Skip to main content

A New Improved Particle Swarm Optimization Technique for Daily Economic Generation Scheduling of Cascaded Hydrothermal Systems

  • Conference paper
Swarm, Evolutionary, and Memetic Computing (SEMCCO 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6466))

Included in the following conference series:

  • 2567 Accesses

Abstract

Optimum scheduling of hydrothermal plants is an important task for economic operation of power systems. Many evolutionary techniques such as particle swarm optimization, differential evolution have been applied to solve these problems and found to perform in a better way in comparison with conventional optimization methods. But often these methods converge to a sub-optimal solution prematurely. This paper presents a new improved particle swarm optimization technique called self-organizing hierarchical particle swarm optimization technique with time-varying acceleration coefficients (SOHPSO_TVAC) for solving daily economic generation scheduling of hydrothermal systems to avoid premature convergence. The performance of the proposed method is demonstrated on a sample test system comprising of cascaded reservoirs. The results obtained by the proposed methods are compared with other methods. The results show that the proposed technique is capable of producing comparable results.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  • Catalão, J.P.S., Pousinho, H.M.I., Mendes, V.M.F.: Scheduling of head-dependent cascaded hydro systems: Mixed-integer quadratic programming approach. Energy Conversion & Management 51, 524–530 (2010)

    Article  Google Scholar 

  • Chang, S., Chen, C., Fong, I., Luh, P.B.: Hydroelectric generation scheduling with an ef-fective differential dynamic programming. IEEE Trans. PWRS 5(3), 737–743 (1990)

    Google Scholar 

  • Gil, E., Bustos, J., Rudnick, H.: Short-Term Hydrothermal Generation Scheduling Model Using a Genetic Algorithm. IEEE Transaction on Power Systems 18(4), 1256–1264 (2003)

    Article  Google Scholar 

  • Jiekang, W., Jianquan, Z., Guotong, C., Hongliang, Z.: A Hybrid Method for Optimal Scheduling of Short-Term Electric Power Generation of Cascaded Hydroelectric Plants Based on Particle Swarm Optimization and Chance-Constrained Programming. IEEE Transaction on Power Systems 23(4), 1570–1579 (2008)

    Article  Google Scholar 

  • Lakshminarasimman, L., Subramanian, S.: A modified hybrid differential evolution for short-term scheduling of hydrothermal power systems with cascaded reservoirs. Energy Conversion & Management 49, 2513–2521 (2008)

    Article  Google Scholar 

  • Park, J.B., Lee, K.S., Shin, J.R., Lee, K.Y.: A Particle swarm optimization for solving the economic dispatch with non-smooth cost functions. IEEE Transaction on Power Systems 20(1), 34–42 (2005)

    Article  Google Scholar 

  • Ratnaweera, A., Halgamuge, S.K., Watson, H.C.: Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans. Evol. Comput. 8(3), 240–255 (2004)

    Article  Google Scholar 

  • Wong, K.P., Wong, Y.W.: Short-term hydrothermal scheduling part 1: simulated annealing approach. In: IEE Proceedings Generation, Transmission and Distribution, vol. 141(5), pp. 497–501 (1994)

    Google Scholar 

  • Yu, B., Xiaohui Yuan, X., Wang, J.: Short-term hydro-thermal scheduling using particle swarm optimization method. Energy Conversion & Management 48, 1902–1908 (2007)

    Article  Google Scholar 

  • Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proc. IEEE Conf. Neural Networks (ICNN 1995), vol. IV, pp. 1942–1948 (1995)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Mandal, K.K., Tudu, B., Chakraborty, N. (2010). A New Improved Particle Swarm Optimization Technique for Daily Economic Generation Scheduling of Cascaded Hydrothermal Systems. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Dash, S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2010. Lecture Notes in Computer Science, vol 6466. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17563-3_80

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-17563-3_80

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17562-6

  • Online ISBN: 978-3-642-17563-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics