Skip to main content
SpringerLink
Log in

Environmental/economic power dispatch using a Hybrid Big Bang–Big Crunch optimization algorithm

  • Original Article
  • Published:
International Journal of System Assurance Engineering and Management Aims and scope Submit manuscript

Abstract

The combined economic and emission dispatch (CEED) problem where objective function is highly non linear, non-differentiable and may have multiple local minima. Therefore, classical optimization methods may not converge or get trapped to any local minima. This paper proposes a Hybrid Big Bang–Big Crunch (HBB–BC) optimization algorithm technique for solving the CEED. Six generator test and IEEE 30 standard bus system was used for testing and validation purposes. The preference of the HBB–BC is compared with other heuristic methods. The results show, clearly, that the proposed method gives better optimal solution as compared to the other methods.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Abido MA (2003) A niched Pareto genetic algorithm for multiobjective environmental/economic dispatch. Electr Power Syst Res 25:97–99

    Article  Google Scholar 

  • Abido MA (2006) Multiobjective evolutionary algorithms for electric power dispatch problem. IEEE Trans Evol Comput 10:315–329

    Article  Google Scholar 

  • Cai J, Xiaoqian M, Qiong L, Lixiang L, Haipeng Peng D (2009) A multi-objective chaotic particle swarm optimization for environmental/economic dispatch. Energy Convers Manage 50:1318–1325

    Article  Google Scholar 

  • Charles VC, Huq F (2013) CO2 and cost optimization of reinforced concrete frames using a Big Bang–Big Crunch algorithm. Eng Struct 48:363–372

    Article  Google Scholar 

  • Chen P-H, Chang H-C (1995) Large-scale economic dispatch by genetic algorithm. IEEE Trans Power Syst 10:19–26

    Article  MathSciNet  Google Scholar 

  • El-Keib A, Ma H, Hart JL (1994) Economic dispatch in view of the clean air act of 1990. IEEE Trans Power Syst 9:972–978

    Article  Google Scholar 

  • Erol OK, Eksin I (2006) New optimization method: Big Bang–Big Crunch. Adv Eng Softw 37:106–111

    Article  Google Scholar 

  • Farag A, Al-Baiyat S, Cheng TC (1995) Economic load dispatch multiobjective optimization procedures using linear programming techniques. IEEE Trans Power Syst 10:731–738

    Article  Google Scholar 

  • Giang Z-L (2003) Particle swarm optimization to solving the economic dispatch considering the generator constraints. IEEE Trans Power Syst 18:1187–2123

    Article  Google Scholar 

  • Granelli GP, Montagna M, Pasini GL, Marannino P (1992) Emission constrained dynamic dispatch. Electric Power Syst Res 24:56–64

    Article  Google Scholar 

  • Helsin JS, Hobbs BF (1989) Amultiobjective production costingmodel for analyzing emission dispatching and fuel switching. IEEE Trans Power Syst 4:836–842

    Article  Google Scholar 

  • Jiejin C, Xiaoqian M, Lixiang L, Peng PH (2007) Chaotic particle swarm optimization for economic dispatch considering the generator constraints. Energy Convers Manage 48:645–653

    Article  Google Scholar 

  • Kaveh A, Talatahari S (2010) A discrete Big Bang–Big Crunch algorithm for optimal design of skeletal structure. Asian J Civ Eng 11(1):103–122

    Google Scholar 

  • Kaveh A, Zolghadr A (2012) Truss optimization with natural frequency constraints using a hybridized CSS-BBBC algorithm with trap recognition capability. Comput Struct 102–103:14–27

    Article  Google Scholar 

  • Kaveh A, Talatahari S (2009) Size optimization of space trusses using Big Bang–Big Crunch algorithm. Comput Struct 87:1129–1140

    Article  Google Scholar 

  • Khamsawang S, Jiriwibhakorn S (2009) Solving the economic dispatch problem using novel particle swarm optimization. Int J Electric Electron Eng 3:41–46

    Google Scholar 

  • Labbi Y, Ben Attous D (2010) Big Bang–Big Crunch optimization algorithm for economic dispatch with valve-point effect. J Theor Appl Inf Technol 6:48–56

    Google Scholar 

  • Ongsakul W, Dechanupapritttha S, Ngamroo I (2001) Tabu search algorithm for constrained economic dispatch. In: Proceedings of the international conference on power systems, Wuhan, China, p 428–433

  • Ongsakul W, Dechanupapritttha S, Ngamroo I (2004) Parallel tabu search algorithm for constrained economic dispatch. IEE Proc 151(2):157–166

    Article  Google Scholar 

  • Panigrah BK, Yadav SR, Agrawal S, Tiwari MK (2006) A clonal algorithm to solve economic load dispatch. Electric Power Syst Res 77:1381–1389

    Article  Google Scholar 

  • Pothiya S, Ngamroo I, Kongprawechnon W (2007) Application of multiple tabu search algorithm to solve dynamic economic dispatch considering generator constraints. Energy Convers Manage 49:506–516

    Article  Google Scholar 

  • Pothiya S, Ngamroo I, Kongprawechnon W (2008) Application of multiple tabu search algorithm to solve dynamic economic dispatch considering generator constraints. Energy Convers Manage 49:506–516

    Article  Google Scholar 

  • Roa-Sepulveda CA (1996) Environmental economic dispatch via hopfield neural network and Taboo search. In: UPEC’96 universities power engineering conference, Crete, Greece, p 1001–1004

  • Talaq JH, El-Hawary F, El-Hawary ME (1994) A summary of environmental/economic dispatch algorithms. IEEE Trans Power Syst 9:1508–1516

    Article  Google Scholar 

  • Walters DC, Sheble GB (1993) Genetic algorithm solution of economic dispatch with valve point loading. IEEE Trans Power Syst 99:1325–1332

    Article  Google Scholar 

  • Wang KP, Fung CC (1993) Simulate annealing base economic dispatch algorithm. IEE Proc C 140(6):507–513

    Google Scholar 

  • Wood AJ, Wollenberg BF (1984) Power generation, operation and control. Wiley, New York

    Google Scholar 

  • Yang HT, Yang PC, Huang CL (1996) Evolution programming based economic dispatch for units with non-smooth fuel cost functions. IEEE Trans Power Syst 11(1):112–118

    Article  Google Scholar 

  • Yokoyama R, Bae SH, Morita T, Sasaki H (1988) Multiobjective generation dispatch based on probability security criteria. IEEE Trans Power Syst 3:317–324

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, University of El-Oued, El-Oued, Algeria

    Yacine Labbi & Djilani Ben Attous

Authors
  1. Yacine Labbi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Djilani Ben Attous
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yacine Labbi.

Appendix

Appendix

Generalized loss coefficient for IEEE-30 bus test system:

$$\begin{aligned} B &= \left[{\begin{array}{*{20}c} {0.1382} & { - 0.0299} & {0.0044} & { - 0.0022} & { - 0.0010} & { - 0.0008} \\ { - 0.0299} & {0.0487} & { - 0.0025} & {0.0004} & {0.0016} & {0.0041} \\ {0.0044} & { - 0.0025} & {0.0182} & { - 0.0070} & { - 0.0066} & { - 0.0066} \\ { - 0.0022} & {0.0004} & { - 0.0070} & {0.0137} & {0.0050} & {0.0033} \\ { - 0.0010} & {0.0016} & { - 0.0066} & {0.0050} & {0.0109} & {0.0005} \\ { - 0.0008} & {0.0041} & { - 0.0066} & {0.0033} & {0.0005} & {0.0244} \\ \end{array}}\right] \\ B_{oi} &= \left[{\begin{array}{*{20}c} { - 0.0107} & {0.0060} & { - 0.0017} & {0.0009} & {0.0002} & {0.0030} \\ \end{array}}\right] ; \\ B_{oo} &=\, 9.8573{\text{e}} - 4; \\ \end{aligned}$$

Rights and permissions

Reprints and permissions

About this article

Cite this article

Labbi, Y., Ben Attous, D. Environmental/economic power dispatch using a Hybrid Big Bang–Big Crunch optimization algorithm. Int J Syst Assur Eng Manag 5, 602–610 (2014). https://doi.org/10.1007/s13198-013-0210-5

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13198-013-0210-5

Keywords

Search

Navigation