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Reconfiguration of distribution networks with optimal placement of distributed generations in the presence of remote voltage controlled bus

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  • Das, Sangeeta
  • Das, Debapriya
  • Patra, Amit

Abstract

This paper presents the reconfiguration of the distribution network in the presence of distributed generations (DGs) by considering two bus types i.e., P bus and PQV bus (remotely voltage controlled bus). The ‘P’ bus is represented by active power specification only whereas the PQV bus is one whose voltage is remotely controlled by the P bus. A methodology is proposed to select the P bus for controlling the voltage magnitude of remotely located PQV bus. A sensitivity analysis approach is used for selecting the buses for the placement of DGs operating at unity power factor. The placement of DGs is done in two ways i.e., non-sequential placement and sequential placement of DG in a distribution network. Genetic algorithm (GA) technique is used for the optimization of DGs followed by network reconfiguration. The objective function for network reconfiguration in this paper is considered to be real power loss reduction. Effectiveness of the proposed method is demonstrated through examples of 33 bus and 69 bus distribution networks.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:rensus:v:73:y:2017:i:c:p:772-781
    DOI: 10.1016/j.rser.2017.01.055
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    References listed on IDEAS

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    1. Viral, Rajkumar & Khatod, D.K., 2012. "Optimal planning of distributed generation systems in distribution system: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 5146-5165.
    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. 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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    Cited by:

    1. 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).
    2. Roy, Nibir Baran & Das, Debapriya, 2024. "Stochastic power allocation of distributed tri-generation plants and energy storage units in a zero bus microgrid with electric vehicles and demand response," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    3. 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.
    4. 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.
    5. Papadimitrakis, M. & Giamarelos, N. & Stogiannos, M. & Zois, E.N. & Livanos, N.A.-I. & Alexandridis, A., 2021. "Metaheuristic search in smart grid: A review with emphasis on planning, scheduling and power flow optimization applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).

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