Distribution Network Performance Enhancement Using Reconfiguration Technique based on Gravitational Search Algorithm

Authors

  • Ola Subhi Bdran lecturer in Palestine Technical University – Kadoorie (PTUK)

DOI:

https://doi.org/10.33977/2106-000-008-001

Keywords:

Gravitational Search Algorithm, Optimization Technique, Voltage Profile, Network Reconfiguration, Power Loss

Abstract

Objectives: The main goals of this work are to minimize network power loss and enhance the system's voltage profile (VF).

Methods: This work presents a novel methodology that simultaneously optimizes Distribution Network Reconfiguration (DNR), Distributed Generation (DG) sizing, and DG placement using the Gravitational Search Algorithm (GSA) optimization technique. The DNR approach helps reduce power loss, but its effectiveness is limited when applied alone. Similarly, optimizing DG sizing and placement can further minimize power loss, but improper integration with DNR may lead to increased power loss and voltage fluctuations. Hence, it is essential to develop an efficient optimization strategy that simultaneously determines the optimal DG size and location while achieving optimal DNR.

Results: For the IEEE 33-bus network, active and reactive power losses were reduced by 67.488% and 64.88%, respectively. Similarly, for the IEEE 69-bus network, the reductions in active and reactive power losses were 82.55% and 62.25%, respectively.

Conclusions: The findings show that adjusting the size and location of distributed generation units (DGs) while configuring the network significantly improves the voltage profile and reduces losses.

Author Biography

Ola Subhi Bdran, lecturer in Palestine Technical University – Kadoorie (PTUK)

assistant professor

References

Abdelaziz, M. (2017). Distribution network reconfiguration using a genetic algorithm with varying population size. Electric Power Systems Research, 142, 9-11.

Avchat, H. S., & Mhetre, S. (2020). Optimal placement of distributed generation in distribution network using particle swarm optimization. Paper presented at the 2020 International Conference for Emerging Technology (INCET).

Badran, O. (2023). IEEE-69 Distribution Network Performance Improvement by Simultaneously Optimal Distributed Generation Sizing and Location Using PSO Algorithm.

Badran, O., & Jallad, J. (2023). Multi-Objective Decision Approach for Optimal Real-Time Switching Sequence of Network Reconfiguration Realizing Maximum Load Capacity. Energies, 16(19), 6779.

Badran, O., Mekhilef, S., Mokhlis, H., & Dahalan, W. (2017). Optimal reconfiguration of distribution system connected with distributed generations: A review of different methodologies. Renewable and Sustainable Energy Reviews, 73, 854-867.

Badran, O., Mokhlis, H., Mekhilef, S., & Dahalan, W. (2018). Multi-Objective network reconfiguration with optimal DG output using meta-heuristic search algorithms. Arabian Journal for Science and Engineering, 43, 2673-2686.

Essallah, S., & Khedher, A. (2020). Optimization of distribution system operation by network reconfiguration and DG integration using the MPSO algorithm. Renewable Energy Focus, 34, 37-46.

Imran, A. M., Kowsalya, M., & Kothari, D. (2014). A novel integration technique for optimal network reconfiguration and distributed generation placement in power distribution networks. International Journal of Electrical Power & Energy Systems, 63, 461-472.

Karunarathne, E., Pasupuleti, J., Ekanayake, J., & Almeida, D. (2021). The optimal placement and sizing of distributed generation in an active distribution network with several soft open points. Energies, 14(4), 1084.

Mohandas, N., Balamurugan, R., & Lakshminarasimman, L. (2015). Optimal location and sizing of real power DG units to improve the voltage stability in the distribution system using the ABC algorithm united with chaos. International Journal of Electrical Power & Energy Systems, 66, 41-52.

Moradi, M. H., & Abedini, M. (2012). A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems. International Journal of Electrical Power & Energy Systems, 34(1), 66-74.

Nguyen, T. T., & Truong, A. V. (2015). Distribution network reconfiguration for power loss minimization and voltage profile improvement using cuckoo search algorithm. International Journal of Electrical Power & Energy Systems, 68, 233-242.

Pegado, R., Ñaupari, Z., Molina, Y., & Castillo, C. (2019). Radial distribution network reconfiguration for power losses reduction based on improved selective BPSO. Electric Power Systems Research, 169, 206-213.

Rahim, M. N. A., Mokhlis, H., Bakar, A. H. A., Rahman, M. T., Badran, O., & Mansor, N. N. (2019). Protection coordination toward optimal network reconfiguration and DG sizing. IEEE Access, 7, 163700-163718.

Rao, R. S., Ravindra, K., Satish, K., & Narasimham, S. (2012). Power loss minimization in distribution system using network reconfiguration in the presence of distributed generation. IEEE transactions on power systems, 28(1), 317-325.

Raut, U., & Mishra, S. (2020). An improved sine–cosine algorithm for simultaneous network reconfiguration and DG allocation in power distribution systems. Applied Soft Computing, 92, 106293.

Yan, J., Shamim, T., Chou, S., Desideri, U., & Li, H. (2017). Clean, efficient and affordable energy for a sustainable future. Applied Energy, 185, 953-962.

Downloads

Published

2025-06-02

How to Cite

Bdran, O. S. (2025). Distribution Network Performance Enhancement Using Reconfiguration Technique based on Gravitational Search Algorithm. Palestinian Journal of Technology and Applied Sciences (PJTAS), 1(8). https://doi.org/10.33977/2106-000-008-001

Most read articles by the same author(s)

Obs.: This plugin requires at least one statistics/report plugin to be enabled. If your statistics plugins provide more than one metric then please also select a main metric on the admin's site settings page and/or on the journal manager's settings pages.