| Peer-Reviewed

Dynamic Economic Dispatch for Combined Heat and Power Units using Particle Swarm Algorithms

Received: 4 February 2015     Accepted: 11 March 2015     Published: 21 March 2015
Views:       Downloads:
Abstract

In this paper, combined heat and power units are incorporated in dynamic economic dispatch to minimize total production costs considering realistic constraints such as ramp rate and spinning reserve limits effects over a short time span. Four evolutionary approaches, namely particle swarm optimization (PSO), particle swarm optimization with constriction factor (PSOCFA), particle swarm optimization with inertia weight factor (PSOIWA) and particle swarm optimization with both constriction factor and inertia weight factor (PSOCFIWA) are successfully implemented to solve the combined heat and power economic dispatch (CHPED) problem. These approaches have been tested on 12-generation units system with two steam, four gas and six cogeneration units. In addition, the performance tests are applied to measure the actual power output and the fuel consumption in every point tests for achieving different curves such as input/output, incremental heat rate and heat rate curves for the twelve units. The results of the four approaches are compared with those obtained using existing performance testing method. The results show that the particle swarm optimization with improved inertia weight is able to achieve a better solution at less computational time.

Published in International Journal of Energy and Power Engineering (Volume 4, Issue 2)
DOI 10.11648/j.ijepe.20150402.19
Page(s) 84-93
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2015. Published by Science Publishing Group

Keywords

Combined Heat and Power Economic Dispatch (CHPED), Spinning Reserve, Ramp Rate, Particle Swarm Optimization (PSO)

References
[1] Niknam T, Kavousi Fard A, Baziar A. Multi-objective stochastic distribution feeder reconfiguration problem considering hydrogen and thermal energy production by fuel cell power plants. Energy June 2012; 42(1):563e73.
[2] Rong A, Hakonen H, Lahdelma R. A dynamic regrouping based sequential dynamic programming algorithm for unit commitment of combined heat and power systems. Energy Convers Manage 2009; 50:1108e15.
[3] Liu C, Shahidehpour M, Li Z, Fotuhi-Firuzabad M. Component and mode models for the short-term scheduling of combined-cycle units. IEEE Trans Power Syst 2009; 24:976e90.
[4] Niknam T, Azizipanah-Abarghooee R, Roosta A, Amiri B. A new multi-objective reserve constrained combined heat and power dynamic economic emission dispatch. Energy 2012; 42:530e45.
[5] A. Rong, H. Hakonen, R. Lahdelma, “An Efficient Linear Model and Optimization Algorithm for Multisite Combined Heat and Power Production”, European Journal of Operational Research, Vol. 168, pp. 612-632, 2006.
[6] K. Nekooei, M.M. Farsangi, H. Nezamabadi-pour, “An Improved Harmony Search Approach to Economic Dispatch”, International Journal on Technical and Physical Problems of Engineering (IJTPE), Issue 8, Vol. 3, No. 3, pp. 25-31, September 2011.
[7] Y.H. Song, C.S. Chou, T.J. Stonham, “Combined Heat and Power Dispatch by Improved Ant Colony Search Algorithm”, Electric Power Systems Research, Vol. 52, pp. 115-121, 1999.
[8] C.T. Su, C.L. Chiang, “An Incorporated Algorithm for Combined Heat and Power Economic Dispatch”, Electric Power Systems Research, Vol. 69, pp. 187-195, 2004.
[9] A. Vasebi, M. Fesanghary, S.M.T. Bathaee, “Combined Heat and Power Economic Dispatch by Harmony Search Algorithm”, International Journal of Electrical Power Energy Systems, Vol. 29, pp. 713-719, 2007.
[10] L. Wang, C. Singh, “Stochastic Combined Heat and Power Dispatch Based on Multi Objective Particle Swarm Optimization”, International Journal of Electrical Power Energy Systems, Vol. 30, pp. 226-234, 2008.
[11] Moustafa YG, Mekhamer SF, Moustafa YG, EI-Sherif N, Mansour MM. A modified particle swarm optimizer applied to the solution of the economic dispatch problem. International conference on electrical, electronic, and computer engineering, ICEEC, Cairo, Egypt. pp. 724--31, 2004.
[12] Park JB, Lee KS, Shin JR, Lee KY. A particle swarm optimization for economic dispatch with nonsmooth cost function. IEEE Trans Power Syst;pp. 20(1 ):34--42,2005.
[13] Eberhart RC, Kennedy, JF. A new optimizer using particle swarm theory. In: Proceedings of the 6th international symposium on micro machine and human science, Nagoya, Japan. pp. 39-43, 1 995.
[14] M.fotuhi-firuzabab, R.Billinton.Asecurity based approach for generating unit scheduling, IEEE, power systems Research Group University of Saskatchewan Saskatoon, Canada.
[15] P.S.R MURTY, O. U. Hyderabad. Operation and Control in Power System, Giriraj Lane, Sultan Bazar. 2008
[16] Koustav Dasgupta, Koustav, Sumit Banerjee.An Analysis of Economic Load Dispatch using Different Algorithms, Kalyani, WB, India. 2014
[17] AIEE Working Group Report on Application of Incremental Heat Rates for Economic Dispatch of Power, AIEE publication S-104.
[18] H. H. Happ, W. B. Ille, R. H. Risinger, Economic System Operation, Part I. Method of Computing Valve Loop Heat Rates on Multi-Valve Turbines, AIEE PAS No.64, pp. 609-6 15, 1963.
Cite This Article
  • APA Style

    Mohamed Ahmed Sadeek, Azza Ahmed El Dessouky, Abd El Hay Ahmed Sallam. (2015). Dynamic Economic Dispatch for Combined Heat and Power Units using Particle Swarm Algorithms. International Journal of Energy and Power Engineering, 4(2), 84-93. https://doi.org/10.11648/j.ijepe.20150402.19

    Copy | Download

    ACS Style

    Mohamed Ahmed Sadeek; Azza Ahmed El Dessouky; Abd El Hay Ahmed Sallam. Dynamic Economic Dispatch for Combined Heat and Power Units using Particle Swarm Algorithms. Int. J. Energy Power Eng. 2015, 4(2), 84-93. doi: 10.11648/j.ijepe.20150402.19

    Copy | Download

    AMA Style

    Mohamed Ahmed Sadeek, Azza Ahmed El Dessouky, Abd El Hay Ahmed Sallam. Dynamic Economic Dispatch for Combined Heat and Power Units using Particle Swarm Algorithms. Int J Energy Power Eng. 2015;4(2):84-93. doi: 10.11648/j.ijepe.20150402.19

    Copy | Download

  • @article{10.11648/j.ijepe.20150402.19,
      author = {Mohamed Ahmed Sadeek and Azza Ahmed El Dessouky and Abd El Hay Ahmed Sallam},
      title = {Dynamic Economic Dispatch for Combined Heat and Power Units using Particle Swarm Algorithms},
      journal = {International Journal of Energy and Power Engineering},
      volume = {4},
      number = {2},
      pages = {84-93},
      doi = {10.11648/j.ijepe.20150402.19},
      url = {https://doi.org/10.11648/j.ijepe.20150402.19},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijepe.20150402.19},
      abstract = {In this paper, combined heat and power units are incorporated in dynamic economic dispatch to minimize total production costs considering realistic constraints such as ramp rate and spinning reserve limits effects over a short time span. Four evolutionary approaches, namely particle swarm optimization (PSO), particle swarm optimization with constriction factor (PSOCFA), particle swarm optimization with inertia weight factor (PSOIWA) and particle swarm optimization with both constriction factor and inertia weight factor (PSOCFIWA) are successfully implemented to solve the combined heat and power economic dispatch (CHPED) problem. These approaches have been tested on 12-generation units system with two steam, four gas and six cogeneration units. In addition, the performance tests are applied to measure the actual power output and the fuel consumption in every point tests for achieving different curves such as input/output, incremental heat rate and heat rate curves for the twelve units. The results of the four approaches are compared with those obtained using existing performance testing method. The results show that the particle swarm optimization with improved inertia weight is able to achieve a better solution at less computational time.},
     year = {2015}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Dynamic Economic Dispatch for Combined Heat and Power Units using Particle Swarm Algorithms
    AU  - Mohamed Ahmed Sadeek
    AU  - Azza Ahmed El Dessouky
    AU  - Abd El Hay Ahmed Sallam
    Y1  - 2015/03/21
    PY  - 2015
    N1  - https://doi.org/10.11648/j.ijepe.20150402.19
    DO  - 10.11648/j.ijepe.20150402.19
    T2  - International Journal of Energy and Power Engineering
    JF  - International Journal of Energy and Power Engineering
    JO  - International Journal of Energy and Power Engineering
    SP  - 84
    EP  - 93
    PB  - Science Publishing Group
    SN  - 2326-960X
    UR  - https://doi.org/10.11648/j.ijepe.20150402.19
    AB  - In this paper, combined heat and power units are incorporated in dynamic economic dispatch to minimize total production costs considering realistic constraints such as ramp rate and spinning reserve limits effects over a short time span. Four evolutionary approaches, namely particle swarm optimization (PSO), particle swarm optimization with constriction factor (PSOCFA), particle swarm optimization with inertia weight factor (PSOIWA) and particle swarm optimization with both constriction factor and inertia weight factor (PSOCFIWA) are successfully implemented to solve the combined heat and power economic dispatch (CHPED) problem. These approaches have been tested on 12-generation units system with two steam, four gas and six cogeneration units. In addition, the performance tests are applied to measure the actual power output and the fuel consumption in every point tests for achieving different curves such as input/output, incremental heat rate and heat rate curves for the twelve units. The results of the four approaches are compared with those obtained using existing performance testing method. The results show that the particle swarm optimization with improved inertia weight is able to achieve a better solution at less computational time.
    VL  - 4
    IS  - 2
    ER  - 

    Copy | Download

Author Information
  • East Delta Electricity Production Company, Ismailia, Egypt

  • Faculty of engineering, Port-Said University, Port-Said, Egypt

  • Faculty of engineering, Port-Said University, Port-Said, Egypt

  • Sections