Skip to main content

Advertisement

SpringerLink
Log in

Hybrid and Modified Harmony Search Optimization application in economic load dispatch with integrated renewable source

  • Original Paper
  • Published:
Electrical Engineering Aims and scope Submit manuscript

Abstract

Curbing CO2 emission is required for the global warming reduction effect. Human civilization is moving toward renewable sources to achieve this as early as possible. The electrical energy requirement increases day by day as with population and industrial growth. Therefore, more electrical energy saving and proper utilization of electricity are becoming essential. Nowadays researchers are moving toward, developing and demonstrating net zero emission (CO2 emission) renewable energy sources (like solar energy, wind power, Hydro plant and more) technologies. However, incorporating those energy sources in the electrical power grid, the grid becomes hybrid and complex. It is necessary for economic load dispatch (ELD) requirements that the hybrid grid must be optimized as well as utilized efficiently that results economical efficient energy saving. In this article, we contribute a solution toward efficient optimization applicable to moderate size generating stations (using renewable source for hybrid grid network). The presented work fulfills ELD requirement using hybrid harmony search and modified harmony search optimization. Renewable energy sources are a suitable options to meet the contingencies in conventional methods of electricity generation. Wind and geothermal power have the all-time availability and thus are the favorable clean energy sources. However, wind’s discontinuous behaviors effecting grid systems with unpredictable injection of power becomes a vital challenge to maintain good quality of the grid. Here the ELD requirement is prepared with the addition of renewable energy (wind) generation cost and thermal units. The penalty costs are being added for renewable power because of variability and uncertainty. Hybrid harmony search, ant colony, modified harmony search optimization and PSO are applied to the wind and thermal generating units (hybrid network) with an objective to get generation cost minimum to fulfill customer requirement. The proposed estimation is applied in twenty six Bus systems and simulation output data acquired with those optimization methods are compared among each other.

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

Similar content being viewed by others

Data availability statement

The datasets analyzed during the current study are available in the IEEE repository, https://ieeexplore.ieee.org/document/7470712.

References

  1. Lu M, Chang C, Lee W, Wang L (2009) Combining the wind power generation system with energy storage equipment. IEEE Trans Ind Appl 45(6):2109–2115

    Article  Google Scholar 

  2. Wu C, Zhang X-P, Sterling M (2022) Wind power generation variations and aggregations. CSEE J Power Energy Syst 8(1):17–38. https://doi.org/10.17775/CSEEJPES.2021.03070

    Article  Google Scholar 

  3. Shu Z, Jirutitijaroen P (2014) Optimal operation strategy of energy storage system for grid-connected wind power plants. IEEE Trans Sustain Energy 5(1):190–199. https://doi.org/10.1109/TSTE.2013.2278406

    Article  Google Scholar 

  4. Zhang Z, Zhang Y, Huang Q, Lee W (2018) Market-oriented optimal dispatching strategy for a wind farm with a multiple stage hybrid energy storage system. CSEE J Power Energy Syst 4(4):417–424. https://doi.org/10.17775/CSEEJPES.2018.00130

    Article  Google Scholar 

  5. Opathella C, Venkatesh B (2013) Managing uncertainty of wind energy with wind generators cooperative. IEEE Trans Power Syst 28(3):2918–2928. https://doi.org/10.1109/TPWRS.2012.2233502

    Article  Google Scholar 

  6. Bhattacharjee K, Patel N (2020) A comparative study of Economic Load Dispatch with complex non-linear constraints using Salp Swarm Algorithm. Int J Sci Technol Sci Iran. https://doi.org/10.24200/SCI.2020.52145.2562

    Article  Google Scholar 

  7. Bhattacharjee K (2018) Economic dispatch problems using backtracking search optimization. Int J Energy Optim Eng IGI-Global 7(2):39–60. https://doi.org/10.4018/IJEOE.2018040102

    Article  Google Scholar 

  8. Roy S, Bhattacharjee K, Bhattacharya A (2016) A modern approach to solve of economic load dispatch using group leader optimization technique. Int J Energy Optim Eng IGI-Global 6(1):66–85. https://doi.org/10.4018/IJEOE.2017010104

    Article  Google Scholar 

  9. Bhattacharjee K, Bhattacharya A, Dey SHN (2014) Oppositional real coded chemical reaction optimization for different economic dispatch problems. Int J Electr Power Energy Syst (Elsevier) 55:378–391

    Article  Google Scholar 

  10. Bhattacharjee K, Bhattacharya A, Dey SHN (2014) Chemical reaction optimisation for different economic dispatch problems. Gen Transm Distrib IET 8(3):530–541

    Article  Google Scholar 

  11. Bhattacharjee K, Patel N (2018) A comparative study of economic load dispatch using sine cosine algorithm. Int J Sci Technol Sci Iran. https://doi.org/10.24200/sci.2018.50635.1796

    Article  Google Scholar 

  12. Bhattacharjee K, Patel N (2019) An experimental study regarding economic load dispatch using search group optimization. Int J Sci Technol Sci Iran. https://doi.org/10.24200/sci.2019.51798.2367

    Article  Google Scholar 

  13. Bhattacharjee K, Bhattacharya A, Dey SHN (2014) Teaching Learning Based Optimization for Different Economic Dispatch Problems. Int J Sci Technol 21(3):870–884

    Google Scholar 

  14. Bhattacharjee K, Bhattacharya A, Chattopadhyay PK (2013) Discussion on “A GA-API solution for the economic dispatch of generation in power system operation.” IEEE Trans Power Syst 28(1):570–571

    Article  Google Scholar 

  15. Dubey HM, Pandit M, Panigrahi BK (2015) Hybrid flower pollination algorithm with time-varying fuzzy selection mechanism for wind integrated multi-objective dynamic economic dispatch. Renew Energy 83:188–202. https://doi.org/10.1016/j.renene.2015.04.034

    Article  Google Scholar 

  16. Dubey HM, Pandit M, Panigrahi BK (2018) An overview and comparative analysis of recent bio-inspired optimization techniques for wind integrated multi-objective power dispatch. Swarm Evol Comput 38:12–34. https://doi.org/10.1016/j.swevo.2017.07.012

    Article  Google Scholar 

  17. Dubey SM, Dubey HM, Pandit M, Salkuti SR (2021) Multiobjective scheduling of hybrid renewable energy system using equilibrium optimization. Energies 14(19):6376. https://doi.org/10.3390/en14196376

    Article  Google Scholar 

  18. Dubey SM, Dubey HM, Salkuti SR (2022) Modified quasi-opposition-based grey wolf optimization for mathematical and electrical benchmark problems. Energies 15:5704. https://doi.org/10.3390/en15155704

    Article  Google Scholar 

  19. Tiwari V, Dubey HM, Pandit M, Salkuti SR (2022) CHP-based economic emission dispatch of microgrid using Harris Hawks optimization. Fluids 7:248. https://doi.org/10.3390/fluids7070248

    Article  Google Scholar 

  20. Srivastava A, Singh S (2020) Implementation of ant colony optimization and particle swarm optimization in economic load dispatch problem using renewable source. In: IEEE 17th India council international conference (INDICON), pp 1–6. https://doi.org/10.1109/INDICON49873.2020.9342190.

  21. Al-Awami AT, Sortomme E, El-Sharkawi MA (2009) Optimizing economic/environmental dispatch with wind and thermal units. IEEE Power Energy Soc Gen Meet 2009:1–6. https://doi.org/10.1109/PES.2009.5275667

    Article  Google Scholar 

  22. Biswas PP, Suganthan PN, Amaratunga GAJ (2017) Optimal power flow solutions incorporating stochastic wind and solar power. Energy Convers Manag 148:1194–1207

    Article  Google Scholar 

  23. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN'95—international conference on neural networks, vol 4, pp 1942–1948. https://doi.org/10.1109/ICNN.1995.488968

  24. Shami TM, El-Saleh AA, Alswaitti M, Al-Tashi Q, Summakieh MA, Mirjalili S (2022) Particle swarm optimization: a comprehensive survey. IEEE Access 10:10031–10061. https://doi.org/10.1109/ACCESS.2022.3142859

    Article  Google Scholar 

  25. Deep K, Bansal JC (2018) Solving economic dispatch problems with valve-point effects using particle swarm optimization. J Univ Comput Sci 18(13):1842–1852. https://doi.org/10.3217/jucs-018-13-1842

    Article  MATH  Google Scholar 

  26. Ciuprina G, Ioan D, Munteanu I (2002) Use of intelligent-particle swarm optimization in electromagnetics. IEEE Trans Magn 38(2):1037–1040. https://doi.org/10.1109/20.996266

    Article  Google Scholar 

  27. Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11:341–359. https://doi.org/10.1023/A:1008202821328

    Article  MathSciNet  MATH  Google Scholar 

  28. Rabiee A, Mohammadi-Ivatloo B, Ehsan M (2012) Discussion of hybrid differential evolution with biogeography-based optimization for solution of economic load dispatch. IEEE Trans Power Syst 27(1):574–574. https://doi.org/10.1109/TPWRS.2011.2176591

    Article  Google Scholar 

  29. Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern Part B (Cybern) 26(1):29–41. https://doi.org/10.1109/3477.484436

    Article  Google Scholar 

  30. Mulo T, Syam P, Choudhury A (2020) Application of modified harmony search and differential evolution optimization techniques in economic load dispatch. In: Advances in control, signal processing and energy systems: select proceedings of CSPES 2018, Springer, Singapore. pp 199–213. https://doi.org/10.1007/978-981-32-9346-5_16.

  31. Yang GY, Dong ZY, Wong KP (2008) A modified differential evolution algorithm with fitness sharing for power system planning. IEEE Trans Power Syst 23(2):514–522. https://doi.org/10.1109/TPWRS.2008.919420

    Article  Google Scholar 

  32. Salaria UA, Menhas MI, Manzoor S (2021) Quasi oppositional population based global particle swarm optimizer with inertial weights (QPGPSO-W) for solving economic load dispatch problem. IEEE Access 9:134081–134095. https://doi.org/10.1109/ACCESS.2021.3116066

    Article  Google Scholar 

  33. Mulo T, Syam P, Choudhury AB (2018) Economical load dispatch using modified harmony memory search optimization technique. In: 2018 5th IEEE Uttar Pradesh section international conference on electrical, electronics and computer engineering (UPCON), 2018, pp 1–6, https://doi.org/10.1109/UPCON.2018.8596973.

  34. Kim MJ, Song H-Y, Park J-B, Roh J-H, Lee SU, Son S-Y (2013) An improved mean-variance optimization for nonconvex economic dispatch problems. J Electr Eng Technol 8(1):80–89

    Article  Google Scholar 

  35. Vlachogiannis JG, Lee KY (2010) Closure to discussion on “economic load dispatch—a comparative study on heuristic optimization techniques with an improved coordinated aggregation-based PSO. IEEE Trans Power Syst 25(1):591–592. https://doi.org/10.1109/TPWRS.2009.2037534

    Article  Google Scholar 

  36. Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. SIMULATION 76(2):60–68. https://doi.org/10.1177/003754970107600201

    Article  Google Scholar 

  37. Lee KS, Geem ZW (2005) A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Comput Methods Appl Mech Eng 194(36–38):3902–3933. https://doi.org/10.1016/j.cma.2004.09.007

    Article  MATH  Google Scholar 

  38. Saddat H (1998) Power system analysis. Tata McGraw Hill Publishing Co Ltd, New York

    Google Scholar 

Download references

Funding

The authors declare that they don’t have any funding support.

Author information

Authors and Affiliations

  1. Electrical Engineering Department, Indian Institute of Engineering Science and Technology, Shibpur, India

    Tanmoy Mulo, Prasid Syam & Amalendu Bikash Choudhury

Authors
  1. Tanmoy Mulo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Prasid Syam
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amalendu Bikash Choudhury
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

In this research work, the algorithm development and paper writing are done by Mr. Tanmoy Mulo. The problem identification and conceptual validation are done by Dr. Prasid Syam and Dr. Amalendu Bikash Choudhury.

Corresponding author

Correspondence to Tanmoy Mulo.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Human and animal rights

The authors declare that the study of this paper is not related to humans and animals.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mulo, T., Syam, P. & Choudhury, A.B. Hybrid and Modified Harmony Search Optimization application in economic load dispatch with integrated renewable source. Electr Eng 105, 1923–1935 (2023). https://doi.org/10.1007/s00202-023-01770-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00202-023-01770-1

Keywords

Search

Navigation