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New method for optimal allocation of distribution generation aimed at active losses reduction

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  • da Rosa, William M.
  • Teixeira, Julio C.
  • Belati, Edmarcio A.

Abstract

A new methodology for studying the effect of wind power intermittency on electric power systems is proposed in this paper. The proposed stochastic method is based on the optimal power flow and sensitivity analysis techniques. These techniques are applied on a computational tool capable of allocating the intermittent energy with an exhaustive search technique with low computation time. The methodology was applied to reduce losses in the distribution systems of 34 and 70 buses. The results are compared with fixed power allocation considering the mean power of three different conditions: the average wind speed; generator capacity factor and maximum generator capacity. They showed that a stochastic method that considers each value of wind speed is necessary to determine the correct bus to allocate intermittent wind power.

Suggested Citation

  • da Rosa, William M. & Teixeira, Julio C. & Belati, Edmarcio A., 2018. "New method for optimal allocation of distribution generation aimed at active losses reduction," Renewable Energy, Elsevier, vol. 123(C), pages 334-341.
    • Handle: RePEc:eee:renene:v:123:y:2018:i:c:p:334-341
    DOI: 10.1016/j.renene.2018.02.065
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    References listed on IDEAS

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    1. Rezaee Jordehi, Ahmad, 2016. "Allocation of distributed generation units in electric power systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 893-905.
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    Cited by:

    1. de Oliveira, Glauber Cardoso & Bertone, Edoardo & Stewart, Rodney A., 2022. "Challenges, opportunities, and strategies for undertaking integrated precinct-scale energy–water system planning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    2. Sadeghian, Hamidreza & Wang, Zhifang, 2020. "A novel impact-assessment framework for distributed PV installations in low-voltage secondary networks," Renewable Energy, Elsevier, vol. 147(P1), pages 2179-2194.
    3. Monteiro, Raul V.A. & Guimarães, Geraldo C. & Silva, Fernando Bento & da Silva Teixeira, Raoni F. & Carvalho, Bismarck C. & Finazzi, Antônio de P. & de Vasconcellos, Arnulfo B., 2018. "A medium-term analysis of the reduction in technical losses on distribution systems with variable demand using artificial neural networks: An Electrical Energy Storage approach," Energy, Elsevier, vol. 164(C), pages 1216-1228.

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