An Adaptive Chaos Quantum-Behaved Particle Swarm Optimization Algorithm for Real-Time Dispatch in Power System with Wind Generation

Zhi Kui Wu; Chang Hong Deng; Yong Xiao; Wei Xing Zhao; Qiu Shi Xu

doi:10.4028/www.scientific.net/AMR.1070-1072.297

Paper Titles

Study on Transient Stability Simulation of District Grid Containing High Proportion of Wind Power
p.275

Vulnerability Assessment for Large-Scale Wind Farm
p.279

Wind Speed Change and Influence on Wind Energy in Recent 50 Years over Hexi Corridor
p.284

Economic Operation Research of Wind and Pumped Storage Co-Generation System
p.291

An Adaptive Chaos Quantum-Behaved Particle Swarm Optimization Algorithm for Real-Time Dispatch in Power System with Wind Generation
p.297

Assessment of Offshore Wind Resource in China Using CFSR Data
p.303

Detached Eddy Simulation of the Flow over Bolund
p.309

RBF Neural Network Wind Power Prediction Based on Chaos Theory
p.315

Time Sequence Coordination Frequency Control Strategy Design of Wind Farm
p.319

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 1070-1072An Adaptive Chaos Quantum-Behaved Particle Swarm...

An Adaptive Chaos Quantum-Behaved Particle Swarm Optimization Algorithm for Real-Time Dispatch in Power System with Wind Generation

Abstract:

A real-time dispatch (RTD) model for wind power incorporated power system aimed at maximizing wind power utilization and minimizing fuel cost is proposed in this paper. To cope with the prematurity and local convergence of conventional particle swarm optimization (PSO) algorithm, a novel adaptive chaos quantum-behaved particle swarm optimization (ACQPSO) algorithm is put forward. The adaptive inertia weight and chaotic perturbation mechanism are employed to improve the particle’s search efficiency. Numerical simulation on a 10 unit system with a wind farm demonstrates that the proposed model can maximize wind power utilization while ensuring the safe and economic operation of the power system. The proposed ACQPSO algorithm is of good convergence quality and the computation speed can meet the requirement of RTD.

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Periodical:

Advanced Materials Research (Volumes 1070-1072)

Pages:

297-302

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.1070-1072.297

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Online since:

December 2014

Authors:

Zhi Kui Wu, Chang Hong Deng*, Yong Xiao, Wei Xing Zhao, Qiu Shi Xu

Keywords:

Adaptive Inertia Weight, Chaos Quantum-Behaved Particle Swarm Optimization, Power System, Real-Time Dispatch, Wind Power

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* - Corresponding Author

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