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An efficient algorithm for multi-objective optimal operation management of distribution network considering fuel cell power plants

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  • Niknam, Taher
  • Meymand, Hamed Zeinoddini
  • Mojarrad, Hasan Doagou

Abstract

This paper presents an interactive fuzzy satisfying method based on Hybrid Modified Honey Bee Mating Optimization (HMHBMO). Its purpose is to solve the Multi-objective Optimal Operation Management (MOOM) problem which can be affected by Fuel cell power plants (FCPPs). Minimizing total electrical energy losses, total electrical energy cost, total pollutant emission produced by sources and deviation of bus voltages are the objective functions in this method. A new interactive fuzzy satisfying method is presented to solve the multi-objective problem by assuming that the decision-maker (DM) has fuzzy targets for each of the objective functions. Through the interaction with the DM, the fuzzy goals are quantified by eliciting the corresponding membership functions. Considering the current solution, the DM updates the reference membership values until the best solution can be obtain. The MOOM problem is modeled as a mixed integer nonlinear programming problem. Therefore, evolutionary methods can be used to solve this problem since they are independence of objective function’s type and constraints. Recently researchers have presented a new evolutionary method called Honey Bee Mating Optimizations (HBMO) algorithm. Original HBMO often converges to local optima and this is a disadvantage of this method. In order to avoid this shortcoming we propose a new method. This method improves the mating process and also combines the modified HBMO with a Chaotic Local Search (CLS). Numerical results on a distribution test system have been presented to illustrate the performance and applicability of the proposed method.

Suggested Citation

  • Niknam, Taher & Meymand, Hamed Zeinoddini & Mojarrad, Hasan Doagou, 2011. "An efficient algorithm for multi-objective optimal operation management of distribution network considering fuel cell power plants," Energy, Elsevier, vol. 36(1), pages 119-132.
    • Handle: RePEc:eee:energy:v:36:y:2011:i:1:p:119-132
    DOI: 10.1016/j.energy.2010.10.062
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    References listed on IDEAS

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    Cited by:

    1. Abbasi, Ali Reza & Seifi, Ali Reza, 2015. "Considering cost and reliability in electrical and thermal distribution networks reinforcement planning," Energy, Elsevier, vol. 84(C), pages 25-35.
    2. Niknam, Taher & Azizipanah-Abarghooee, Rasoul & Roosta, Alireza & Amiri, Babak, 2012. "A new multi-objective reserve constrained combined heat and power dynamic economic emission dispatch," Energy, Elsevier, vol. 42(1), pages 530-545.
    3. Haddadian, Hossein & Noroozian, Reza, 2017. "Optimal operation of active distribution systems based on microgrid structure," Renewable Energy, Elsevier, vol. 104(C), pages 197-210.
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    5. Samaneh Seifollahi-Aghmiuni & Omid Bozorg Haddad, 2018. "Multi Objective Optimization with a New Evolutionary Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(12), pages 4013-4030, September.
    6. Kavousi-Fard, Abdollah & Niknam, Taher, 2014. "Multi-objective stochastic Distribution Feeder Reconfiguration from the reliability point of view," Energy, Elsevier, vol. 64(C), pages 342-354.
    7. Ebrahim Farjah & Mosayeb Bornapour & Taher Niknam & Bahman Bahmanifirouzi, 2012. "Placement of Combined Heat, Power and Hydrogen Production Fuel Cell Power Plants in a Distribution Network," Energies, MDPI, vol. 5(3), pages 1-25, March.
    8. Finke, Jonas & Bertsch, Valentin, 2023. "Implementing a highly adaptable method for the multi-objective optimisation of energy systems," Applied Energy, Elsevier, vol. 332(C).
    9. Reddy, S. Surender & Abhyankar, A.R. & Bijwe, P.R., 2011. "Reactive power price clearing using multi-objective optimization," Energy, Elsevier, vol. 36(5), pages 3579-3589.
    10. Zare, Mohsen & Niknam, Taher, 2013. "A new multi-objective for environmental and economic management of Volt/Var Control considering renewable energy resources," Energy, Elsevier, vol. 55(C), pages 236-252.
    11. Sanseverino, Eleonora Riva & Di Silvestre, Maria Luisa & Ippolito, Mariano Giuseppe & De Paola, Alessandra & Lo Re, Giuseppe, 2011. "An execution, monitoring and replanning approach for optimal energy management in microgrids," Energy, Elsevier, vol. 36(5), pages 3429-3436.
    12. Haddadian, Hossein & Noroozian, Reza, 2017. "Multi-microgrids approach for design and operation of future distribution networks based on novel technical indices," Applied Energy, Elsevier, vol. 185(P1), pages 650-663.
    13. Elsied, Moataz & Oukaour, Amrane & Youssef, Tarek & Gualous, Hamid & Mohammed, Osama, 2016. "An advanced real time energy management system for microgrids," Energy, Elsevier, vol. 114(C), pages 742-752.
    14. Finke, Jonas & Bertsch, Valentin, 2022. "Implementing a highly adaptable method for the multi-objective optimisation of energy systems," MPRA Paper 115504, University Library of Munich, Germany.
    15. Majid Niazkar & Seied Afzali, 2015. "Optimum Design of Lined Channel Sections," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(6), pages 1921-1932, April.
    16. Doagou-Mojarrad, Hasan & Gharehpetian, G.B. & Rastegar, H. & Olamaei, Javad, 2013. "Optimal placement and sizing of DG (distributed generation) units in distribution networks by novel hybrid evolutionary algorithm," Energy, Elsevier, vol. 54(C), pages 129-138.
    17. Obara, Shin’ya & Watanabe, Seizi & Rengarajan, Balaji, 2011. "Operation method study based on the energy balance of an independent microgrid using solar-powered water electrolyzer and an electric heat pump," Energy, Elsevier, vol. 36(8), pages 5200-5213.
    18. Aghaei, Jamshid & Alizadeh, Mohammad-Iman, 2013. "Multi-objective self-scheduling of CHP (combined heat and power)-based microgrids considering demand response programs and ESSs (energy storage systems)," Energy, Elsevier, vol. 55(C), pages 1044-1054.
    19. Moghaddam, Amjad Anvari & Seifi, Alireza & Niknam, Taher & Alizadeh Pahlavani, Mohammad Reza, 2011. "Multi-objective operation management of a renewable MG (micro-grid) with back-up micro-turbine/fuel cell/battery hybrid power source," Energy, Elsevier, vol. 36(11), pages 6490-6507.
    20. Arandian, B. & Ardehali, M.M., 2017. "Effects of environmental emissions on optimal combination and allocation of renewable and non-renewable CHP technologies in heat and electricity distribution networks based on improved particle swarm ," Energy, Elsevier, vol. 140(P1), pages 466-480.
    21. Elsied, Moataz & Oukaour, Amrane & Gualous, Hamid & Hassan, Radwan, 2015. "Energy management and optimization in microgrid system based on green energy," Energy, Elsevier, vol. 84(C), pages 139-151.
    22. Niknam, Taher & Kavousi Fard, Abdollah & Baziar, Aliasghar, 2012. "Multi-objective stochastic distribution feeder reconfiguration problem considering hydrogen and thermal energy production by fuel cell power plants," Energy, Elsevier, vol. 42(1), pages 563-573.
    23. Zhang, Houcheng & Chen, Liwei & Zhang, Jinjie & Chen, Jincan, 2014. "Performance analysis of a direct carbon fuel cell with molten carbonate electrolyte," Energy, Elsevier, vol. 68(C), pages 292-300.
    24. Shuya Zhong & Yizeng Chen & Jian Zhou & Yuanyuan Liu, 2017. "An interactive satisficing approach for multi-objective optimization with uncertain parameters," Journal of Intelligent Manufacturing, Springer, vol. 28(3), pages 535-547, March.

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