Species-based Quantum Particle Swarm Optimization for economic load dispatch

Hosseinnezhad, Vahid; Rafiee, Mansour; Ahmadian, Mohammad Taghi; Ameli, Mohammad Taghi

doi:10.1016/j.ijepes.2014.05.066

Cited by 85 publications

(31 citation statements)

References 37 publications

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“…The minimum cost solution of 121411.5959346808$/hr was obtained. From analysing the data in the Table [2] it can be inferred that MTLBO produced a satisfactory solution and it has a better performance compared to the other methods like IABC-LS [9], QPSO [15], PSO [26] and SDE [7].…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Since it has become important to generate power from the generation stations with regards to the economical operation of the generation units, the proper planning and effective operation of generating units is necessary [1,2] .The selection of units to generate maximum power for economic operation is the most important task, as thermal units, consists of multi valve-steam turbines. Hence, the designing approach to Economic Dispatch (ED) must be done along with valve point effects.…”

Section: Introductionmentioning

confidence: 99%

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Economic dispatch considering valve point effects using modified teaching learning based optimisation

Sharmiladeve¹,

Geethanjali²

2015

2015 IEEE International Conference on Engineering and Technology (ICETECH)

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In this paper, Modified Teaching Learning BasedOptimisation is used to solve the Economic Dispatch problems of the generating units considering valve point loadings effects. The formulation of the objective function is carried out in such a way that the losses are neglected and a clarified solution is obtained from the generating units. A recently developed optimisation is the Teaching Learning Based Optimisation which operates on two different phases-teacher phase and learner phase. A modification is done in the Teaching Learning Based Optimisation and is used in this paper as Modified Teaching Learning Based Optimisation. Here, in addition to the two phases, an mutation phase is also introduced. In contrast to the other Optimisation methods this proposed method does not require any algorithm specific parameters, it does not depend on any tuning parameters of algorithm and it enables global optimum solution and also avoids premature convergence to local optima of the objective function. The proposed method finds an optimum solution, such that minimum fuel cost solution can be obtained with extraordinary convergence rates and high consistency. The proposed method is tested on standard IEEE bus with 13 generating unit system, 40 generating unit system with valve point effects. The effectiveness of this method is demonstrated by comparing the results with other optimization techniques. Also, the result confirms that this proposed method has a great potential in determining the optimum solution.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Economic dispatch considering valve point effects using modified teaching learning based optimisation

Sharmiladeve¹,

Geethanjali²

2015

2015 IEEE International Conference on Engineering and Technology (ICETECH)

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show abstract

“…It is seen that, the mean and the best cost obtained by the HGWO are the least of all, with the exception of the SQPSO. The mean and best cost in SQPSO are lower than for the HGWO since the power balance constraint is not satisfied in SQPSO, as mentioned in the last column of Table 4, page 318 of [49]. The total generation therein is less than the demand and loss by 0.1499 MW for SQPSO.…”

Section: Test Systemmentioning

confidence: 97%

Economic dispatch using hybrid grey wolf optimizer

Jayabarathi

Raghunathan

Adarsh

et al. 2016

Energy

316

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“…There is a chance to stuck in a local optimal point. Therefore, various modifications and hybridization of PSO such as New PSO with local random search (NPSO_LRS) [10], Adaptive PSO [11], Improved coordinated aggregationbased PSO [12], Improved PSO [13], Species-based quantum particle swarm [14] have been made to improve the efficiency of this algorithm. [15] have proposed Differential evolution (DE) based on population-based stochastic search technique.…”

Section: Introductionmentioning

confidence: 99%

Quasi-Oppositional Symbiotic Organisms Search Algorithm for Different Economic Load Dispatch Problems

2018

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In this paper, an effective meta-heuristic technique called Quasi-Oppositional Symbiotic Organisms Search is applied for solving non-convex economic dispatch problems. Symbiotic Organisms Search is a soft computing technique, inspired by organisms in the ecosystem. This technique is implemented for improving the solution quality in minimum time. In order to improve convergence rate, quasi-reflected numbers are used here instead of pseudo-random numbers. Different equality and inequality constraints such as transmission loss, load demand, prohibited operating zone, generator operating limits and boundary of ramp rate are considered here. Presence of multiple fuels and valve point are also considered in some cases. This algorithm is applied to four different test systems. Simulation results are compared with many recently developed optimization techniques to show the superiority and consistency of this method. Simulation results also show that the computational efficiency of this algorithm is much better than the other meta-heuristic methods available in the literature.

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Species-based Quantum Particle Swarm Optimization for economic load dispatch

Cited by 85 publications

References 37 publications

Economic dispatch considering valve point effects using modified teaching learning based optimisation

Economic dispatch considering valve point effects using modified teaching learning based optimisation

Economic dispatch using hybrid grey wolf optimizer

Quasi-Oppositional Symbiotic Organisms Search Algorithm for Different Economic Load Dispatch Problems

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