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Data Clustering for Optimal Photovoltaic-Distributed Generation Placement in an Active Distribution Network

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Author:

Nkado, Franklin (Nkado, Franklin.) | Oladeji, Ifedayo (Oladeji, Ifedayo.) | Nkado, Fredrick (Nkado, Fredrick.) | Unfold

Indexed by:

EI CPCI-S Scopus Engineering Village

Abstract:

The growing interest in extensive distribution generating unit (DG) integration into the grid creates new challenges for power system planners and operators. The non-optimal placement of renewable energy sources-based (RES) DG units in distribution networks impacts grids' reliability and stability. This paper proposes a K-means clustering technique for the optimal placement of multiple photovoltaic distributed generation (PVDG) units using voltage stability index (VSI), the voltage risk index (VRI), and the hosting capacity under different PVDG local voltage control techniques. The proposed technique was tested on the IEEE 33-bus radial distribution network. After the optimal placement of the PVDG units, the VSI and VRI for the base network were improved 32.76% and 90.1%, respectively by the best simulation case. The voltage profile was improved by 3.6% and the total active power loss was reduced by 67.7%. The results show that the proposed technique is effective to allocate multiple PVDG units for distribution network planning and expansion as well for increasing the PVDG penetration while minimizing the voltage stability and risk indices. © 2022 IEEE.

Keyword:

Distributed power generation K-means clustering Renewable energy resources Stability Voltage control

Author Community:

  • [ 1 ] [Nkado, Franklin]Electrical & Electronic Engineering, Auckland University of Technology, Auckland, New Zealand
  • [ 2 ] [Oladeji, Ifedayo]Electrical & Electronic Engineering, Auckland University of Technology, Auckland, New Zealand
  • [ 3 ] [Nkado, Fredrick]School of Electrical Engineering, Xi'an Jiaotong University, Xi'an, China
  • [ 4 ] [Zamora, Ramon]Electrical & Electronic Engineering, Auckland University of Technology, Auckland, New Zealand

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Year: 2022

Page: 497-501

Language: English

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 1

30 Days PV: 6

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