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Multi-objective stochastic Distribution Feeder Reconfiguration from the reliability point of view

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  • Kavousi-Fard, Abdollah
  • Niknam, Taher

Abstract

The main purpose of this paper is to assess the DFR (Distribution Feeder Reconfiguration) strategy as a costless technique to enhance the reliability of the distribution systems. The objective functions to be investigated are: SAIFI (System Average Interruption Frequency Index), AENS (Average Energy Not Supplied), total active power losses and the total network cost. In order to observe the effect of renewable energy sources on the reliability of the power system, wind power source as a popular type of renewable energy source is also considered in the system. In addition, to make the analysis more reliable, the uncertainty of the forecast error of active and reactive loads, wind speed variations as well as the failure rate and repair rate parameters are modeled though the probabilistic load flow. Since the problem investigated is a type of discrete, nonlinear and non-convex optimization problem, a novel self adaptive modified optimization algorithm based on the BA (bat algorithm) is proposed too. The proposed self adaptive modification method makes use of three sub-modifications to give each bat (solution) a choice of preferences during the optimization process. The efficiency and feasibility of the proposed method are studied through a standard IEEE (Institute of Electrical and Electronics Engineers) test system.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:64:y:2014:i:c:p:342-354
    DOI: 10.1016/j.energy.2013.08.060
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    References listed on IDEAS

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    9. Niknam, Taher & Meymand, Hamed Zeinoddini & Nayeripour, Majid, 2010. "A practical algorithm for optimal operation management of distribution network including fuel cell power plants," Renewable Energy, Elsevier, vol. 35(8), pages 1696-1714.
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    Cited by:

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    4. Khorshidi, Reza & Shabaninia, Faridon & Niknam, Taher, 2016. "A new smart approach for state estimation of distribution grids considering renewable energy sources," Energy, Elsevier, vol. 94(C), pages 29-37.
    5. 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).
    6. Syed Ali Abbas Kazmi & Muhammad Khuram Shahzad & Dong Ryeol Shin, 2017. "Multi-Objective Planning Techniques in Distribution Networks: A Composite Review," Energies, MDPI, vol. 10(2), pages 1-44, February.
    7. Abdulaziz Alanazi & Mohana Alanazi, 2022. "Artificial Electric Field Algorithm-Pattern Search for Many-Criteria Networks Reconfiguration Considering Power Quality and Energy Not Supplied," Energies, MDPI, vol. 15(14), pages 1-27, July.
    8. Azizivahed, Ali & Narimani, Hossein & Naderi, Ehsan & Fathi, Mehdi & Narimani, Mohammad Rasoul, 2017. "A hybrid evolutionary algorithm for secure multi-objective distribution feeder reconfiguration," Energy, Elsevier, vol. 138(C), pages 355-373.
    9. Kavousi-Fard, Abdollah & Abbasi, Alireza & Rostami, Mohammad-Amin & Khosravi, Abbas, 2015. "Optimal distribution feeder reconfiguration for increasing the penetration of plug-in electric vehicles and minimizing network costs," Energy, Elsevier, vol. 93(P2), pages 1693-1703.
    10. Esmaeeli, M. & Kazemi, A. & Shayanfar, H.A. & Haghifam, M.-R., 2015. "Multistage distribution substations planning considering reliability and growth of energy demand," Energy, Elsevier, vol. 84(C), pages 357-364.
    11. kianmehr, Ehsan & Nikkhah, Saman & Rabiee, Abbas, 2019. "Multi-objective stochastic model for joint optimal allocation of DG units and network reconfiguration from DG owner’s and DisCo’s perspectives," Renewable Energy, Elsevier, vol. 132(C), pages 471-485.
    12. Sultana, Beenish & Mustafa, M.W. & Sultana, U. & Bhatti, Abdul Rauf, 2016. "Review on reliability improvement and power loss reduction in distribution system via network reconfiguration," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 297-310.
    13. 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.
    14. Kavousi-Fard, Abdollah & Abunasri, Alireza & Zare, Alireza & Hoseinzadeh, Rasool, 2014. "Impact of plug-in hybrid electric vehicles charging demand on the optimal energy management of renewable micro-grids," Energy, Elsevier, vol. 78(C), pages 904-915.
    15. Mahmoud M. Sayed & Mohamed Y. Mahdy & Shady H. E. Abdel Aleem & Hosam K. M. Youssef & Tarek A. Boghdady, 2022. "Simultaneous Distribution Network Reconfiguration and Optimal Allocation of Renewable-Based Distributed Generators and Shunt Capacitors under Uncertain Conditions," Energies, MDPI, vol. 15(6), pages 1-27, March.
    16. Aghajani, Saemeh & Kalantar, Mohsen, 2017. "Optimal scheduling of distributed energy resources in smart grids: A complementarity approach," Energy, Elsevier, vol. 141(C), pages 2135-2144.
    17. 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.
    18. Kavousi-Fard, Abdollah & Khodaei, Amin, 2016. "Efficient integration of plug-in electric vehicles via reconfigurable microgrids," Energy, Elsevier, vol. 111(C), pages 653-663.
    19. Haben, Stephen & Arora, Siddharth & Giasemidis, Georgios & Voss, Marcus & Vukadinović Greetham, Danica, 2021. "Review of low voltage load forecasting: Methods, applications, and recommendations," Applied Energy, Elsevier, vol. 304(C).
    20. Zhao, Jiangbin & Si, Shubin & Cai, Zhiqiang, 2019. "A multi-objective reliability optimization for reconfigurable systems considering components degradation," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 104-115.

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