Unit commitment problem solution using invasive weed optimization algorithm

Saravanan, B.; Vasudevan, E.R.; Kothari, D.P.

doi:10.1016/j.ijepes.2013.08.020

Cited by 81 publications

(19 citation statements)

References 19 publications

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“…This problem can also be attempted with more number of constraints, including multiple renewable energy sources. [51] 5460 544733 550193 Table 6. Validation of ICA on the wind, thermal system Method Cost ($) LR [25] 565825 SFLA [25] 563937.7 BFOA [25] 564842 GA [25] 565825 LR PSO [25] 518512.308 ICA 499092.…”

Section: Resultsmentioning

confidence: 99%

“…In literature, different solutions using numerical optimization techniques have been proposed to solve the unit commitment problem such as dynamic programming [26], priority list [27], Bender decomposition [28], mixed integer programming [29], exhaustive enumeration and branch and bound method [27,32] etc., Bio inspired techniques using genetic algorithm [33], particle swarm optimization (PSO) [34], bacterial foraging problem (BFOA) [35], ant colony optimization (ACO) [36] , simulated annealing [37], shuffled frog optimization algorithm (SFLA) [38], invasive weed optimisation [51], etc. The exhaustive enumeration method is a simple combination technique, suffers with high computation time [27].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Imperialistic Competitive Algorithm for Unit Commitment with Wind Generation

Sudhakar¹,

Balasubramanian²,

Padmanaban³

2018

IJIES

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Abstract:The integration of wind power to the grid is essential to meet the growing power demand and possess a challenge to unit commitment to meet the load demand at low cost for scheduling the generating units. A new imperialistic competitive algorithm (ICA) has been developed for the above mentioned unit commitment problem for solving deterministic and stochastic condition. This imperialistic competitive algorithm has been developed with constraints, including generation limits, start up and shut down cost, minimum up and downtime along with ramp constraint. The ICA has been studied initially on the 10 units 24 hour system and 40 units 24 hour system for the deterministic load. Later, a wind generator has been added to the 10 units 24 hour system with wind data for 24 hours and the results are compared with genetic algorithm (GA), particle swarm optimization (PSO), hybrid particle swarm optimisation (HPSO), shuffled frog leaping algorithm (SFLA), invasive weed optimization (IWO). The comparison result is presented in this paper and it shows that the ICA gives optimized fuel cost of $482246.2 which is less than the LR, GA, BFOA, SFLA, IWO for stochastic unit commitment.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Imperialistic Competitive Algorithm for Unit Commitment with Wind Generation

Sudhakar¹,

Balasubramanian²,

Padmanaban³

2018

IJIES

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“…LR method provides a fast solution but due to its dual nature, it may lead to slow convergence and suboptimal solutions. Therefore, metaheuristics techniques such as genetic algorithm (GA) , evolutionary programming (EP) , simulated annealing (SA) , particle swarm optimization (PSO) , differential evolution (DE) , bacterial foraging algorithm (BFA) , imperialistic competition algorithm (ICA) , harmony search algorithm (HSA) , binary gravitational search algorithm (BGSA) and invasive weed optimization have been proposed and are claimed to search near global optimal solution by satisfying complicated constraints easily. However, in order to obtain the global optimal solutions, computational time required by these methods increases for large‐scale UCP.…”

Section: Introductionmentioning

confidence: 99%

A modified binary artificial bee colony algorithm for ramp rate constrained unit commitment problem

Singhal

Naresh

Sharma

2015

Int. Trans. Electr. Energ. Syst.

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Summary This paper proposes a new approach based on modified binary artificial bee colony (MBABC) algorithm and dynamic economic dispatch (DED) method for unit commitment problem (UCP). MBABC algorithm is used for committing/decommitting the thermal units, while optimum dispatch solution is determined using DED method. Proposed MBABC algorithm utilizes a new mechanism based on the measure of dissimilarity between binary strings for generating the new binary solutions for UCP. Moreover, in MBABC algorithm, an intelligent scout bee phase is proposed that replaces the abandoned solution with the global best solution. The solution quality achieved by MBABC is enhanced by hybridizing the genetic crossover (GC) that provides the diversified search space. Performance of the proposed MBABC‐GC algorithm is tested up to 300 thermal units over 24‐hour time interval. The comparison of obtained results with other methods in the literature has confirmed the superiority of the MBABC‐GC algorithm in terms of production cost and robustness. Copyright © 2015 John Wiley & Sons, Ltd.

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“…So far, the academia has carried out numerous studies on algorithm design and how to improve the IWO.AsIWO algorithm has a simple structure with less parameters, fast convergence and good robustness, IWO and its improved algorithms have been widely applied in many fields of natural science and engineering science, such as flow shop scheduling [19], Unit commitment problem [20], model order reduction [21], path optimization problem [22], multi-objective optimization problem [23], [24], information processing [25], [26], design of antenna arrays [27], [28]. All this has evidenced the powerful advantages and potential of IWO.…”

Section: Introductionmentioning

confidence: 99%

GPU-based variation of parallel invasive weed optimization algorithm for 1000D functions

Ouyang

Liu

et al. 2014

2014 10th International Conference on Natural Computation (ICNC)

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Considering the problems of slow convergence and easily getting into local optimum of intelligent optimization algorithms in finding the optimal solution to complex highdimensional functions, we have proposed an improved invasive weed optimization (IIWO). Concrete adjustments include setting the newborn seeds per plant to a fixed number, changing the initial step and final step to adaptive one, and re-initializing the solution which exceeds the boundary value. Meanwhile, through applying the algorithm to the GPU platform, a parallel IIWO (PIIWO) based on GPU is obtained. The algorithm not only improves the convergence, but also strikes a balance between the global and local search capabilities. The simulation results of solving on the CEC' 2010 1000-dimensional (1000D) functions, have shown that, compared with other algorithms, our designed IIWO can yield better performance, faster convergence, higher accuracy and stronger robustness; whilst the PIIWO has significant speedup than the IIWO.

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Unit commitment problem solution using invasive weed optimization algorithm

Cited by 81 publications

References 19 publications

A Novel Imperialistic Competitive Algorithm for Unit Commitment with Wind Generation

A Novel Imperialistic Competitive Algorithm for Unit Commitment with Wind Generation

A modified binary artificial bee colony algorithm for ramp rate constrained unit commitment problem

GPU-based variation of parallel invasive weed optimization algorithm for 1000D functions

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