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Multi-objective stochastic distribution feeder reconfiguration problem considering hydrogen and thermal energy production by fuel cell power plants

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  • Niknam, Taher
  • Kavousi Fard, Abdollah
  • Baziar, Aliasghar

Abstract

This paper assesses the operation and management of electrical energy, hydrogen production and thermal load supplement by the Fuel Cell Power Plants (FCPP) in the distribution systems with regard to the uncertainties which exist in the load demand as well as the price of buying natural gas for FCPPs, fuel cost for residential loads, tariff for purchasing electricity, tariff for selling electricity, hydrogen selling price, operation and maintenance cost and the price of purchasing power from the grid. Therefore, a new modified multi-objective optimization algorithm called Teacher-Learning Algorithm (TLA) is proposed to integrate the optimal operation management of Proton Exchange Membrane FCPPs (PEM-FCPPs) and the optimal configuration of the system through an economic model of the PEM-FCPP. In order to improve the total ability of TLA for global search and exploration, a new modification process is suggested such that the algorithm will search the total search space globally. Also, regarding the uncertainties of the new complicated power systems, in this paper for the first time, the DFR problem is investigated in a stochastic environment by the use of probabilistic load flow technique based on Point Estimate Method (PEM). In order to see the feasibility and the superiority of the proposed method, a standard test system is investigated as the case study. The simulation results are investigated in four different scenarios to show the effect of hydrogen production and thermal recovery more evidently.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:42:y:2012:i:1:p:563-573
    DOI: 10.1016/j.energy.2012.02.023
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    20. Abdi, Hamdi & Beigvand, Soheil Derafshi & Scala, Massimo La, 2017. "A review of optimal power flow studies applied to smart grids and microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 742-766.
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    23. Praveen Agrawal & Neeraj Kanwar & Nikhil Gupta & Khaleequr Rehman Niazi & Anil Swarnkar & Nand K. Meena & Jin Yang, 2020. "Reliability and Network Performance Enhancement by Reconfiguring Underground Distribution Systems," Energies, MDPI, vol. 13(18), pages 1-16, September.
    24. Uzlu, Ergun & Kankal, Murat & Akpınar, Adem & Dede, Tayfun, 2014. "Estimates of energy consumption in Turkey using neural networks with the teaching–learning-based optimization algorithm," Energy, Elsevier, vol. 75(C), pages 295-303.
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