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A novel Q−PQV bus pair method of biomass DGs placement in distribution networks to maintain the voltage of remotely located buses

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  • Barik, Soumyabrata
  • Das, Debapriya

Abstract

This paper proposes a new DG placement and sizing technique by introducing a novel Q−PQV bus pair in distribution networks (DNRs) with seasonal variation of load demand. The principle aim of this paper is to boost and maintain the voltage of remotely located buses to predefined optimal level for seasonal load demand by integrating biomass DGs in DNRs. The selection of locations and sizes of DGs is determined by novel bus pair, which is different from conventional buses present in power system studies. For PQV bus along with active and reactive power, the voltage magnitude also is known, whereas, for Q bus, only reactive power is known. The power injection of Q bus with the load connected determines DG sizes. The inclusion of this new bus pair modifies the load flow technique, which is also given in the paper. The proposed methodology is applied on 33 bus DNR, while active power loss is taken as principal objective function. Both upf and lpf DGs are considered in this paper, and the results are compared with existing methods available in literature. The cost-benefit analysis shows the profit gained from it.

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  • Barik, Soumyabrata & Das, Debapriya, 2020. "A novel Q−PQV bus pair method of biomass DGs placement in distribution networks to maintain the voltage of remotely located buses," Energy, Elsevier, vol. 194(C).
    • Handle: RePEc:eee:energy:v:194:y:2020:i:c:s0360544219325757
    DOI: 10.1016/j.energy.2019.116880
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    References listed on IDEAS

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    1. Fu, Xueqian & Chen, Haoyong & Cai, Runqing & Yang, Ping, 2015. "Optimal allocation and adaptive VAR control of PV-DG in distribution networks," Applied Energy, Elsevier, vol. 137(C), pages 173-182.
    2. 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.
    3. Roy, Kallol & Mandal, Kamal Krishna & Mandal, Atis Chandra, 2019. "Ant-Lion Optimizer algorithm and recurrent neural network for energy management of micro grid connected system," Energy, Elsevier, vol. 167(C), pages 402-416.
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    Citations

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    Cited by:

    1. Ardiaty Arief & Muhammad Bachtiar Nappu, 2023. "Novel Hybrid Modified Modal Analysis and Continuation Power Flow Method for Unity Power Factor DER Placement," Energies, MDPI, vol. 16(4), pages 1-18, February.
    2. Fathy, Ahmed, 2022. "A novel artificial hummingbird algorithm for integrating renewable based biomass distributed generators in radial distribution systems," Applied Energy, Elsevier, vol. 323(C).
    3. Oludamilare Bode Adewuyi & Ayooluwa Peter Adeagbo & Isaiah Gbadegesin Adebayo & Harun Or Rashid Howlader & Yanxia Sun, 2021. "Modified Analytical Approach for PV-DGs Integration into a Radial Distribution Network Considering Loss Sensitivity and Voltage Stability," Energies, MDPI, vol. 14(22), pages 1-20, November.
    4. Mukhopadhyay, Bineeta & Das, Debapriya, 2021. "Optimal multi-objective expansion planning of a droop-regulated islanded microgrid," Energy, Elsevier, vol. 218(C).

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