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Multi-objective dynamic distribution feeder reconfiguration in automated distribution systems

Author

Listed:
  • Azizivahed, Ali
  • Narimani, Hossein
  • Fathi, Mehdi
  • Naderi, Ehsan
  • Safarpour, Hamid Reza
  • Narimani, Mohammad Rasoul

Abstract

Distribution feeder reconfiguration is an important operation problem in distribution system which has been used to improve the efficiency of distribution systems by obtaining the best combination of on/off status of the switches. It is a mixed integer non-linear program problem and hence hard to solve which necessitate employing proper optimization algorithms to converge to global optima or find near global optima. Smart grid implementation has made loads and electricity prices more volatile and as a result makes operational power system problems to be much more time dependent and more complicated rather than before. To cope with these time dependencies, it is crucial to extend the problems on different time intervals. To this end the dynamic distribution feeder reconfiguration, extension of the problem over multiple time intervals, with various objective functions including operation cost, power loss and energy not supplied is developed and investigated in this study. Time varying electricity prices and different load levels juxtapose with the effect of distributed generations are taken into account in order to generalize the proposed approach. Inherent complexities of distribution feeder reconfiguration problem have made proposing solution techniques an ongoing research topic. A new optimization algorithm is proposed to solve the proposed problem.

Suggested Citation

  • Azizivahed, Ali & Narimani, Hossein & Fathi, Mehdi & Naderi, Ehsan & Safarpour, Hamid Reza & Narimani, Mohammad Rasoul, 2018. "Multi-objective dynamic distribution feeder reconfiguration in automated distribution systems," Energy, Elsevier, vol. 147(C), pages 896-914.
    • Handle: RePEc:eee:energy:v:147:y:2018:i:c:p:896-914
    DOI: 10.1016/j.energy.2018.01.111
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Mohammad Rasoul Narimani & Maigha & Jhi-Young Joo & Mariesa Crow, 2017. "Multi-Objective Dynamic Economic Dispatch with Demand Side Management of Residential Loads and Electric Vehicles," Energies, MDPI, vol. 10(5), pages 1-18, May.
    4. Narimani, Mohammad Rasoul & Azizipanah-Abarghooee, Rasoul & Zoghdar-Moghadam-Shahrekohne, Behrouz & Gholami, Kayvan, 2013. "A novel approach to multi-objective optimal power flow by a new hybrid optimization algorithm considering generator constraints and multi-fuel type," Energy, Elsevier, vol. 49(C), pages 119-136.
    5. Esmaeili, Mobin & Sedighizadeh, Mostafa & Esmaili, Masoud, 2016. "Multi-objective optimal reconfiguration and DG (Distributed Generation) power allocation in distribution networks using Big Bang-Big Crunch algorithm considering load uncertainty," Energy, Elsevier, vol. 103(C), pages 86-99.
    6. Das, Sangeeta & Das, Debapriya & Patra, Amit, 2017. "Reconfiguration of distribution networks with optimal placement of distributed generations in the presence of remote voltage controlled bus," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 772-781.
    7. Niknam, Taher & Fard, Abdollah Kavousi & Seifi, Alireza, 2012. "Distribution feeder reconfiguration considering fuel cell/wind/photovoltaic power plants," Renewable Energy, Elsevier, vol. 37(1), pages 213-225.
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    Cited by:

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    2. Mostafa Abdo & Salah Kamel & Mohamed Ebeed & Juan Yu & Francisco Jurado, 2018. "Solving Non-Smooth Optimal Power Flow Problems Using a Developed Grey Wolf Optimizer," Energies, MDPI, vol. 11(7), pages 1-16, June.
    3. Wang, Hong-Jiang & Pan, Jeng-Shyang & Nguyen, Trong-The & Weng, Shaowei, 2022. "Distribution network reconfiguration with distributed generation based on parallel slime mould algorithm," Energy, Elsevier, vol. 244(PB).
    4. Bo Li & Panpan Zhang & Xiangjun Li & Shengxian Cao, 2019. "Distributed Absorption and Half-Search Approach for Economic Dispatch Problem in Smart Grids," Energies, MDPI, vol. 12(8), pages 1-21, April.
    5. 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).
    6. Narimani, Hossein & Razavi, Seyed-Ehsan & Azizivahed, Ali & Naderi, Ehsan & Fathi, Mehdi & Ataei, Mohammad H. & Narimani, Mohammad Rasoul, 2018. "A multi-objective framework for multi-area economic emission dispatch," Energy, Elsevier, vol. 154(C), pages 126-142.
    7. Li, J.Y. & Chen, J.J. & Wang, Y.X. & Chen, W.G., 2024. "Combining multi-step reconfiguration with many-objective reduction as iterative bi-level scheduling for stochastic distribution network," Energy, Elsevier, vol. 290(C).
    8. Terlouw, Tom & AlSkaif, Tarek & Bauer, Christian & van Sark, Wilfried, 2019. "Multi-objective optimization of energy arbitrage in community energy storage systems using different battery technologies," Applied Energy, Elsevier, vol. 239(C), pages 356-372.

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