Economic load dispatch using krill herd algorithm

Mandal, Biplab; Roy, Provas Kumar; Mandal, Sanjoy

doi:10.1016/j.ijepes.2013.11.016

Cited by 188 publications

(85 citation statements)

References 62 publications

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“…Table 11 reports the comparison of the best cost and the value of N FES from methods. It can see that the proposed method can yield optimal solutions as good as others excluding FA with the worst results and the proposed method's N FES is also the lowest value excluding comparison to ALHN and EALHN, which are not Table 12 can see that KHA methods in [23] are the best ones with the surprised lowest BCI compared to the proposed method; however, the validation confirmation of solutions cannot be done due to the lack of optimal solution report in [23]. Compared to popular methods such as PSO, GA, TSA (Tabu search algorithm), and FA, the proposed method is better in terms of lower cost and lower N FES .…”

Section: Comparisons Of Test Casementioning

confidence: 84%

“…The observation of BCI can lead 14 Complexity to an evaluation that the proposed method is slightly better than most methods (the improvement is from 0.0002% to +0.1030%). KHA4 [23], OBKHA [24], and EMA [30] have the same cost as the proposed method, but α-MFA [53] has slightly lower cost than IFA. However, the recalculated cost of α-MFA [53] is $15445.46, which is higher than the cost of the proposed method.…”

Section: Comparison and Discussionmentioning

confidence: 99%

“…However, there have been more drawbacks such as higher number of control parameters, difficult task of selection of such factors, and complicated implementation when combining two or more methods. The different versions of krill herd algorithm (KHA) [23] have been used for solving different cases of the considered problem such as KHA without using genetic operation (KHA1), KHA using crossover operation (KHA2), KHA using mutation operation (KHA3), and KHA using crossover operation and mutation operation (KHA4). Several disadvantages of these KHA methods have been pointed out in [24] such as low convergence speed and low success rate.…”

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: 84%

Section: Comparison and Discussionmentioning

confidence: 99%

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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“…The motion induced is dynamically adjusted by the target swarm density, local swarm density and a repulsive swarm density. Mathematically the velocity of i-th krill is defined by [46,47]: …”

Section: Movement Affected By Other Krill Individualsmentioning

confidence: 99%

Opposition-based krill herd algorithm applied to economic load dispatch problem

Bulbul¹,

Pradhan²,

Roy³

et al. 2018

Ain Shams Engineering Journal

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Economic load dispatch (ELD) is the process of allocating the committed units such that the constraints imposed are satisfied and the production cost is minimized. This paper presents a novel and heuristic algorithm for solving complex ELD problem, by employing a comparatively new method named krill herd algorithm (OKHA). KHA is nature-inspired metaheuristics which mimics the herding behaviour of ocean krill individuals. In this article, KHA is combined with opposition based learning (OBL) to improve the convergence speed and accuracy of the basic KHA algorithm. The proposed approach is found to provide optimal results while working with several operational constraints in ELD and valve point loading. The effectiveness of the proposed method is examined and validated by carrying out numerical tests on five different standard systems. Comparing the numerical results with other well established methods affirms the proficiency and robustness of proposed algorithm over other existing methods. Ó 2016 Faculty of Engineering, Ain Shams University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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“…There has been plenty of research on the determination of optimal power flow (OPF) for various power system objectives, such as the generation cost, emissions, and transmission losses (see, for example, [1][2][3][4][5][6][7][8][9][10][11][12][13][14] for individual objectives, and [15][16][17][18][19][20][21][22][23][24][25] for single-area multi-objective systems). A number of approaches to the multi-area economic dispatch problem under various power system operating constraints are discussed in [26][27][28][29][30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Multi-area Multi-fuel Economic–Emission Dispatch Using a Generalized Unified Power Flow Controller Under Practical Constraints

Suresh

Sivanagaraju

Rao³

2014

Arab J Sci Eng

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This paper presents a novel optimization algorithm for the multi-area multi-fuel economic-emission dispatch problem and total power loss objective. The proposed method uses the uniform distribution to determine the control parameters and employs a two-stage initialization process. This enables various objectives to be optimized under practical constraints and device limits. We formulate a realistic generation cost that includes the cost of reactive power generation, shunt power injections, and total power losses, along with the conventional active power generation cost. A novel objective based on the concept of multi-fuel emissions makes the problem more realistic, and a generalized unified power flow controller (GUPFC) is considered. A detailed power injection model is developed for a GUPFC with two series converters, including switching losses of converters. A nondominated sorting methodology and particle swarm optimization algorithm are then used to solve the multi-objective problem on the standard IEEE-30 bus and real-time Indian-124 bus test systems.Keywords GUPFC power injection model · Multi-fuel active power cost · Multi-fuel reactive power cost · Multi-fuel emissions · GUPFC installation cost · NSUDTPSO

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Economic load dispatch using krill herd algorithm

Cited by 188 publications

References 62 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

Opposition-based krill herd algorithm applied to economic load dispatch problem

Multi-area Multi-fuel Economic–Emission Dispatch Using a Generalized Unified Power Flow Controller Under Practical Constraints

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