Skip to main content
SpringerLink
Log in

Hydropower Generation Optimization and Forecasting Using PSO

  • Conference paper
  • First Online:
Computational Intelligence in Data Mining

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 711))

Abstract

Deriving optimal operation rules for maximizing the hydropower generation in a multi-purpose reservoir is relatively challenging among the various other purposes such as irrigation and flood control. This paper addresses the optimal functioning of a multi-purpose reservoir for improving hydropower generation. Efficient bio-inspired optimization techniques were proposed for hydropower optimization and hydrological variables forecasting. A particle swarm optimization (PSO)-based methodology is proposed for maximal hydropower generation through optimal reservoir release policies of Aliyar reservoir, located in Coimbatore district of TamilNadu state in India. The reservoir release is also optimized by Global Solver LINGO and compared with PSO, and it is explored that PSO-based model is powerful in hydropower maximization. To handle the uncertain behavior of hydrologic variables, artificial neural networks model is also applied for forecasting reservoir inflow and hydropower generation. The results obtained through the optimal reservoir release patterns suggested in this work have shown that the Aliyar Mini Hydel Power Station has a huge potential in generating considerably more hydropower than the actual generation observed from the power plant over the past years.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. ADB.: Hydropower Development in India: A Sector Assessment. Asian Development Bank, Publication Stock No. 031607, Philippines (2007).

    Google Scholar 

  2. Alcigeimes, B., Celeste., Wilson, F., Curi., Rosires, C., Curi.: Implicit Stochastic Optimization For Deriving Reservoir Operating Rules In Semiarid Brazil. Operations Research. 29, 223–234 (2009).

    Google Scholar 

  3. Arunkumar, R., Jothiprakash, V.: Artificial Neural Network Models for Shivajisagar Lake Evaporation Prediction. National Journal on Chembiosys. 2(1), 35–42 (2011).

    Google Scholar 

  4. Arunkumar, R., Jothiprakash, V.: Multi-reservoir Optimization for Hydropower Generation Using NLP Technique. KSCE Journal of Civil Engineering. 18(1), 344–354 (2014).

    Google Scholar 

  5. Arunkumar, R., Jothiprakash, V.: Optimal Reservoir Operation for Hydropower Generation Using Non-Linear Programming Model. Journal of The Institution of Engineers, India Ser A. 93 (2), 111–120 (2012).

    Article  Google Scholar 

  6. Devamane, M.G., Jothiprakash, V., Mohan, S.: Non-linear Programming Model for Multipurpose Multi-reservoir Operation. Hydrology Journal. 29(3–4) (2006).

    Google Scholar 

  7. Ghimire, B.N.S., Reddy, M.J.: Optimal reservoir operation for hydropower production using particle swarm optimization and sustainability analysis of hydropower. ISH. J. Hydraul. Eng. 19(3), 196–210 (2013).

    Article  Google Scholar 

  8. Kiruthiga, D., Amudha, T.: Optimal Reservoir Release for Hydropower Generation Maximization Using Particle Swarm Optimization. Innovations in Bio-Inspired Computing and Applications, Advances in Intelligent Systems and Computing, Springer, 424, 577–585 (2015).

    Google Scholar 

  9. Loucks, D.P., Stedinger, J.R., Haith, D.A.: Water Resources Systems Planning and Analysis. Prentice Hall Inc, Englewood Cliffs, New Jersey (1981).

    Google Scholar 

  10. Nagesh, Kumar, D., Janga, Reddy, M.: Ant Colony Optimization for Multi-Purpose Reservoir Operation. Water Resources Management. 20, 879–898 (2006).

    Article  Google Scholar 

  11. Nagesh, Kumar, D., Janga, Reddy, M.: Multiobjective Differential Evolution with Application to Reservoir System Optimization. Journal of Computing in Civil Engineering. (2007).

    Google Scholar 

  12. Nagesh, Kumar, D., Janga, Reddy, M.: Performance Evaluation of Elitist-Mutated Multi-Objective Particle Swarm Optimization for Integrated Water Resources Management. Journal of Hydroinfomatics. 11.1, 79–88 (2009).

    Article  Google Scholar 

  13. Public Works Department: Aliyar Reservoir. Water Resource Organization, Pollachi Region, Coimbatore (2013).

    Google Scholar 

  14. Simonovic, S.P.: Reservoir System Analysis: Closing Gap Between Theory and Practice. Journal of Water Resources Planning and Management. 118(3), 262–280 (1992).

    Article  Google Scholar 

  15. Sreenivasan, K.R., Vedula, S.: Reservoir Operation for Hydropower Optimization: A Chance-Constrained Approach. Sadhana, 211(4), 503–510 (1995).

    Article  Google Scholar 

  16. Wurbs, R.A.: Reservoir-system Simulation and Operation Models. Journal of Water Resource Planning and Management. 119(4), 455–472 (1993).

    Article  Google Scholar 

  17. Yakowitz, S.: Dynamic Programming Applications in Water Resources. Water Resources Research. 18(4), 673–696 (1982).

    Article  Google Scholar 

  18. Yeh, W.W.G.: Reservoir Management and Operation Models: A State-of-the-Art Review. Water Resources Research. 21(12), 1797–1818 (1985).

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Applications, Bharathiar University, Coimbatore, 641046, TamilNadu, India

    D. Kiruthiga & T. Amudha

Authors
  1. D. Kiruthiga
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. T. Amudha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to T. Amudha .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering & Information Technology, Veer Surendra Sai University of Technology, Sambalpur, Odisha, India

    Himansu Sekhar Behera

  2. Department of Computer Science and Engineering, Sri Sivani College of Engineering (SSCE), Srikakulam, Andhra Pradesh, India

    Janmenjoy Nayak

  3. Department of Computer Application, Veer Surendra Sai University of Technology, Sambalpur, Odisha, India

    Bighnaraj Naik

  4. Machine Intelligence Research (MIR) Lab, Auburn, WA, USA

    Ajith Abraham

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kiruthiga, D., Amudha, T. (2019). Hydropower Generation Optimization and Forecasting Using PSO. In: Behera, H., Nayak, J., Naik, B., Abraham, A. (eds) Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 711. Springer, Singapore. https://doi.org/10.1007/978-981-10-8055-5_37

Download citation

Publish with us

Policies and ethics

Search

Navigation