Artificial bee colony optimization for economic dispatch with valve point effect

Labbi, Yacine; Attous, Djilani Ben; Mahdad, Belkacem

doi:10.1007/s11708-014-0316-8

Cited by 33 publications

(10 citation statements)

References 30 publications

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“…Numerical results manifested that the RCGA held higher operation efficiency than binarycoded genetic algorithm (BCGA). Considering the VPE, artificial bee colony (ABC) as a novel neural network algorithm was adopted to deal with ED problem, which proved the validity of the proposed approach in multifarious test system [7].…”

Section: Introductionmentioning

confidence: 92%

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Recurrent Neural Network for Nonconvex Economic Emission Dispatch

Wang

Huang

et al. 2021

Journal of Modern Power Systems and Clean Energy

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In this paper, an economic emission dispatch (EED) model is developed to reduce fuel cost and environmental pollution emissions. Considering the development of new energy sources in recent years, the EED problem involves thermal units with the valve point effect and WTs. Meanwhile, it complies with demand constraint and generator capacity constraints. A recurrent neural network (RNN) is proposed to search for local optimal solution of the introduced nonconvex EED problem. The optimality and convergence of the proposed dynamic model are given. The RNN algorithm is verified on a power generation system for the optimization of scheduling and minimization of total cost. Moreover, a particle swarm optimization (PSO) algorithm is compared with RNN under the same problematic frame. Numerical simulation results demonstrate that the optimal scheduling given by RNN is more precise and has lower total cost than PSO. In addition, the dynamic variation of power load demand is considered and the power distribution of eight generators during 12 time periods is depicted.

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Section: Introductionmentioning

confidence: 92%

“…where Ω is the projection object and X o is an abstract projection variable. At the same time, the inequality constraint ( 12) is equivalent to the demand constraint (7).…”

Section: Algorithm Model Descriptionmentioning

confidence: 99%

Recurrent Neural Network for Nonconvex Economic Emission Dispatch

Wang

Huang

et al. 2021

Journal of Modern Power Systems and Clean Energy

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“…In [1], a differential evolution-based technique has been used to solve the DEEDP where a fuzzy-based method has been employed to extract the optimal solution. Authors in [5] utilized the artificial bee colony algorithm to solve the EDP with VPLEs. Unfortunately, the environmental impact of thermal units has not been considered.…”

Section: Introductionmentioning

confidence: 99%

Teaching-Learning-Based Optimization Algorithm for the Combined Dynamic Economic Environmental Dispatch Problem

Alshammari

2020

Eng. Technol. Appl. Sci. Res.

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The Dynamic Economic Environmental Dispatch Problem (DEEDP) is a major issue in power system control. It aims to find the optimum schedule of the power output of thermal units in order to meet the required load at the lowest cost and emission of harmful gases. Several constraints, such as generation limits, valve point loading effects, prohibited operating zones, and ramp rate limits, can be considered. In this paper, a method based on Teaching-Learning-Based Optimization (TLBO) is proposed for dealing with the DEEDP problem where all aforementioned constraints are considered. To investigate the effectiveness of the proposed method for solving this discontinuous and nonlinear problem, the ten-unit system under four cases is used. The obtained results are compared with those obtained by other metaheuristic techniques. The comparison of the simulation results shows that the proposed technique has good performance.

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“…Recently, metaheuristic techniques, such as evolutionary programming, genetic algorithm, simulated annealing, harmony search, differential evolution (DE), bacteria foraging optimization, ant colony optimization, artificial bee colony, and particle swarm optimization (PSO), have been successfully applied to solve ELD problems due to their ability to handle complex optimization problems. In addition, some hybridization of these methods has been used to solve ELD problems effectively .…”

Section: Introductionmentioning

confidence: 99%

Nonconvex/nonsmooth economic load dispatch using modified time‐varying particle swarm optimization

Parouha

2019

Computational Intelligence

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In this paper, a modified time‐varying particle swarm optimization (MTVPSO) is proposed for solving nonconvex economic load dispatch problems. It is a variant of the traditional particle swarm optimization (PSO) algorithm. In an MTVPSO, novel acceleration coefficients for cognitive and social components are presented as linear time‐varying parameters in the velocity update equation of the PSO algorithm. In the early stages of the optimization process, it improves the global search capability of particles and directs the global optima at the end stage. Additionally, a linearly decreased inertia weight is introduced in an MTVPSO, instead of a fixed constant value, which helps improve the diversity of the population. Through this modification mechanism in PSO, the proposed algorithm has a higher probability of avoiding local optima, and it is likely to find global optima more quickly. Six complex benchmark functions have been used to validate the effectiveness of the proposed algorithm. Furthermore, to demonstrate its efficiency, feasibility, and fastness, six different cases (3‐, 6‐, 13‐, 15‐, and 40‐unit systems and one large‐scale Korean power 140‐unit system) of the economic load dispatch problem are solved by an MTVPSO. The results of the proposed algorithm have been compared with state‐of‐the‐art algorithms. It was found that the proposed MTVPSO can deliver better results in terms of solution quality, convergence characteristics, and robustness.

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Artificial bee colony optimization for economic dispatch with valve point effect

Cited by 33 publications

References 30 publications

Recurrent Neural Network for Nonconvex Economic Emission Dispatch

Recurrent Neural Network for Nonconvex Economic Emission Dispatch

Teaching-Learning-Based Optimization Algorithm for the Combined Dynamic Economic Environmental Dispatch Problem

Nonconvex/nonsmooth economic load dispatch using modified time‐varying particle swarm optimization

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