Short-term hydrothermal scheduling using clonal selection algorithm

Swain, Ramakrushna; Barisal, Ajit Kumar; Hota, Prakash Kumar; Chakrabarti, R.

doi:10.1016/j.ijepes.2010.11.016

Cited by 149 publications

(81 citation statements)

References 20 publications

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“…The best, average and worst cost and average CPU time among 100 runs of solutions obtained from proposed ODE and DE are shown in Table 11. The cost obtained from modified differential evolution (MDE) [10], clonal selection algorithm (CSA) [15] and teaching learning based optimization (TLBO) [16] is also shown in Table 11. The cost convergence characteristic obtained from proposed ODE and DE is shown in Fig.…”

Section: Test Systemmentioning

confidence: 99%

“…Recently, stochastic search algorithms such as simulated annealing (SA) [5], evolutionary programming (EP) [6], genetic algorithm (GA) [7,8], evolutionary programming technique [9], differential evolution (DE) [10][11][12], particle swarm optimization [13], artificial immune system [14], clonal selection algorithm [15] and teaching learning based optimization [16] have been successfully used to solve hydrothermal scheduling problem.…”

Section: Introductionmentioning

confidence: 99%

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Opposition-based differential evolution for hydrothermal power system

Pattanaik

Basu

Dash³

2017

Prot Control Mod Power Syst

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This paper presents opposition-based differential evolution to determine the optimal hourly schedule of power generation in a hydrothermal system. Differential evolution (DE) is a population-based stochastic parallel search evolutionary algorithm. Opposition-based differential evolution has been used here to improve the effectiveness and quality of the solution. The proposed opposition-based differential evolution (ODE) employs opposition-based learning (OBL) for population initialization and also for generation jumping. The effectiveness of the proposed method has been verified on two test problems, two fixed head hydrothermal test systems and three hydrothermal multi-reservoir cascaded hydroelectric test systems having prohibited operating zones and thermal units with valve point loading. The results of the proposed approach are compared with those obtained by other evolutionary methods. It is found that the proposed opposition-based differential evolution based approach is able to provide better solution.

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Section: Test Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Opposition-based differential evolution for hydrothermal power system

Pattanaik

Basu

Dash³

2017

Prot Control Mod Power Syst

View full text Add to dashboard Cite

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“…To run the program 20 times, the optimal fuel cost and the average CPU time of proposed CQPSO algorithm and other artificial intelligence algorithms, including MHDE [8], CSA [10] and QOTLBO [22] are given in Table 1. The symbol '-' means the respective value cannot be obtained according the original paper.…”

Section: Case 1: Value-point Effects Is Consideredmentioning

confidence: 99%

“…Typical algorithms such as evolutionary programming (EP) [6], genetic algorithm (GA) [7], differential evolution (DE) [8,9] clonal selection (CS) [10] and particle swarm optimization (PSO) [11] have obtained good effect. However, those algorithms are easy to trap into the local optimum and sensitive to initial point which may debase the solution quality as well as effectiveness.…”

Section: Introductionmentioning

confidence: 99%

A Chaotic Quantum Behaved Particle Swarm Optimization Algorithm for Short-term Hydrothermal Scheduling

Chen¹,

Huang²,

Zhi³

2017

TOEEJ

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This study proposes a novel chaotic quantum-behaved particle swarm optimization (CQPSO) algorithm for solving shortterm hydrothermal scheduling problem with a set of equality and inequality constraints. In the proposed method, chaotic local search technique is employed to enhance the local search capability and convergence rate of the algorithm. In addition, a novel constraint handling strategy is presented to deal with the complicated equality constrains and then ensures the feasibility and effectiveness of solution. A system including four hydro plants coupled hydraulically and three thermal plants has been tested by the proposed algorithm. The results are compared with particle swarm optimization (PSO), quantum-behaved particle swarm optimization (QPSO) and other population-based artificial intelligence algorithms considered. Comparison results reveal that the proposed method can cope with short-term hydrothermal scheduling problem and outperforms other evolutionary methods in the literature.

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“…In recent decades, several artificial intelligence algorithms, such as genetic algorithm (GA) [2][3], two-phase neural network [4], evolutionary programming technique (EP) [5], particle swarm optimization (PSO) [6][7], differential evolution [8][9], and clonal selection algorithm (CSA) [10] have been widely and successfully applied for solving the ST-CHTS problems where quadratic and/or nonconvex fuel cost function of thermal units are considered. Among the methods, GA is the worst one since it obtains very high fuel cost, high constraint violation and long execution time.…”

Section: Introductionmentioning

confidence: 99%