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Novel hybrid fuzzy-Bees algorithm for optimal feeder multi-objective reconfiguration by considering multiple-distributed generation

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  • Tolabi, Hajar Bagheri
  • Ali, Mohd Hasan
  • Shahrin Bin Md Ayob,
  • Rizwan, M.

Abstract

This paper presents a hybrid optimal multi-objective reconfiguration method to determine an optimal size and location of multiple-distributed generation (DG) in a distribution feeder. The purposes of this research are to mitigate losses, improve voltage profile and equalize the feeder load balancing in power distribution systems. To reduce the search space, the Improved Analytical (IA) method is employed to select the optimum candidate locations for multiple-DG, while the Bees algorithm (BA) approach as a population based algorithm is used to simultaneously reconfigure and identify the optimal capacity for installation of DG units in the distribution network. In order for the algorithm to facilitate ability for multi-objective search the optimization problem is formulated to minimize fuzzy performance indices. The proposed method is validated using the IEEE 33 bus test system at nominal load. The obtained results revealed that this proposed hybrid method has superior accuracy and efficient convergence property over the other intelligent search algorithms. It is also can be concluded that the multi-objective simultaneous placement of DGs along with multi-objective reconfiguration can be more beneficial than separate single-objective optimization.

Suggested Citation

  • Tolabi, Hajar Bagheri & Ali, Mohd Hasan & Shahrin Bin Md Ayob, & Rizwan, M., 2014. "Novel hybrid fuzzy-Bees algorithm for optimal feeder multi-objective reconfiguration by considering multiple-distributed generation," Energy, Elsevier, vol. 71(C), pages 507-515.
    • Handle: RePEc:eee:energy:v:71:y:2014:i:c:p:507-515
    DOI: 10.1016/j.energy.2014.04.099
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    References listed on IDEAS

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    1. Zidan, Aboelsood & El-Saadany, Ehab F., 2013. "Distribution system reconfiguration for energy loss reduction considering the variability of load and local renewable generation," Energy, Elsevier, vol. 59(C), pages 698-707.
    2. Aman, M.M. & Jasmon, G.B. & Bakar, A.H.A. & Mokhlis, H., 2014. "A new approach for optimum simultaneous multi-DG distributed generation Units placement and sizing based on maximization of system loadability using HPSO (hybrid particle swarm optimization) algorithm," Energy, Elsevier, vol. 66(C), pages 202-215.
    3. 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.
    4. 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.
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    Citations

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    Cited by:

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    2. Arasteh, Hamidreza & Sepasian, Mohammad Sadegh & Vahidinasab, Vahid, 2016. "An aggregated model for coordinated planning and reconfiguration of electric distribution networks," Energy, Elsevier, vol. 94(C), pages 786-798.
    3. Tolabi, H.B. & Ara, A. Lashkar & Hosseini, R., 2020. "A new thief and police algorithm and its application in simultaneous reconfiguration with optimal allocation of capacitor and distributed generation units," Energy, Elsevier, vol. 203(C).
    4. Mukhopadhyay, Bineeta & Das, Debapriya, 2020. "Multi-objective dynamic and static reconfiguration with optimized allocation of PV-DG and battery energy storage system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    5. Prakash, Prem & Khatod, Dheeraj K., 2016. "Optimal sizing and siting techniques for distributed generation in distribution systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 111-130.
    6. Sedighizadeh, Mostafa & Esmaili, Masoud & Esmaeili, Mobin, 2014. "Application of the hybrid Big Bang-Big Crunch algorithm to optimal reconfiguration and distributed generation power allocation in distribution systems," Energy, Elsevier, vol. 76(C), pages 920-930.

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