art methods. In comparison with CCSA, the proposed method is always more effective and more robust since the proposed method can find most solutions with better quality and faster convergence speed. In comparison with LMS and NNMS, the proposed method can also find solutions with approximate or equal quality. In comparison with popular methods and state-of-the-art methods, the proposed method has more potential since it can reach faster convergence to valid solutions with approximate or better quality. Consequently, it can be concluded that the proposed HPCSA is an effective optimization tool for dealing with ELD problems.

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Optimal power flow solution incorporating a simplified UPFC model using lightning attachment procedure optimization

Taher¹,

Kamel

Ebeed

2019

Int Trans Electr Energ Syst

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Summary Unified power flow controller (UPFC) is utilized to regulate the bus voltage as well as the power flow through a power system. A solution of optimal power flow (OPF) problems with UPFC is a crucial and complex task due to the required modifications for considering the parameters of UPFC. This work proposes a simplified UPFC modeling into an OPF code in order to avoid the programming complexity. Moreover, the solution of an OPF problem with a UPFC model is obtained by a recent physical‐based optimization technique called lightning attachment procedure optimization (LAPO). Different objective functions including minimizing fuel cost and fuel cost with valve point effect (VPE), reduction of emission, improvement of voltage profile, and improving the voltage stability index are considered. The proposed algorithm is tested using the standard IEEE 30‐bus system. The results obtained by the proposed algorithm are compared with those obtained by other optimization techniques. However, the obtained results verify the applicability of the simplified UPFC model in OPF.

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Optimal power flow using moth swarm algorithm

Cited by 405 publications

References 44 publications

Improved Firefly Algorithm: A Novel Method for Optimal Operation of Thermal Generating Units

Improved Firefly Algorithm: A Novel Method for Optimal Operation of Thermal Generating Units

Modified Cuckoo Search Algorithm: A Novel Method to Minimize the Fuel Cost

Optimal power flow solution incorporating a simplified UPFC model using lightning attachment procedure optimization

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