Advertisement

Hybridization of two metaheuristics for solving the combined economic and emission dispatch problem

  • Yamina Ahlem GherbiEmail author
  • Fatiha Lakdja
  • Hamid Bouzeboudja
  • Fatima Zohra Gherbi
Original Article
  • 37 Downloads

Abstract

The development of computers and control software has contributed to the innovation of electrical networks. This development is necessarily linked to several concerns: energy, economic, environmental, etc. The introduction of the techniques of artificial intelligence software in the control and decision is essential in research and in the development of tomorrow’s networks. This paper deals with multi-criteria optimization metaheuristics. These criteria are moving toward the economic/environmental dispatch that addresses the impact of the cost of production and the emission of toxic gases such as competing objectives. This requires some form of conflict resolution to reach a solution. That is why we need effective optimization algorithms. The firefly algorithm and bat algorithm are two recent metaheuristics inspired by nature. Both methods have been studied and adapted to solve our multi-objective optimization problem within the constraints. At the end of this work, the hybridization of the firefly algorithm and bat algorithm was proposed. The purpose of this hybridization is to combine the advantages of both methods and thus improve their performance. The effectiveness of this new method was demonstrated by applying it on different network tests of 6, 10, and 20 generators; testing with several power demands in accordance with constraints; and considering the variability of active transmission losses.

Keywords

Bat algorithm Combined economic and emission dispatch Firefly algorithm Hybridization Metaheuristics 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    Slimani L, Bouktir T (2013) Economic power dispatch of power systems with pollution control using artificial bee colony optimization. Turk J Electr Eng Comput Sci 21:1515–1527CrossRefGoogle Scholar
  2. 2.
    Basu M (2011) Economic environmental dispatch using multi-objective differential evolution. Appl Soft Comput 11:2845–2853CrossRefGoogle Scholar
  3. 3.
    Koodalsamy C, Simon SP (2013) Fuzzified artificial bee colony algorithm for nonsmooth and nonconvex multiobjective economic dispatch problem. Turk J Electr Eng Comput Sci 21:1995–2014CrossRefGoogle Scholar
  4. 4.
    Fahad PM, Pandian V, Abdullah-Al-Wadud M, Vish K, Junzo W (2018) Quantum-behaved bat algorithm for many-objective combined economic emission dispatch problem using cubic criterion function. Neural Comput Appl 1–13Google Scholar
  5. 5.
    Elhameed MA, El-Fergany AA (2017) Water cycle algorithm-based economic dispatcher for sequential and simultaneous objectives including practical constraints. Appl Soft Comput 58:145–154CrossRefGoogle Scholar
  6. 6.
    El-ghazali T (2009) Metaheuristics from design to implementation. Wiley, LondonzbMATHGoogle Scholar
  7. 7.
    Ankur G, Ankit C (2018) A metaheuristic method to hide MP3 sound in JPEG image. Neural Comput Appl 30:1611–1618CrossRefGoogle Scholar
  8. 8.
    Pauline O, Desmond DVSC, Choon SH, Chuan HN (2018) Modeling and optimization of cold extrusion process by using response surface methodology and metaheuristic approaches. Neural Comput Appl 29:1077–1087CrossRefGoogle Scholar
  9. 9.
    Arif A, Javed F, Arshad N (2014) Integrating renewables economic dispatch with demand side management in micro-grids: a genetic algorithm-based approach. Energy Effic 7(2):271–284CrossRefGoogle Scholar
  10. 10.
    Bakare GA, Aliyu UO, Venayagamoorthy GK, Shu’aibu YK (2005) Genetic algorithms based economic dispatch with application to coordination of Nigerian thermal power plants. In: IEEE Power Engineering Society General Meeting, 2005, San Francisco, CA, USA, 16 June 2005, vol 1, pp 551–556Google Scholar
  11. 11.
    Azimi R, Esmaeili S (2013) Multiobjective daily Volt/VAr control in distribution systems with distributed generation using binary ant colony optimization. Turk J Electr Eng Comput Sci 21:613–629Google Scholar
  12. 12.
    Mangaiyarkarasi SP, Sree Renga Raja T (2014) PSO Based optimal location and sizing of SVC for novel multiobjective voltage stability analysis during N − 2 line contingency. Arch Electr Eng 63:535–550CrossRefGoogle Scholar
  13. 13.
    Abdullah MN, Abu Bakar AH, Abd Rahim N (2015) Modified particle swarm optimization for economic-emission load dispatch of power system operation. Turk J Electr Eng Comput Sci 23:2304–2318CrossRefGoogle Scholar
  14. 14.
    Bahmani-Firouzi B, Farjah E, Azizipanah-Abarghooee R (2013) An efficient scenario-based and fuzzy self-adaptive learning particle swarm optimization approach for dynamic economic emission dispatch considering load and wind power uncertainties. Energy 50:232–244CrossRefGoogle Scholar
  15. 15.
    Yang XS (2010) Engineering optimization: an introduction with metaheuristic application. Wiley, LondonCrossRefGoogle Scholar
  16. 16.
    Younes M, Khodja F, Kherfane RL (2014) Multi-objective economic emission dispatch solution using hybrid FFA (firefly algorithm) and considering wind power penetration. Energy 67:595–606CrossRefGoogle Scholar
  17. 17.
    Durkota K (2011) Bachelor thesis: implementation of a discrete firefly algorithm for the QAP problem within the seage framework. Czech Technical University, Faculty of Electrical Engineering, PragueGoogle Scholar
  18. 18.
    Yang XS (2010) Nature-inspired metaheuristic algorithms, 2nd edn. Luniver press, University of Cambridge, United KingdomGoogle Scholar
  19. 19.
    Baziar A, Kavoosi-Fard A, Zare J (2013) A novel self adaptive modification approach based on bat algorithm for optimal management of renewable MG. J Intell Learn Syst Appl 5:11–18Google Scholar
  20. 20.
    Belmadani A, Benasla L, Rahli M (2009) Etude d’un dispatching économique-environnemental par la method de harmony search. Acta Electrotehnica 50:44–48Google Scholar
  21. 21.
    Yang XS, Hosseini SSS, Gandomi AH (2012) Firefly algorithm for solving non-convex economic dispatch problems with valve loading effect. Appl Soft Comput 12:1180–1186CrossRefGoogle Scholar
  22. 22.
    Taha AM, Tang AYC (2013) Bat algorithm for rough set attribute reduction. J Theor Appl Inf Technol 51:1–8Google Scholar
  23. 23.
    Gandomi AH, Yang XS, Alavi AH, Talatahari S (2013) Bat algorithm for constrained optimization tasks. Neural Comput Appl 22:1239–1255CrossRefGoogle Scholar
  24. 24.
    Ganguli S, Kaur G, Sarkar P (2018) A novel hybrid metaheuristic algorithm for model order reduction in the delta domain: a unified approach. Neural Comput Appl.  https://doi.org/10.1007/s00521-018-3440-2 Google Scholar
  25. 25.
    Glover F (1986) Future paths for integer programming and links to artificial intelligence. Comput Oper Res 12(5):533–549MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.
    Mühlenbein H (1991) Parallel genetic algorithms, population genetics and combinatorial optimization. Workshop on parallel processing: logic, organization, and technology. Springer, BerlinGoogle Scholar
  27. 27.
    Grefenstette J (1986) Optimization of control parameters for genetic algorithms. IEEE 16(1):122–128Google Scholar
  28. 28.
    Fetanat A, Shafipour GA (2017) A hybrid method of LMDI, symmetrical components, and SFA to estimate the distribution of energy-saving potential with consideration of unbalanced components in decomposition analysis. Energy Effic l 10(4):1041–1059CrossRefGoogle Scholar
  29. 29.
    Jiang S, Ji Z, Shen Y (2014) A novel hybrid particle swarm optimization and gravitational search algorithm for solving economic emission load dispatch problems with various practical constraints. Electr Power Energy Syst 55:628–644CrossRefGoogle Scholar
  30. 30.
    Niu Q, Zhang H, Wang X, Li K, Irwin GW (2014) A hybrid harmony search with arithmetic crossover operation for economic dispatch. Electr Power Energy Syst 62:237–257CrossRefGoogle Scholar
  31. 31.
    Blum C, Aguilera MJB, Roli A, Sampels M (2008) Hybrid metaheuristics. Springer, BerlinCrossRefzbMATHGoogle Scholar
  32. 32.
    Amrouche H (2012) Sur l’hybridation des métaheuristiques, Algeria: Magister theses, Faculty of Electrical Engineering and Computer Science, Automatic Department, University of Mouloud Mammeri, Tizi-ouzouGoogle Scholar
  33. 33.
    Basu M (2011) Economic environmental dispatch using multi-objective differential evolution. Appl Soft Comput 11:2845–2853CrossRefGoogle Scholar
  34. 34.
    Basu M (2008) Dynamic economic emission dispatch using non dominated sorting genetic algorithm-II. Electr Power Energy Syst 30:140–149CrossRefGoogle Scholar
  35. 35.
    Coelho LDS, Lee CS (2008) Solving economic load dispatch problems in power systems using chaotic and Gaussian particle swarm optimization approaches. Electrical Power Energy Syst 30:297–307CrossRefGoogle Scholar
  36. 36.
    Gholami A, Ansari J, Jamei M, Kazemi A (2014) Environmental/economic dispatch incorporating renewable energy sources and plug-in vehicles. IET Gener Transm Distrib 8:2183–2198CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of Electrical Engineering, Durable Development of Electric Power LaboratoryUSTOOranAlgeria
  2. 2.Intelligent Control and Electrical Power System LaboratorySidi-Bel-Abbes UniversitySaidaAlgeria
  3. 3.Intelligent Control and Electrical Power System LaboratoryDjillali Liabès UniversitySidi-Bel-AbbesAlgeria

Personalised recommendations