A new optimization algorithm for multi-objective Economic/Emission Dispatch

Niknam, Taher; Doagou‐Mojarrad, Hasan; Firouzi, Bahman Bahmani

doi:10.1016/j.ijepes.2012.10.001

Cited by 48 publications

(31 citation statements)

References 43 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%

“…Many research efforts were made for the EEDP. 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.…”

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%

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

Chiang¹

2018

dtcse

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“…Many research efforts were made for the COP [5][6][7][8][9][10][11]. Niknam et al [5] proposed an innovative tribe-modified differential evolution (Tribe-MDE) for the COP.…”

Section: Introductionmentioning

confidence: 99%

“…Niknam et al [5] proposed an innovative tribe-modified differential evolution (Tribe-MDE) for the COP. Rao and Vaisakh [6] provided a multi objective optimization approach based on adaptive clonal selection algorithm (ACSA) to solve the complex COP of thermal generators in power system.…”

Section: Introductionmentioning

confidence: 99%

A Case Study of Power Economic Emission Dispatch Using an Integrated Algorithm

Chiang¹

2017

dteees

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Abstract. This paper presents an integrated algorithm of power economic dispatch problem aiming to manage emissions for generating units of power systems. Differential evolution (DE) can efficiently search and actively explore solutions. The multiplier updating (MU) is introduced to avoid deforming the augmented Lagrange function and resulting in difficulty to solution searching. To handle the constrained optimal problem (COP), the ε-constraint technique is employed. The proposed approach integrates the ε-constraint technique, the DE, and the MU. The proposed method (DE-MU) has the merit of automatically adjusting the randomly given penalty to a proper value and requiring only a small-size population. Numerical results indicate that the proposed approach is superior to previous methods in solution quality.

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Multi-objective economic emission dispatch using interior search algorithm

Karthik

Parvathy

Rajagopalan

2018

Int Trans Electr Energ Syst

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Summary The purpose of the combined economic emission dispatch of generation in an electric power is to offer the finest schedule for the generating units which must run with both lesser fuel cost and emission levels concurrently thereby fulfilling the system equality and inequality constraints. This paper presents an interior search algorithm (ISA) for solving multi‐objective combined economic emission dispatch (CEED) problems. Simulation results obtained substantiate the efficiency of ISA algorithm in solving the CEED problems. The effectiveness of the proposed ISA is investigated on five various test systems which comprise three‐unit system, IEEE 30‐bus system, 10‐unit system, 20‐unit system, and Taiwan 40‐unit generating system. Simulation results and their comparison with other meta‐heuristic optimization techniques reveal that the proposed ISA is proficient in solving CEED problem of fossil fuel generators.

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A new optimization algorithm for multi-objective Economic/Emission Dispatch

Cited by 48 publications

References 43 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

A Case Study of Power Economic Emission Dispatch Using an Integrated Algorithm

Multi-objective economic emission dispatch using interior search algorithm

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