Artificial bee colony algorithm with dynamic population size to combined economic and emission dispatch problem

Aydın, Doğan; Özyön, Serdar; Yaşar, Celal; Liao, Tianjun

doi:10.1016/j.ijepes.2013.06.020

Cited by 161 publications

(71 citation statements)

References 40 publications

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“…As seen from the best solution of QPSO-MU listed in Table 1, the emission output is 0.194200 ton/h. It is observed that the best total cost (TC) utilizing QPSO-MU is 643.8737 $/h, which is much less than the best results previously reported in Tribe-MDE [3], ABCDP [4], CSS [5], MOACSA [6] and NSBF [7]. The equality constraint (10) of power balance and the expected emission limit (11) are fully satisfied.…”

Section: System Simulationsmentioning

confidence: 84%

“…So, the values of the best cost with F . Table 1 compares five computational results obtained from previous papers and the proposed QPSO-MU, Tribe-MDE [3], ABCDP [4], CSS [5], MOACSA [6] and NSBF [7]. As seen from the best solution of QPSO-MU listed in Table 1, the emission output is 0.194200 ton/h.…”

Section: System Simulationsmentioning

confidence: 98%

“…Niknam et al [3] proposed an innovative tribe-modified differential evolution (Tribe-MDE) for the EEDP. Aydin et al [4] presented an artificial bee colony algorithm with dynamic population size to combined economic and emission dispatch problem (ABCDP) for solving the EEDP. Őzyőn et al [5] developed a charged system search (CSS) algorithm to investigate the EEDP.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Quantum-behaved Particle Swarm Optimization for Economic/Emission Dispatch Problem of Power System

Chiang¹

2018

dtcse

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Abstract. This paper proposes a quantum-behaved particle swarm optimization with multiplier updating (QPSO-MU) for solving economic /emission dispatch problems (EEDP) of power system. The quantum-behaved particle swarm optimization (QPSO) has the ability to efficiently search and actively explore solutions. Multiplier updating (MU) is introduced to avoid deforming the augmented Lagrange function and resulting in difficulty to solution searching. The ε-constraint technique is employed to handle the EEDP. The proposed algorithm integrates the ε-constraint technique, QPSO, and the MU. The simulation using the proposed method is carried out on a 6-unit test system, and results are compared with that obtained using other different methods. Numerical results indicate that the proposed approach is superior to other methods in solution quality and computational burden.

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Section: System Simulationsmentioning

confidence: 84%

Section: System Simulationsmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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Quantum-behaved Particle Swarm Optimization for Economic/Emission Dispatch Problem of Power System

Chiang¹

2018

dtcse

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“…2,3 Aydin et al 2 introduced ABC with dynamic population to solve combined economic and emission dispatch problem. Jadhav et al 3 used gbest guided ABC to optimize the emission and overall cost of wind-thermal power system.…”

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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“…As the ABC algorithm is effective in determining the global minimum points of nonlinear and nonconvex problems, it has been applied to optimal power flow problems of AC and AC-DC power systems successfully in recent years [13][14][15][16][17][18][19][20][21]. In addition to this, up to now, a current-balancing approach has been used for solving the reactive power flow problem of MTDC systems.…”

Section: Introductionmentioning

confidence: 99%

A new approach for optimal reactive power flow of MTDC systems using the ABC algorithm

Kılıç¹,

Ayan²

2017

Turk J Elec Eng & Comp Sci

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This paper presents a new approach to optimize reactive power flow of multiterminal high voltage direct current (HVDC) systems. Successful application of two-terminal DC systems worldwide makes the use of multiterminal direct current (MTDC) systems more attractive. Due to the economic and technical advantages of HVDC technology, MTDC systems have been used extensively in recent years. In this study, the artificial bee colony (ABC) algorithm is used for solution of the optimal reactive power flow problem of MTDC systems. In opposition to the current-balancing method used in the literature, this study represents a new approach for DC system power flow calculations. The proposed approach is tested on a sample IEEE MTDC test system. The results by the proposed approach are compared with those reported in the literature. Thus, the applicability and the efficiency of this approach used together with the ABC algorithm are shown.

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Artificial bee colony algorithm with dynamic population size to combined economic and emission dispatch problem

Cited by 161 publications

References 40 publications

Quantum-behaved Particle Swarm Optimization for Economic/Emission Dispatch Problem of Power System

Quantum-behaved Particle Swarm Optimization for Economic/Emission Dispatch Problem of Power System

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

A new approach for optimal reactive power flow of MTDC systems using the ABC algorithm

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