Gbest guided artificial bee colony algorithm for environmental/economic dispatch considering wind power

Jadhav, H. T.; Roy, Ranjit

doi:10.1016/j.eswa.2013.05.048

Cited by 92 publications

(69 citation statements)

References 82 publications

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“…The initial velocities are also generated in the same way for all the particles. Step 2: Compute penalty factor h j for all thermal power plants using Equation (12). These values are constant and hence do not require modification in the iteration process Step 3: Calculate the hydro power plant's output and apply the respective power inequality constraints.…”

Section: Simulation Resultsmentioning

confidence: 99%

Hydro-Thermal-Wind Generation Scheduling Considering Economic and Environmental Factors Using Heuristic Algorithms

Damodaran¹,

Kumar

2018

Energies

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Hydro-thermal-wind generation scheduling (HTWGS) with economic and environmental factors is a multi-objective complex nonlinear power system optimization problem with many equality and inequality constraints. The objective of the problem is to generate an hour-by-hour optimum schedule of hydro-thermal-wind power plants to attain the least emission of pollutants from thermal plants and a reduced generation cost of thermal and wind plants for a 24-h period, satisfying the system constraints. The paper presents a detailed framework of the HTWGS problem and proposes a modified particle swarm optimization (MPSO) algorithm for evolving a solution. The competency of selected heuristic algorithms, representing different heuristic groups, viz. the binary coded genetic algorithm (BCGA), particle swarm optimization (PSO), improved harmony search (IHS), and JAYA algorithm, for searching for an optimal solution to HTWGS considering economic and environmental factors was investigated in a trial system consisting of a multi-stream cascaded system with four reservoirs, three thermal plants, and two wind plants. Appropriate mathematical models were used for representing the water discharge, generation cost, and pollutant emission of respective power plants incorporated in the system. Statistical analysis was performed to check the consistency and reliability of the proposed algorithm. The simulation results indicated that the proposed MPSO algorithm provided a better solution to the problem of HTWGS, with a reduced generation cost and the least emission, when compared with the other heuristic algorithms considered.

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Section: Simulation Resultsmentioning

confidence: 99%

Hydro-Thermal-Wind Generation Scheduling Considering Economic and Environmental Factors Using Heuristic Algorithms

Damodaran¹,

Kumar

2018

Energies

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“…To validate the superiority, the simulation results of OKHA and KHA are compared with those obtained by using previously published methods of QPSO [60] and GABC [61] methods. The optimum generation value of different generators, fuel cost (FC), wind cost (WC) and total cost (TC) obtained by OKHA and KHA along with QPSO and GABC are for load demand of 1200 MW is listed in Fig.…”

Section: Test Systemmentioning

confidence: 97%

“…To accommodate with complex problem some hybrid algorithms are introduced such as quantum-inspired PSO (QPSO) [31], modified group search optimizer algorithm (MGSO) [32], oppositional BBO (OBBO) [33], quasi-oppositional BBO (QOBBO) [34], hybrid DE algorithm based on PSO (DE/ PSO) [35], Gbest guided ABC (GABC) [36], hybrid PSO and GSA (HPSO-GSA) [37] have been applied to solve the ELD problem. HPSO-GSA was successfully applied to some of the test systems such as standard IEEE 30-bus, 6, 10 and 40-unit system and confirms the effectiveness over some wellestablished algorithms.…”

Section: Introductionmentioning

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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“…Jadhav et al 3 used gbest guided ABC to optimize the emission and overall cost of wind-thermal power system. Many variants of PSO 4 and GSA 5 have also been developed to solve dispatch problems.…”

Section: Introductionmentioning

confidence: 99%

Operation management of daily economic dispatch using novel hybrid particle swarm optimization and gravitational search algorithm with hybrid mutation strategy

Wang

Huang

2017

Mod. Phys. Lett. B

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This paper presents a hybrid particle swarm optimization and gravitational search algorithm based on hybrid mutation strategy (HGSAPSO-M) to optimize economic dispatch (ED) including distributed generations (DGs) considering market-based energy pricing. A daily ED model was formulated and a hybrid mutation strategy was adopted in HGSAPSO-M. The hybrid mutation strategy includes two mutation operators, chaotic mutation, Gaussian mutation. The proposed algorithm was tested on IEEE-33 bus and results show that the approach is effective for this problem.

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Gbest guided artificial bee colony algorithm for environmental/economic dispatch considering wind power

Cited by 92 publications

References 82 publications

Hydro-Thermal-Wind Generation Scheduling Considering Economic and Environmental Factors Using Heuristic Algorithms

Hydro-Thermal-Wind Generation Scheduling Considering Economic and Environmental Factors Using Heuristic Algorithms

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

Operation management of daily economic dispatch using novel hybrid particle swarm optimization and gravitational search algorithm with hybrid mutation strategy

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