Economic load dispatch problems with valve-point loading using natural updated harmony search

Al-Betar, Mohammed Azmi; Awadallah, Mohammed A.; Khader, Ahamad Tajudin; Bolaji, Asaju La’aro; Almomani, Ammar

doi:10.1007/s00521-016-2611-2

Cited by 71 publications

(40 citation statements)

References 100 publications

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“…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%

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: Introductionmentioning

confidence: 99%

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

2018

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“…Bu da ekonomik dağıtım ile yapılmaktadır. Ekonomik dağıtım, sistemden talep edilen gücün, jeneratörlerin çalışma sınırları gibi kısıtlar altında, minimum maliyetle karşılanmasının planlanmasını amaç edinmektedir [1,2,7,8]. EDP ise doğrusal olmayan (non-lineer), eşitlik ve eşitsizlik kısıtlamaları altında, yakıt maliyetini minimum yapan bir optimizasyon problemidir [1-3, 7, 12].…”

Section: Bus Turkey Wind-thermal Power System)unclassified

“…Fakat bu geleneksel yöntemlerin çözümleri özellikle büyük sistemler için optimum sonuçtan çok uzak olmakta ve hesaplama süresi fazla olmaktadır [7]. Bundan dolayı ekonomik dağıtım problemlerinin optimum sonuca daha yakın, etkili ve hızlı çözümleri için yapay zekâ temelli sezgisel optimizasyon yöntemleri kullanılmaktadır [8]. EDP çözümünde kullanılan sezgisel optimizasyon yöntemlerinden bazıları şunlardır: Genetik algoritma (GA) [9], parçacık sürü optimizasyonu (PSO) [1], karınca koloni algoritması (KKA) [10], yapay arı koloni algoritması (YAK) [11], yerçekimsel arama algoritması (YAA) [12], öğretmeöğrenme tabanlı optimizasyon algoritması (ÖÖTO) [13].…”

Section: Gi̇ri̇ş (Introduction)unclassified

“…EDP çözümünde kullanılan sezgisel optimizasyon yöntemlerinden bazıları şunlardır: Genetik algoritma (GA) [9], parçacık sürü optimizasyonu (PSO) [1], karınca koloni algoritması (KKA) [10], yapay arı koloni algoritması (YAK) [11], yerçekimsel arama algoritması (YAA) [12], öğretmeöğrenme tabanlı optimizasyon algoritması (ÖÖTO) [13]. Her ne kadar bu yöntemler EDP'yi çözse bile temel olarak, bu yöntemler uzun hesaplama süresinden ve yerel minimuma takılma probleminden kolayca kurtulamamaktadırlar [8,14]. Bu yüzden sezgisel algoritmaların güçlü olduğu noktalarda birleştirilerek hibrit yöntemler elde edilmektedir ve EDP'ye uygulanmaktadır.…”

Section: Gi̇ri̇ş (Introduction)unclassified

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Modifiye hibrit optimizasyon yöntemi ile rüzgâr-termal güç sistemleri için ekonomik dağıtım probleminin çözümü

Tefek¹,

Uğuz²

2019

Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

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Proposed a Modified Hybrid Gravitational Search Algorithm-Teaching-Learning Optimization Based Method  Searching the solution quality in the proposed MHGT method compared to the standard GSA and TLBO methods  Implementation of the proposed MHGT method to a new 19-bus Turkey wind-thermal power system for the solution of In this study, modified hybrid gravitational search algorithm (GSA)-teaching-learning based optimization (TLBO) algorithm (MHGT) method has been developed to solve the economic dispatch problem of windthermal power systems. The proposed MHGT method was developed by modifying the global search superiority in GSA and powerful local search specialty in TLBO for the solution of constrained optimization problem. With the MHGT method, it is aimed to reach the global minimum result with the least number of iterations and to get rid of the local minimum. Figure A shows the GSA in the first search space. The second search space is based on the optimal result of the GSA algorithm. Then, TLBO run in this second search space. Figure A. Graphical representation of the MHGT method Purpose: The proposed MHGT method is aimed to solve the economic dispatch problem of 6 bus and Turkey-19 bus wind-thermal power system. Theory and Methods: In this study, firstly, equations of hybrid method were given and MHGT method was applied to benchmark test functions. Then the proposed MHGT method was applied to 6 bus and Turkey-19 bus wind-thermal power system. Results: The success of the proposed MHGT method in benchmark functions was determined by Wilcoxon signedrank test. The proposed MHGT method is wind-thermal power system of economic dispatch problem solving achievement is shown in the graphs and figures. Conclusion: It is concluded that the proposed MHGT method finds the solution in short execution time and less fuel cost with more reliably and more efficiently in terms of both fuel cost and execution time.

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“…The ELD optimization problem is in one of the difficult constraints-based optimization systems in the power sector that usually needs excessive computations because of the nature of the cost functions and inherent non-smooth properties. A number of studies have introduced a variety of optimization procedures for ELD problems with and without valve point loading effect (VPLE) based on conventional and recently introduced meta-heuristics schemes, such as Newton methods [4,5], genetic algorithms [6], biogeography-based optimization algorithms [7], teaching learning based optimization methods [8], grey-wolf optimization algorithms [9], ant lion optimization procedures [10], modified krill herd algorithms [11,12], natural updated harmony searches [13], improved differential evolution [14], mine blast algorithms [15], and crow-search algorithms [16].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Bio-Inspired Computational Heuristic Paradigm for Integrated Load Dispatch Problems Involving Stochastic Wind

et al. 2019

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In this research work, bio-inspired computational heuristic algorithms (BCHAs) integrated with active-set algorithms (ASA) were designed to study integrated economics load dispatch problems with valve point effects involving stochastic wind power. These BCHAs are developed through variants of genetic algorithms based on a different set of routines for reproduction operators in order to make exploration and exploitation in the entire search space for finding the global optima, while the ASA is used for rapid local refinements of the results. The designed schemes are estimated on different load dispatch systems consisting of a combination of thermal generating units and wind power plants with and without valve point loading effects. The accuracy, convergence, robustness and complexity of the proposed schemes has been examined through comparative studies based on a sufficiently large number of independent trails and their statistical observations in terms of different performance indices.

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Economic load dispatch problems with valve-point loading using natural updated harmony search

Cited by 71 publications

References 100 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

Modifiye hibrit optimizasyon yöntemi ile rüzgâr-termal güç sistemleri için ekonomik dağıtım probleminin çözümü

Hybrid Bio-Inspired Computational Heuristic Paradigm for Integrated Load Dispatch Problems Involving Stochastic Wind

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