Implementation of flower pollination algorithm for solving economic load dispatch and combined economic emission dispatch problems in power systems

Abdelaziz, Almoataz Y.; Ali, E. S.; Elazim, S.M. Abd

doi:10.1016/j.energy.2016.02.041

Cited by 158 publications

(62 citation statements)

References 54 publications

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“…The ant lion optimization algorithm (ALOA) [31] was also a new method with few applications, and the method in the study has not shown its potential search ability persuasively due to simple test employment. In addition, many new methods have been developed for solving the problem such as modified symbiotic organisms search algorithm (MSOSA) [32], mine blast algorithm (MBA) [33], clonal algorithm (CA) [34], mathematical programming algorithm (MPA) [35], improved quantuminspired evolutionary algorithm (IQIEA) [36], cuckoo optimization algorithm (COA) [37], improved colliding bodies optimization algorithm (ICBOA) [38], flower pollination algorithm (FPA) [39], natural updated harmony search (NUHS) [40], lightning flash algorithm (LFA) [41,42], moth swarm algorithm (MSA) [43], and orthogonal learning competitive swarm optimization algorithm (OLCSOA) [44]. [45].…”

Section: Introductionmentioning

confidence: 99%

Improved Firefly Algorithm: A Novel Method for Optimal Operation of Thermal Generating Units

2018

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This paper presents a novel improved firefly algorithm (IFA) to deal the problem of the optimal operation of thermal generating units (OOTGU) with the purpose of reducing the total electricity generation fuel cost. The proposed IFA is developed based on combining three improvements. The first is to be based on the radius between two solutions, the second is updated step size for each considered solution based on different new equations, and the third is to slightly modify a formula producing new solutions by using normally distributed random numbers and canceling uniform random numbers of conventional firefly algorithm (FA). The effect of each proposed improvement on IFA is investigated by executing five benchmark functions and two different systems. The performance of IFA is investigated on six other study cases consisting of different types of objective function and complex level of constraints. The objective function considers single fuel with quadratic form and nonconvex form, and multifuels with the sum of several quadratic and nonconvex functions while a set of constraints taken into account are power loss, prohibited zone, ramp rate limit, spinning reserve, and all constraints in transmission power networks. The obtained results indicate the proposed improvements in terms of high optimal solution quality, stabilization of search ability, and fast convergence compared with FA. In addition, the comparisons with other methods also lead to a conclusion that the proposed method is a very promising optimization tool for systems with quadratic fuel cost function and with complicated constraints.

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Section: Introductionmentioning

confidence: 99%

Improved Firefly Algorithm: A Novel Method for Optimal Operation of Thermal Generating Units

2018

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“…In the literature [11], researchers use it to find out the optimal parameters of a static VAR compensator. In the literature [12], combined economic emission dispatch problem in power systems is suggested to be solved by the pollination algorithm.…”

Section: Multi-objective Optimization Methods Pollination Algorithmmentioning

confidence: 99%

Multi-objective Optimization of Transmission Line Annual Maintenance Scheduling Based on Flower Pollination Algorithm

Xianyong¹,

Quanyu²

2018

dtcse

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The optimization of the annual maintenance scheduling is a multidimensional and nonlinear integer optimization problem. In this paper, the annual maintenance scheduling model is established with the minimum cost of maintenance and the expected minimum power supply, and the pollination algorithm based on e constraint is proposed. By obtaining the Pareto optimal solutions and comparing the economic and reliability objectives of the optimization problem of the maintenance scheduling, the proposed algorithm can effectively improve the solution precision of the multiobjective optimization problem and the distribution of the optimal solution. Based on the annual inspection scheduling of 12 transmission lines in a region, this paper compares the proposed algorithm with the Differential Evolution Flower Pollination Algorithm with Time Variant Factor (TVDFPA) and the Simulated Annealing Pollination Algorithm (SFPA). Based on the result, ε-constrained algorithm is superior to the other two in performance and calculation time.

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“…Further, it is also forced to tolerate the dimensionality problem especially in case of large-scale power systems. 14 Flower pollination algorithm (FPA) as suggested in the literature 15 has only one key parameter for tuning the algorithm. 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.…”

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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Implementation of flower pollination algorithm for solving economic load dispatch and combined economic emission dispatch problems in power systems

Cited by 158 publications

References 54 publications

Improved Firefly Algorithm: A Novel Method for Optimal Operation of Thermal Generating Units

Improved Firefly Algorithm: A Novel Method for Optimal Operation of Thermal Generating Units

Multi-objective Optimization of Transmission Line Annual Maintenance Scheduling Based on Flower Pollination Algorithm

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

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