Application of genetic algorithm to economic load dispatch with Lineflow constraints

Nanda, J.; Narayanan, Rangesan

doi:10.1016/s0142-0615(02)00002-9

Cited by 57 publications

(13 citation statements)

References 7 publications

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“…In general, the fossil fuel-based electricity generation methods discharge a lot of pollutants into the atmosphere such as nitrogen oxides, sulfur dioxides, and carbon dioxide. Evolutionary algorithms such as genetic algorithm (GA), 4 evolutionary programming (EP), 5 hybrid EP, LP, 6 and improved particle swarm optimization (IPSO) 7 have been used to solve economic load dispatch (ELD) problems with security constraints as alternative techniques to achieve the best solution when compared with conventional techniques. A number of methods were proposed in the literature 1,2 to minimize the atmospheric emissions.…”

Section: Introductionmentioning

confidence: 99%

Chaotic self‐adaptive interior search algorithm to solve combined economic emission dispatch problems with security constraints

Rajagopalan

Kasinathan²,

Karthik

et al. 2019

Int Trans Electr Energ Syst

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Summary The main goal behind the combined economic emission dispatch (CEED) is to reduce the costs incurred upon fuel and emission for the generating units available without any intention to violate the generator and security constraints. Hence, the CEED must be handled after considering two challenging goals such as the costs involved with emission and fuel. In this paper, chaotic self‐adaptive interior search algorithm (CSAISA) was proposed to solve the CEED problems, considering the nonlinear behavior of generators in terms of valve point effects, prohibited operating zones, and security constraints. The proposed algorithm was tested for its effectiveness using 11‐generating units (without security), IEEE‐30 bus system, and IEEE‐118 bus system with security constraints. The results of the proposed CSAISA were compared with interior search algorithm (ISA), harmony search algorithm (HSA), differential evolution (DE), particle swarm optimization (PSO), and genetic algorithm (GA). To conclude, the proposed CSAISA outperformed all other algorithms in terms of convergence speed, implementation time, and solution quality, which was tested using performance metrics.

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Section: Introductionmentioning

confidence: 99%

Chaotic self‐adaptive interior search algorithm to solve combined economic emission dispatch problems with security constraints

Rajagopalan

Kasinathan²,

Karthik

et al. 2019

Int Trans Electr Energ Syst

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“…A-loss coefficients when compared with Bloss coefficients are extremely robust and need not be reevaluated even for wide changes in the loading pattern [25]. In [27], A-loss coefficients were used to calculate transmission losses in ED. However, the use of A-loss coefficients in solving DED is not yet explored.…”

Section: Introductionmentioning

confidence: 99%

Gravitational search algorithm-based dynamic economic dispatch by estimating transmission system losses using A-loss coefficients

Hemparuva¹,

Simon²,

Sundareswaran³

et al. 2016

Turk J Elec Eng & Comp Sci

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Abstract:Dynamic economic dispatch (DED) is an important problem in power system generation, operation, planning, and control. The objective of the DED problem is to schedule power generation for the online units over a time horizon, satisfying the unit and ramp rate constraints. Here, valve point loading effects that cause nonsmoothness of the objective function is also considered while solving the DED. The accuracy of the solution not only depends on the optimal scheduling of generating units, but it also lies in accuracy while estimating transmission system losses. Generally, B-loss coefficients are used in estimating transmission losses. However, in the literature, A-loss coefficients are found to be at par with B-loss coefficients in estimating transmission system losses. Therefore, in this paper, the performance in estimating the transmission system losses using A-loss coefficients are investigated through the solution of the DED problem. Here, a recently evolved heuristic search technique called the gravitational search algorithm is used for solving the DED.The feasibility of the proposed method is tested and validated on standard benchmark test systems such as the IEEE 30-bus system, IEEE 39-bus system, and IEEE 118-bus system. All simulations are carried out using SCILAB 5.4 (www.scilab.org), which is open-source software.

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“…The evolutionary programming (EP) [6,7], differential evolution (DE) [8], simulated annealing (SA) [9], genetic algorithm (GA) [10,11], ant colony (AC), particle swarm optimization (PSO) [12], tabu search (TS) [13], and artificial bee colony (ABC) [14] were implemented to tackle the many ELD problems. These methods are not always guarantee to obtain a global optimal solution and they might be converged at local optimal solution.…”

Section: Introductionmentioning

confidence: 99%

Implementation of hybrid particle swarm optimization for combined Economic-Emission Load Dispatch Problem

Abdullah

Bakar

Rahim

et al. 2014

2014 IEEE 8th International Power Engineering and Optimization Conference (PEOCO2014)

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This paper presents the implementation of hybrid particle swarm optimization for solving Economic-Emission Load Dispatch Problem (EELD). Due to environmental issues, the environmental pollution releases by thermal power generation should be considered in power dispatch planning instead of minimizing the total fuel cost only. Significant emission reduction can be achieved by performing the emission power dispatch. In this study, the hybrid Evolutionary programming (EP) and Particle Swarm Optimization (PSO) named Evolutionary Particle Swarm Optimization (EPSO) is proposed. The effectiveness of the EPSO algorithm has been tested on the IEEE 30 bus system and the results obtained are compared with the other reported algorithms. The results also reveal the capability of the proposed EPSO for obtaining the best fuel cost and emission amount at shorter time compared to PSO.

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Application of genetic algorithm to economic load dispatch with Lineflow constraints

Cited by 57 publications

References 7 publications

Chaotic self‐adaptive interior search algorithm to solve combined economic emission dispatch problems with security constraints

Chaotic self‐adaptive interior search algorithm to solve combined economic emission dispatch problems with security constraints

Gravitational search algorithm-based dynamic economic dispatch by estimating transmission system losses using A-loss coefficients

Implementation of hybrid particle swarm optimization for combined Economic-Emission Load Dispatch Problem

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