Short-term scheduling of hydrothermal power system with cascaded reservoirs by using modified differential evolution

Lakshminarasimman, L.; Subramanian, S.

doi:10.1049/ip-gtd:20050407

Cited by 194 publications

(75 citation statements)

References 14 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 summarized in Table 9. The cost obtained from modified differential evolution (MDE) [10], improved particle swarm optimization (IPSO) [13], teaching learning based optimization (TLBO) [16], improved fast evolutionary programming (IFEP) [9] and genetic algorithm (GA) [7] methods are also shown in Table 9. The cost convergence characteristic obtained from proposed ODE and DE is shown in Fig.…”

Section: Casementioning

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: Casementioning

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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“…The main body of the algorithm takes four or five lines of code in any programming language. Despite its simplicity, the gross performance of DE in terms of accuracy, convergence rate and robustness makes it attractive for applications to various real-world optimization problems [10][11][12], where finding an approximate solution in a reasonable amount of computational time is of considerable importance. The spatial complexity of DE is lower than that of some highly competitive real parameter optimizers.…”

Section: Modern Heuristic Optimization Algorithmsmentioning

confidence: 99%

A review on applications of heuristic optimization algorithms for optimal power flow in modern power systems

Ming

Wan

2014

J. Mod. Power Syst. Clean Energy

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Optimal power flow (OPF) is one of the key tools for optimal operation and planning of modern power systems. Due to the high complexity with continuous and discrete control variables, modern heuristic optimization algorithms (HOAs) have been widely employed for the solution of OPF. This paper provides an overview of the latest applications of advanced HOAs in OPF problems. The most frequently applied HOAs for solving the OPF problem in recent years are covered and briefly introduced, including genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), and evolutionary programming (EP), etc.

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“…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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Short-term scheduling of hydrothermal power system with cascaded reservoirs by using modified differential evolution

Cited by 194 publications

References 14 publications

Opposition-based differential evolution for hydrothermal power system

Opposition-based differential evolution for hydrothermal power system

A review on applications of heuristic optimization algorithms for optimal power flow in modern power systems

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

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