Hybrid Evolutionary Particle Swarm Optimization for the Coordination of Wind and Thermal Generation Dispatch

K. Dhayalini; S. Sathiyamoorthy; Asir Rajan C. Christober

doi:10.4028/www.scientific.net/AMM.573.684

Paper Titles

Voltage Stability Margin Assessment Using Multilayer Feed Forward Neural Network
p.661

Power Quality Enhancement in Microgrid
p.668

Design of Filters for Power Quality Improvement in Microgrids
p.673

A Novel Technique for Solving Hydrothermal Scheduling and Unit Commitment Using Artificial Immune System
p.679

Hybrid Evolutionary Particle Swarm Optimization for the Coordination of Wind and Thermal Generation Dispatch
p.684

Performance Analysis of Unified Power Quality Conditioner for Power Quality Improvement
p.690

Islanding and Non-Islanding Detection for Grid Connected Distribution System
p.696

Day and Night Utilization of Solar PV System Integrated with Stand Alone Wind Generator for Real and Reactive Power Management in Three Phase Unbalanced Load
p.702

A Hybrid Optimization Technique for Fault Classification in Power Transformer Using Dissolved Gas Analysis
p.708

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 573Hybrid Evolutionary Particle Swarm Optimization...

Hybrid Evolutionary Particle Swarm Optimization for the Coordination of Wind and Thermal Generation Dispatch

Abstract:

Development of better wind and thermal coordination dispatch is necessary to determine the optimal dispatch scheme that can integrate wind power reliably and efficiently. In this paper hybrid Evolutionary Programming (EP) and Particle Swarm Optimization (PSO) approach is utilized to coordinate the wind and thermal generation dispatch and to minimize the total production cost considering wind power generation and valve effect of thermal units. Numerical studies have been performed for three different test systems, i.e., six, thirteen and forty generating unit systems. The simulation results demonstrate the effectiveness of the proposed approach and shows the effect of wind power generation in reducing the total fuel.

You might also be interested in these eBooks

Advancements in Automation and Control Technologies

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volume 573)

Pages:

684-689

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.573.684

Citation:

Cite this paper

Online since:

June 2014

Authors:

K. Dhayalini*, S. Sathiyamoorthy, Asir Rajan C. Christober

Keywords:

Evolutionary Programming, Fuel Cost, Particle Swarm Optimization (PSO), Thermal Generation Dispatch, Wind

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Yue, C.D., Liu, C.M., Eric, M.L.L., A Transition toward a Sustainable Energy Future: Feasibility Assessment and Development Strategies of Wind Power in Taiwan, Energy Policy, Vol. 29, 2001, pp.951-963.

DOI: 10.1016/s0301-4215(01)00025-8

Google Scholar

[2] Chang, T.J., Wu, Y.T., Hsu, H.Y., Chu, C.R., Liao, C.M., Assessment of Wind Characteristics and Wind Turbine Characteristics in Taiwan, Renewable Energy, Vol. 28, 2003, pp.851-862.

DOI: 10.1016/s0960-1481(02)00184-2

Google Scholar

[3] Bakirtzis, A.G., Dokopoulos, P.S., Short Term Generation Scheduling in a Small Autonomous system with Unconventional Energy Sources, IEEE Transactions on Power Systems, Vol. 3, No. 3, 1988, pp.1230-1236.

DOI: 10.1109/59.14586

Google Scholar

[4] Wood, A.J. and Wollenberg, B.F., Power Generation Operation and Control 2nd edn. (Wiley, New York), (1996).

Google Scholar

[5] Mantawy, A.H., Abdel-Magid, Y.L., and Selim, S.Z., A simulated annealing algorithm for unit commitment, IEEE Transactions on Power Systems, Vol. 13, No. 1, 1998, p.197– 204.

DOI: 10.1109/59.651636

Google Scholar

[6] Juste, K.A., Kiat, H., Tanaka, E., and Hasegawa, J., An evolutionary programming solution to the unit commitment problem, IEEE Transactions on Power Systems, Vol. 14, No. 4, 1999, p.1452–1459.

DOI: 10.1109/59.801925

Google Scholar

[7] Bakirtzis, A.G., and Petridis, V., A genetic algorithm solution to the unit commitment problem, IEEE Transactions on Power Systems, Vol. 11, No. 1, 1996, p.83–92.

DOI: 10.1109/59.485989

Google Scholar

[8] C. Jiejin, X. Ma, L. Li and P. Haipeng, Chaotic particle swarm optimization for economic dispatch considering the generator constraints, Energy Conversion and management, vol. 48, no. 2, 2007, pp.645-653.

DOI: 10.1016/j.enconman.2006.05.020

Google Scholar

[9] N. Sinha, R. Chakrabarti and P. K. Chattopadhyay. Evolutionary Programming Techniques for Economic Load Dispatch, IEEE Trans. Evolutionary Computation, Vol. 1, no. 1, 2003, pp.83-94.

DOI: 10.1109/tevc.2002.806788

Google Scholar