An Optimal Dynamic Generation Scheduling for a Wind-Thermal Power System


In this paper, a dynamic generation scheduling model is formulated, aiming at minimizing the costs of power generation and taking into account the constraints of thermal power units and spinning reserve in wind power integrated systems. A dynamic solving method blended with particle swarm optimization algorithm is proposed. In this method, the solution space of the states of unit commitment is created and will be updated when the status of unit commitment changes in a period to meet the spinning reserve demand. The thermal unit operation constrains are inspected in adjacent time intervals to ensure all the states in the solution space effective. The particle swarm algorithm is applied in the procedure to optimize the load distribution of each unit commitment state. A case study in a simulation system is finally given to verify the feasibility and effectiveness of this dynamic optimization algorithm.

Share and Cite:

X. Li and D. Zhao, "An Optimal Dynamic Generation Scheduling for a Wind-Thermal Power System," Energy and Power Engineering, Vol. 5 No. 4B, 2013, pp. 1016-1021. doi: 10.4236/epe.2013.54B194.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] H. Y. Chen, J. F. Chen and X. Z. Duan, “Fuzzy Modeling and Optimization Algorithm on Dynamic Economic Dispatch in Wind Power Integrated System,” Automation of Electric Power Systems, Vol. 30, No. 2, 2010, pp. 22-26.
[2] M. L. Wang, B. M. Zhang and Q. Xia, “A Novel Economic Dispatching Algorithm with Unit Ramp Rate and Network Security Constraints,” Automation of Electric Power Systems, Vol. 24, No.10, 2000, pp. 32-37.
[3] Y. Z. Sun, J. Wu, G. J. Li and J. He, “Dynamic Economic Dispatch Considering Wind Power Penetration Based on Wind Speed Forecasting and Stochastic Programming,” Proceedings of the CSEE, Vol. 29, No. 4, 2009, pp. 23-32.
[4] T. Senjyu, “A Fast Technique for Unit Commitment Problem by Extended Priority List,” IEEE Transactions on Power Systems, Vol. 18, No. 2, 2003, pp. 882-888. doi:10.1109/TPWRS.2003.811000
[5] F. N. Lee, “The Application of Commitment Utilization Factor (UFC) to the Thermal Unit Commitment,” IEEE Transactions on Power Systems, Vol. 6, 1991, pp. 691-698. doi:10.1109/59.76714
[6] L. Y. Sun, Y. Zhang and C. W. Jiang, “A Solution to the Unit Commitment Problem Based on Matrix Real-coded Genetic Algorithm,” Proceedings of the CSEE, Vol. 26, No. 2, pp. 82-87, Feb. 2006.
[7] S. Chusanapiputt, D. Nualhong and S. Jantarang, “Unit Commitment by Selective Self-adaptive ACO with Relativity Pheromone Updating Approach,” Power Energy Conference, Vol. 13, No. 24, 2007, pp. 36-71.
[8] K. Han, J. Zhao and J. X. Qian, “A Closed-loop Particle Swarm Optimization Algorithm for Power System Unit Commitment,” Automation of Electric Power Systems, Vol. 33, No. 1, 2009, pp. 36-40.
[9] Y. W. Jiang, C. Chen and B. Y. Wen, “Particle Swarm Research of Stochastic Simulation for Unit Commitment in Wind Farms Integrated Power System,” Transactions Of China Electro Technical Society, Vol. 24, No. 6, 2009, pp. 129-137.
[10] R. Q. Li and Z. Qin, “The Optimization Operation of Unit Commitment by Considering System Reliability,” Modern Electric Power, Vol. 29, No. 2, 2012, pp. 44-49.
[11] Yang Xiuyuan, Xiao Yang and Chen Shuyong, “Wind Speed and Generated Power Forecasting in wind Farm,” Proceedings of the CSEE, vol. 25, No.11, pp. 1-5, June 2005.
[12] Chun-Lung Chen, “Optimal Wind-Thermal Generating Unit Commitment,” IEEE Transactions on energy Conversion, vol. 23, No. 1, Mar. 2008.doi:10.1109/TEC.2007.914188

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.