Shuffled differential evolution for economic dispatch with valve point loading effects

Reddy, A. Srinivasa; Vaisakh, K.

doi:10.1016/j.ijepes.2012.10.012

Cited by 77 publications

(15 citation statements)

References 70 publications

(105 reference statements)

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“…FA [18] 8234.07 25 100 5000 ITS [23] 8234.07 20 50 1000 MPSO [27] 8234.07 20 100 2000 FCASO-SQP [39] 8234.07 20 200 12,000 PSO-RDL [40] 8234.07 20 50 1000 SDE [42] 8234.07 ---MSFS 8234.07 10 10 300…”

Section: Methods Min Cost ($/H) N Pop N Iter N Germentioning

confidence: 99%

“…However, MSFS has only used 300 evaluations while most of the other methods have used many times of fitness evaluation. N Ger of SDE [42] cannot be calculated because it has not reported population and iterations. [17] 8344.60 500 300 150,000 CSA [22] 8344.59 ---ICSA [25] 8344.59 ---NSGA-II [32] 8344.60 500 20,000 10,000,000 MSFS 8344.59 3 15 135 Table 6.…”

Section: Test System 1 With 3 Unitsmentioning

confidence: 99%

“…Therefore, many researchers have chosen outstanding algorithms and combined them to create new algorithms with more promising results than the original algorithms. For this purpose, many articles have productively introduced and launched highly effective algorithms for the ELD problem, such as hybrid PSO with real-valued mutation (RVM-PSO) [35], combination of the differential evolution and particle swarm optimization algorithms (DEPSO) [36], hybrid PSO with gravitational search algorithm (HPSO-GSA) [37], modified shuffled frog leaping algorithm (MSFLA) [38], global best harmony search algorithm (GHS) [38], hybrid SFLA-GHS algorithm [38], combination of hybrid SFLA-GH and shuffled differential evolution (MSFLA/GHS/SFLA-GHS/SDE) [38], hybrid fuzzy adaptive chaotic ant swarm optimization (FCASO) algorithm and sequential quadratic programming (SQP) technique (FCASO-SQP) [39], a new hybridization of particle swarm optimization with dynamic linkage discovery (PSO-RDL) [40], hybrid ant colony optimization and real-coded genetic algorithm (GAAIP) [41] and shuffled differential evolution (SED) [42]. All of the previous methods showed that they were powerful and effective techniques that could deal with most of such difficulties of classical methods.…”

Section: Introductionmentioning

confidence: 99%

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A High-Performance Stochastic Fractal Search Algorithm for Optimal Generation Dispatch Problem

et al. 2019

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This paper proposes applications of a modified stochastic fractal search algorithm (MSFS) to solve the economic load dispatch problem (ELD) in which valve-point effects, prohibited operating zones, power losses in all conductors, multi-fuel sources and other constraints of power system are taken into consideration. The proposed method is first developed in the study by performing two modifications on two procedures of new solution generation from conventional stochastic fractal search (SFS). The first modification is used to change the strategy of producing new solutions of the first and the second update procedures while the second one is to newly update the worst solutions in the first update process and the best solutions in the second update process. These modifications have major influence on the solution search performance of the proposed method. All improvements of the proposed method can be illustrated by solving and analyzing results from various test systems with different system scales including 3-unit, 6-unit, 10-unit, and 20-unit systems. Comparison of results obtained by MSFS, SFS, and other existing methods indicates that the proposed MSFS method is more effective and robust than compared methods in terms of solution quality, high-quality solution search stability and convergence process. Consequently, the proposed method should be used as a very favorable optimization method for the ELD problem and it should be tried for other optimization problems in electrical engineering.

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Section: Methods Min Cost ($/H) N Pop N Iter N Germentioning

confidence: 99%

Section: Test System 1 With 3 Unitsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A High-Performance Stochastic Fractal Search Algorithm for Optimal Generation Dispatch Problem

et al. 2019

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“…Needless to say, when the WESs are incorporated into the problem model, the problem becomes more complicated. To overcome drawbacks of conventional methods, numerous intelligent optimisation techniques, such as genetic algorithms (GAs) [22], bacterial foraging algorithm [23], artificial bee colony (ABC) [24], particle swarm optimisation (PSO) [25], simulated annealing (SA) [26], and differential evolution (DE) [27] were introduced to solve the power dispatch problem.…”

Section: Introductionmentioning

confidence: 99%

Chaotic sine–cosine algorithm for chance‐constrained economic emission dispatch problem including wind energy

Guesmi

Farah

Ismail

et al. 2020

IET Renewable Power Generation

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One of the most effective approaches to reduce carbon emissions is the integration of renewable energy sources into electrical power networks. Currently, wind turbines are the fastest growing among all renewable sources. With the integration of wind farms into electrical grids, the economic emission dispatch (EED) problem is becoming more complicated due to the stochastic availability of wind energy. In this study, a new approach is proposed to solve the EED problem incorporating wind farms. The problem is formulated as a chance-constrained problem to deal with the stochastic characteristic of wind power. A novel chaotic sine-cosine algorithm (CSCA) is proposed to provide the optimal generation schedule to minimise simultaneously the generation cost and emission. Some weakness has been encountered in exploitation and exploration capabilities in standard sine cosine algorithm (SCA). Hence, the chaos is integrated into the original SCA to improve its performance. In addition, a new mutation strategy is added to the SCA. In this study, the new algorithm is based on three mutually exclusive equations. The new technique is applied on the 69-bus ten-unit and 40-unit test systems with and without wind energy. The results performed by CSCA are compared with those generated by other recent techniques.

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“…A hybrid methodology integrating bee colony optimization with sequential quadratic programming is proposed by Basu [29] for solving dynamic economic dispatch problem of generating units considering valve-point effects. In [30] an optimization methodology is proposed which is based on hybrid shuffled differential evolution algorithm which combines the benefits of shuffled frog leaping algorithm and differential evolution, to solve economic dispatch problem considering valve point loading effects. In [31] a solution for multi-objective economic dispatch problem with transmission losses is provided by semi-definite programming formulation.…”

Section: Introductionmentioning

confidence: 99%

Teaching learning based optimization for economic load dispatch problem considering valve point loading effect

Banerjee¹,

Maity²,

Chanda

2015

International Journal of Electrical Power & Energy Systems

132

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Shuffled differential evolution for economic dispatch with valve point loading effects

Cited by 77 publications

References 70 publications

A High-Performance Stochastic Fractal Search Algorithm for Optimal Generation Dispatch Problem

A High-Performance Stochastic Fractal Search Algorithm for Optimal Generation Dispatch Problem

Chaotic sine–cosine algorithm for chance‐constrained economic emission dispatch problem including wind energy

Teaching learning based optimization for economic load dispatch problem considering valve point loading effect

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