Advertisement

Optimal Unit Commitment for Thermal Power Systems Using Penguin Search Algorithm

  • C. ChitraEmail author
  • T. Amudha
Conference paper
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 3)

Abstract

Electricity networks day to day delivers hundreds of giga watts per hour (GWH) to consumers. Those interconnected systems became hard enough challenging to maintain and run in the operating mode. So need arises to generate and supply electricity in a smart methodology which is expected to be highly economical. Unit Commitment, an important economic problem through optimization to be solved for obtaining an optimal cost saving in a specific period of time which is purely based on the determination of the combination of available generators. An electrical industry which deals mainly with meeting the load demand for all the generators. A generating unit has various limitations such as minimum uptime, minimum downtime, minimum and maximum power generation limits, prohibited zones etc. This research implementation paper presents a general idea of scheduling the generating units using the Bio-inspired algorithm PeSOA - Penguin Search Optimization Algorithm. PeSOA is a latest metaheuristic method through penguins collaborative hunting strategies, specially designed for optimizing non-linear systems. Significance of PeSOA is penguins Distribution is balanced through local minima and global minimum. Implementation carried out for various types of systems include problem instances of 10 units - 24 h system, 3 units – 24 h system, 4 units - 8 h system and 4 units – 24 h system, each of all the cases has compared with the results from the literature.

Keywords

Unit commitment Optimization Economic dispatch Bio-inspired computing Penguin Search Optimization Algorithm 

References

  1. 1.
    Christober Asir Rajan C, Surendra K, Rama Anvesh Reddy B, Shobana R, Sreekanth Reddy Y (2014) A refined solution to classical unit commitment problem using IWO algorithm. Int J Res Eng Technol 3(7):327–335Google Scholar
  2. 2.
    Chuang CS, Chang GW (2013) Lagrangian relaxation-based unit commitment considering fast response reserve constraints. Energy Power Eng 5:970–974Google Scholar
  3. 3.
    Ananthan D, Nishanthivalli S (2014) Unit commitment solution using particle swarm optimization. Int J Emerg Technol Adv Eng 4(1):1–9Google Scholar
  4. 4.
    Dudek G (2009) Adaptive simulated annealing schedule of the unit commitment problem. Electr Power Syst Res 80: 465–472Google Scholar
  5. 5.
    Lopez JA, Ceciliano-Meza JL, Moya IG, Gomez RN (2011) A MIQCP formulation to solve the unit commitment problem for large-scale power systems. Electr Power Energy Syst 36: 68–75Google Scholar
  6. 6.
    Srikanth Reddy K, Panwar LK, Kumar R, Panigrahi BK (2016) Binary fireworks algorithm for profit based unit commitment (PBUC) problem. Electr Power Energy Syst 83:270–282Google Scholar
  7. 7.
    Sharma S, Mehta S, Verma T (2015) Weight pattern based cuckoo search for unit commitment problem. Int J Res Advent Technol 3(5):102–110Google Scholar
  8. 8.
    Sharma Y, Swarnkar KK (2014) Power system generation scheduling and optimization using fuzzy logic Technique. Int J Comput Eng Res 3(4):99–106Google Scholar
  9. 9.
  10. 10.
  11. 11.
  12. 12.
  13. 13.
  14. 14.
  15. 15.
  16. 16.
  17. 17.
  18. 18.
  19. 19.
  20. 20.
  21. 21.
  22. 22.

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer ApplicationsBharathiar UniversityCoimbatoreIndia

Personalised recommendations