Optimizing Solar PV Placement for Enhanced Integration in Radial Distribution Networks using Deep Learning Techniques


  • Mohamed Ali Zdiri CEM Laboratory, Engineering School of Sfax, Tunisia
  • Bilel Dhouib CEM Laboratory, Engineering School of Sfax, Tunisia
  • Zuhair Alaas Department of Electrical Engineering, Faculty of Engineering, Jazan University, Saudi Arabia
  • Hsan Hadj Abdallah CEM Laboratory, Engineering School of Sfax, Tunisia
Volume: 14 | Issue: 2 | Pages: 13681-13687 | April 2024 | https://doi.org/10.48084/etasr.6818


This study introduces a highly effective technique to address the load flow challenge in Radial Distribution Networks (RDNs). The proposed approach leverages two matrices derived from the topological features of distribution networks to provide an optimal solution to handle load flow challenges. To assess the efficacy of this technique, simulations were executed on an IEEE 33-bus radial distribution system using MATLAB. Deep Learning (DL) has become a powerful artificial intelligence technique that excels at interpreting power grid datasets. Thus, a data-driven methodology is presented that incorporates an advanced Long-Short-Term-Memory (LSTM) network. Employing the Recurrent Neural Network with the LSTM (RNN-LSTM) technique based on these simulations, the study precisely identifies the optimal placement of an integrated PV generator within the radial network. The application of DL techniques, specifically LSTM networks, exemplifies the potential of data-driven approaches in enhancing decision-making processes. The results of this study highlight the potential of RNN-LSTM for the optimal integration of PV generators and for ameliorating the reliability of RDNs.


RDN, PV, DL, RNN-LSTM, load flow


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J. A. M. Rupa and S. Ganesh, "Power Flow Analysis for Radial Distribution System Using Backward/Forward Sweep Method," International Journal of Electrical and Computer Engineering, vol. 8, no. 10, pp. 1628–1632, Jan. 2015.

M. R. Shakarami, H. Beiranvand, A. Beiranvand, and E. Sharifipour, "A recursive power flow method for radial distribution networks: Analysis, solvability and convergence," International Journal of Electrical Power & Energy Systems, vol. 86, pp. 71–80, Mar. 2017.

B. de Nadai Nascimento, A. C. Zambroni de Souza, J. G. de Carvalho Costa, and M. Castilla, "Load shedding scheme with under-frequency and undervoltage corrective actions to supply high priority loads in islanded microgrids," IET Renewable Power Generation, vol. 13, no. 11, pp. 1981–1989, 2019.

M. A. Zdiri, A. S. Alshammari, A. A. Alzamil, M. B. Ammar, and H. H. Abdallah, "Optimal Shedding Against Voltage Collapse Based on Genetic Algorithm," Engineering, Technology & Applied Science Research, vol. 11, no. 5, pp. 7695–7701, Oct. 2021.

P. S. Bhowmik, S. P. Bose, D. V. Rajan, and S. Deb, "Power flow analysis of power system using Power Perturbation method," in 2011 IEEE Power Engineering and Automation Conference, Sep. 2011, vol. 3, pp. 380–384.

R. Yan, T. K. Saha, N. Modi, N. A. Masood, and M. Mosadeghy, "The combined effects of high penetration of wind and PV on power system frequency response," Applied Energy, vol. 145, pp. 320–330, May 2015.

S. M. Ghania, K. R. M. Mahmoud, and A. M. Hashmi, "Α Reliability Study of Renewable Energy Resources and their Integration with Utility Grids," Engineering, Technology & Applied Science Research, vol. 12, no. 5, pp. 9078–9086, Oct. 2022.

V. V. Lakshmi, G. V. Naga, and A. J. Laxmi, "Optimal Allocation and Sizing of Multiple Distributed Generators Using Genetic Algorithm," in International Conference on Advances in Communication, Network, and Computing, 2014, pp. 305–312.

O. Mohamed, M. Mohamed, and A. Kansab, "Optimal Placement and Sizing of Distributed Generation Sources in Distribution Networks Using SPEA Algorithm," International Journal on Electrical Engineering and Informatics, vol. 11, no. 2, pp. 326–340, Jun. 2019.

M. A. Zdiri, B. Dhouib, Z. Alaas, F. B. Salem, and H. H. Abdallah, "Load Flow Analysis and the Impact of a Solar PV Generator in a Radial Distribution Network," Engineering, Technology & Applied Science Research, vol. 13, no. 1, pp. 10078–10085, Feb. 2023.

J. Schmidhuber, "Deep Learning," Scholarpedia, vol. 10, no. 11, Nov. 2015, Art. no. 32832.

J. Ahmad, H. Farman, and Z. Jan, "Deep Learning Methods and Applications," in Deep Learning: Convergence to Big Data Analytics, M. Khan, B. Jan, and H. Farman, Eds. Singapore: Springer, 2019, pp. 31–42.

S. F. Ahmed et al., "Deep learning modelling techniques: current progress, applications, advantages, and challenges," Artificial Intelligence Review, vol. 56, no. 11, pp. 13521–13617, Nov. 2023.

H. Li, A. Zhang, X. Shen, and J. Xu, "A load flow method for weakly meshed distribution networks using powers as flow variables," International Journal of Electrical Power & Energy Systems, vol. 58, pp. 291–299, Jun. 2014.

J.-H. Teng and C. Y. Chang, "A novel and fast three-phase load flow for unbalanced radial distribution systems," IEEE Transactions on Power Systems, vol. 17, no. 4, pp. 1238–1244, Nov. 2002.

M. A. Fares, L. Atik, G. Bachir, and M. Aillerie, "Photovoltaic panels characterization and experimental testing," Energy Procedia, vol. 119, pp. 945–952, Jul. 2017.

Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," Nature, vol. 521, no. 7553, pp. 436–444, May 2015.

J. Schmidhuber, "Deep learning in neural networks: An overview," Neural Networks, vol. 61, pp. 85–117, Jan. 2015.

A. Oka, N. Ishimura, and S. Ishihara, "A New Dawn for the Use of Artificial Intelligence in Gastroenterology, Hepatology and Pancreatology," Diagnostics, vol. 11, no. 9, Sep. 2021, Art. no. 1719.

I. Sutskever, O. Vinyals, and Q. V. Le, "Sequence to Sequence Learning with Neural Networks," in Advances in Neural Information Processing Systems, 2014, vol. 27.

X. Wang, W. Jiang, and Z. Luo, "Combination of Convolutional and Recurrent Neural Network for Sentiment Analysis of Short Texts," in Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, Osaka, Japan, Sep. 2016, pp. 2428–2437, [Online]. Available: https://aclanthology.org/C16-1229.

T. Yao, Y. Pan, Y. Li, and T. Mei, "Incorporating Copying Mechanism in Image Captioning for Learning Novel Objects," in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, Jul. 2017, pp. 5263–5271.

J. Li, D. Xiong, Z. Tu, M. Zhu, M. Zhang, and G. Zhou, "Modeling Source Syntax for Neural Machine Translation." arXiv, May 02, 2017.

T. Mikolov, S. Kombrink, L. Burget, J. Černocký, and S. Khudanpur, "Extensions of recurrent neural network language model," in 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, Czech Republic, May 2011, pp. 5528–5531.

V. Vita, "Development of a Decision-Making Algorithm for the Optimum Size and Placement of Distributed Generation Units in Distribution Networks," Energies, vol. 10, no. 9, Sep. 2017, Art. no. 1433.

S. Katyara et al., "Leveraging a Genetic Algorithm for the Optimal Placement of Distributed Generation and the Need for Energy Management Strategies Using a Fuzzy Inference System," Electronics, vol. 10, no. 2, Jan. 2021, Art. no. 172.

R. Deshmukh and A. Kalage, "Optimal Placement and Sizing of Distributed Generator in Distribution System Using Artificial Bee Colony Algorithm," in 2018 IEEE Global Conference on Wireless Computing and Networking (GCWCN), Lonavala, India, Nov. 2018, pp. 178–181.

M. A. Ali, A. R. Bhatti, A. Rasool, M. Farhan, and E. Esenogho, "Optimal Location and Sizing of Photovoltaic-Based Distributed Generations to Improve the Efficiency and Symmetry of a Distribution Network by Handling Random Constraints of Particle Swarm Optimization Algorithm," Symmetry, vol. 15, no. 9, Sep. 2023, Art. no. 1752.


How to Cite

M. A. Zdiri, B. Dhouib, Z. Alaas, and H. Hadj Abdallah, “Optimizing Solar PV Placement for Enhanced Integration in Radial Distribution Networks using Deep Learning Techniques”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 2, pp. 13681–13687, Apr. 2024.


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