Call for Paper - January 2024 Edition
IJCA solicits original research papers for the January 2024 Edition. Last date of manuscript submission is December 20, 2023. Read More

Optimal Allocation and Sizing of Distributed Generation in Radial Distribution Systems using Geolocation-Aware Heuristic Optimization

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2021
Ali M. Al-Jumaili, Yılmaz Aslan, Celal Yaşar

Ali M Al-Jumaili, Yılmaz Aslan and Celal Yaşar. Optimal Allocation and Sizing of Distributed Generation in Radial Distribution Systems using Geolocation-Aware Heuristic Optimization. International Journal of Computer Applications 183(16):26-34, July 2021. BibTeX

	author = {Ali M. Al-Jumaili and Yılmaz Aslan and Celal Yaşar},
	title = {Optimal Allocation and Sizing of Distributed Generation in Radial Distribution Systems using Geolocation-Aware Heuristic Optimization},
	journal = {International Journal of Computer Applications},
	issue_date = {July 2021},
	volume = {183},
	number = {16},
	month = {Jul},
	year = {2021},
	issn = {0975-8887},
	pages = {26-34},
	numpages = {9},
	url = {},
	doi = {10.5120/ijca2021921491},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


With the growing need of energy and the recent concerns about global warming, attention has been brought towards the integration of renewable energy sources into existing power grids. The optimal location of the units and their sizes are significant parameters that influence the overall performance of the power grid. Most of the recent placement and sizing methods rely on using heuristic optimization algorithms, which rely on particles that are randomly deployed in the search space in order to recognize the optimal values that minimizes the cost value. In this study, a geolocation-aware representation of the power grid is proposed, which allows the optimizers to conduct a more efficient search, as the changes of the values matches the movement of the particles in the search space. Hence, the proposed approach has been able to significantly improve the performance of the optimizers, which in return has significantly improved the characteristics of the power grid. Accordingly, the proposed approach has been able to improve the IEEE 33 bus radial distribution test system by reducing the loss to 6.7KW, compared to the original 24.97, by adding two generation units, with significant improvement in the voltage profile, with minimum voltage of 0.9976 Pu and maximum and average voltage of 1Pu.


  1. M. Andreasson, D. V. Dimarogonas, K. H. Johansson, and H. Sandberg, "Distributed vs. centralized power systems frequency control," in 2013 European Control Conference (ECC), 2013, pp. 3524-3529.
  2. J. Liu, W. Yao, J. Wen, J. Fang, L. Jiang, H. He, et al., "Impact of power grid strength and PLL parameters on stability of grid-connected DFIG wind farm," IEEE Transactions on Sustainable Energy, vol. 11, pp. 545-557, 2019.
  3. V. Murty and A. Kumar, "Multi-objective energy management in microgrids with hybrid energy sources and battery energy storage systems," Protection and Control of Modern Power Systems, vol. 5, pp. 1-20, 2020.
  4. S. K. Injeti and V. K. Thunuguntla, "Optimal integration of DGs into radial distribution network in the presence of plug-in electric vehicles to minimize daily active power losses and to improve the voltage profile of the system using bio-inspired optimization algorithms," Protection and Control of Modern Power Systems, vol. 5, pp. 1-15, 2020.
  5. Y. Shen, W. Yao, J. Wen, H. He, and L. Jiang, "Resilient wide-area damping control using GrHDP to tolerate communication failures," IEEE Transactions on Smart Grid, vol. 10, pp. 2547-2557, 2018.
  6. K. Sun, W. Yao, J. Fang, X. Ai, J. Wen, and S. Cheng, "Impedance modeling and stability analysis of grid-connected DFIG-based wind farm with a VSC-HVDC," IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 8, pp. 1375-1390, 2019.
  7. X. Zhang, T. Tan, B. Zhou, T. Yu, B. Yang, and X. Huang, "Adaptive distributed auction-based algorithm for optimal mileage based AGC dispatch with high participation of renewable energy," International Journal of Electrical Power & Energy Systems, vol. 124, p. 106371, 2021.
  8. B. Yang, L. Yu, Y. Chen, H. Ye, R. Shao, H. Shu, et al., "Modelling, applications, and evaluations of optimal sizing and placement of distributed generations: A critical state‐of‐the‐art survey," International Journal of Energy Research, vol. 45, pp. 3615-3642, 2021.
  9. Z. Abdmouleh, A. Gastli, L. Ben-Brahim, M. Haouari, and N. A. Al-Emadi, "Review of optimization techniques applied for the integration of distributed generation from renewable energy sources," Renewable Energy, vol. 113, pp. 266-280, 2017.
  10. G. S. Chaurasia, A. K. Singh, S. Agrawal, and N. Sharma, "A meta-heuristic firefly algorithm based smart control strategy and analysis of a grid connected hybrid photovoltaic/wind distributed generation system," Solar Energy, vol. 150, pp. 265-274, 2017.
  11. I. Pisica, C. Bulac, and M. Eremia, "Optimal distributed generation location and sizing using genetic algorithms," in 2009 15th International Conference on Intelligent System Applications to Power Systems, 2009, pp. 1-6.
  12. K.-L. Du and M. Swamy, "Particle swarm optimization," in Search and Optimization by Metaheuristics, ed: Springer, 2016, pp. 153-173.
  13. J. L. Awange, B. Paláncz, R. H. Lewis, and L. Völgyesi, "Particle swarm optimization," in Mathematical Geosciences, ed: Springer, 2018, pp. 167-184.
  14. A. Engelbrecht, "Particle swarm optimization," in Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation, 2014, pp. 381-406.
  15. S. Chavan and N. P. Adgokar, "An overview on particle swarm optimization: basic concepts and modified variants," International Journal of Science and Research, vol. 4, pp. 255-260, 2015.
  16. D. Karaboga, B. Basturk, A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm, Journal of Global Optimization 39 (3) (2007) 459–471.
  17. S. Mirjalili, " SCA: A Sine Cosine Algorithm for solving optimization," Knowledge-Based Systems, vol. 96, pp. 120-133, 2016.
  18. J. Ponoćko, Data Analytics-Based Demand Profiling and Advanced Demand Side Management for Flexible Operation of Sustainable Power Networks: Springer Nature, 2020.
  19. J. Savier and D. Das, "Impact of network reconfiguration on loss allocation of radial distribution systems," IEEE Transactions on Power Delivery, vol. 22, pp. 2473-2480, 2007.
  20. T. Brown, J. Hörsch, and D. Schlachtberger, "PyPSA: Python for power system analysis," arXiv preprint arXiv:1707.09913, 2017.
  21. G. H. de Rosa, D. Rodrigues, and J. P. Papa,"Opytimizer: A nature-inspired python optimizer," arXiv preprint arXiv:1912.13002, 2019.
  22. W. Haider, S. Hassan, A. Mehdi, A. Hussain, G. O. M. Adjayeng, and C.-H. Kim, "Voltage profile enhancement and loss minimization using optimal placement and sizing of distributed generation in reconfigured network," Machines, vol. 9, p. 20, 2021.


Distributed generation, Radial distribution system,Optimal size, Optimal location, Active power losses.