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Optimal placement, sizing and power factor of distributed generation: A comprehensive study spanning from the planning stage to the operation stage

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  • Huy, Phung Dang
  • Ramachandaramurthy, Vigna K.
  • Yong, Jia Ying
  • Tan, Kang Miao
  • Ekanayake, Janaka B.

Abstract

In this paper, an optimised framework utilising a Differential Evolution algorithm is presented to optimally integrate multiple distributed generation sources simultaneously into the distribution grid. By considering the important power system constraints, the proposed algorithm optimises the location, sizing and power factor setting for each distributed generation source to minimise network losses and maximise distributed generation integration. Various case studies were conducted at constant or varying levels of load and generation in both the planning stage and the real-time operation stage. The results of all case studies revealed that the proposed Differential Evolution-based algorithm delivered better performance in terms of network loss reduction and maximised distributed generation compared to other existing methods. The network loss reduction of 95.71% was achieved when all three parameters of placement, sizing and power factor of distributed generation were optimised simultaneously. In addition, a practical framework with a varying optimal power factor for distributed generation was designed. The optimal power factor setting for each distributed generation source was dynamically adjusted during real-time power grid operation, resulting in further minimisation of the system loss reduction. The overall loss reduction achieved was 96.04% relative to the base case of no distributed generation connection.

Suggested Citation

  • Huy, Phung Dang & Ramachandaramurthy, Vigna K. & Yong, Jia Ying & Tan, Kang Miao & Ekanayake, Janaka B., 2020. "Optimal placement, sizing and power factor of distributed generation: A comprehensive study spanning from the planning stage to the operation stage," Energy, Elsevier, vol. 195(C).
    • Handle: RePEc:eee:energy:v:195:y:2020:i:c:s0360544220301183
    DOI: 10.1016/j.energy.2020.117011
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