Economic dispatch using hybrid grey wolf optimizer

Jayabarathi, T.; Raghunathan, Trivellore E.; Adarsh, B.R.; Suganthan, Ponnuthurai Nagaratnam

doi:10.1016/j.energy.2016.05.105

Cited by 313 publications

(102 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…[9][10][11][12][13][14][15][16] The significant aim of the present work is to provide an efficient PEMFC model according to Amphlett equation model that simulates the practical performance of the cell at various operational circumstances. [31][32][33][34][35][36] So in this article, SBO is used to establish variables of PEMFC model. In the earlier decade, metaheuristic approaches developing as optimization issue have applied by many researchers.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal parameter identification for the proton exchange membrane fuel cell using Satin Bowerbird optimizer

2019

View full text Add to dashboard Cite

Summary This study proposes precise modeling for the proton exchange membrane fuel cells which present desirable advantages compared with other energy management systems. The presented model can be applied for the simulation of the actual behavior of the proton exchange membrane fuel cells such as the electrical, electrochemical, and mechanical. In the present literature, a newly presented optimizer namely Satin Bowerbird is implemented for the evaluation of the proton exchange membrane fuel cell performance criteria. The Satin Bowerbird optimizer is an evolutionary algorithm that imitates the mating process of the Bowerbirds in the mating season. The Satin Bowerbird optimizer is applied to the different commercial benchmark of proton exchange membrane fuel cell stack to assess the performance of the proposed algorithm. The statistical study is also carried out to show the superiority of the proposed method compared with other schemes. The standard deviation for the Satin Bowerbird optimizer is obtained 0.0941 which is the lowest value amongst the other well‐known approaches. Also, the lowest sum of squared error is calculated for the proposed algorithm. Moreover, the validation of the presented method is done with the experimental data which shows good agreement between the experimental and modeling data.

show abstract

Section: Introductionmentioning

confidence: 99%

“…The mentioned advantage of SBO method is the main reason of researcher to use in many problems of electrical engineering. [31][32][33][34][35][36] So in this article, SBO is used to establish variables of PEMFC model.…”

Section: Introductionmentioning

confidence: 99%

Optimal parameter identification for the proton exchange membrane fuel cell using Satin Bowerbird optimizer

2019

View full text Add to dashboard Cite

show abstract

“…24 One among the SI-based nature-inspired algorithm is the grey wolf optimization (GWO) 25 that follows the hunting mechanism of the grey wolves as a pack. The GWO algorithm is a proven optimization technique for diversified applications such as feature selection, 26 power systems for optimal load dispatch for conventional, 27 and multiobjective 28 problems. The GWO is also used in parameter estimation in biomedical 29,30 and seismic applications.…”

Section: Introductionmentioning

confidence: 99%

Metaheuristic‐based approach for state and process parameter prediction using hybrid grey wolf optimization

Sankaranarayanan

Sivakumaran

Radhakrishnan

et al. 2018

Asia-Pacific J Chem Eng

View full text Add to dashboard Cite

Metaheuristic-based optimization algorithms can be used to solve the complexities in estimating the parameters and states of a complex nonlinear process. In this work, a hybridized version of grey wolf optimizer is employed to solve the intricacies of a complex linear process using hybrid grey wolf optimization. The proposed algorithm is executed simultaneously in 2 steps: (a) predicting the parameters and states using grey wolf optimizer and (b) updating the predicted parameters using static modified Kalman-Bucy mechanism. The significance of the hybridization is that it improves the convergence rate towards an optimal solution and explores global optimum effectively over a complex search space. The performance of the proposed algorithm is tested through 10 typical benchmark functions and is compared with the parent algorithm, grey wolf optimizer, and conventional particle swarm optimization. Furthermore, the findings of the hybrid grey wolf optimization are compared with the outcomes of the conventional algorithms to establish the effectiveness of the proposed hybrid grey wolf optimization in process parameter estimation.

show abstract

“…In [25], the authors presented DSM techniques for the residential sector. Multiple homes with SM were considered for two-way communication between the consumer and utility.…”

Section: Related Workmentioning

confidence: 99%

“…Novel hybrid algorithm for electricity price and load forecasting in smart grids with demand-side management [25] Electricity price and load forecasting…”

Section: Mimomentioning

confidence: 99%

Efficient Power Scheduling in Smart Homes Using Hybrid Grey Wolf Differential Evolution Optimization Technique with Real Time and Critical Peak Pricing Schemes

et al. 2018

View full text Add to dashboard Cite

With the emergence of automated environments, energy demand by consumers is increasing rapidly. More than 80% of total electricity is being consumed in the residential sector. This brings a challenging task of maintaining the balance between demand and generation of electric power. In order to meet such challenges, a traditional grid is renovated by integrating two-way communication between the consumer and generation unit. To reduce electricity cost and peak load demand, demand side management (DSM) is modeled as an optimization problem, and the solution is obtained by applying meta-heuristic techniques with different pricing schemes. In this paper, an optimization technique, the hybrid gray wolf differential evolution (HGWDE), is proposed by merging enhanced differential evolution (EDE) and gray wolf optimization (GWO) scheme using real-time pricing (RTP) and critical peak pricing (CPP). Load shifting is performed from on-peak hours to off-peak hours depending on the electricity cost defined by the utility. However, there is a trade-off between user comfort and cost. To validate the performance of the proposed algorithm, simulations have been carried out in MATLAB. Results illustrate that using RTP, the peak to average ratio (PAR) is reduced to 53.02%, 29.02% and 26.55%, while the electricity bill is reduced to 12.81%, 12.012% and 12.95%, respectively, for the 15-, 30-and 60-min operational time interval (OTI). On the other hand, the PAR and electricity bill are reduced to 47.27%, 22.91%, 22% and 13.04%, 12%, 11.11% using the CPP tariff.

show abstract

Economic dispatch using hybrid grey wolf optimizer

Cited by 313 publications

References 55 publications

Optimal parameter identification for the proton exchange membrane fuel cell using Satin Bowerbird optimizer

Optimal parameter identification for the proton exchange membrane fuel cell using Satin Bowerbird optimizer

Metaheuristic‐based approach for state and process parameter prediction using hybrid grey wolf optimization

Efficient Power Scheduling in Smart Homes Using Hybrid Grey Wolf Differential Evolution Optimization Technique with Real Time and Critical Peak Pricing Schemes

Contact Info

Product

Resources

About