Optimizing Solar PV Placement for Enhanced Integration in Radial Distribution Networks using Deep Learning Techniques
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
D. G. Pylarinos
2024-04-01
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| Series: | Engineering, Technology & Applied Science Research |
| Subjects: | |
| Online Access: | https://etasr.com/index.php/ETASR/article/view/6818 |
| _version_ | 1797226284039798784 |
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| author | Mohamed Ali Zdiri Bilel Dhouib Zuhair Alaas Hsan Hadj Abdallah |
| author_facet | Mohamed Ali Zdiri Bilel Dhouib Zuhair Alaas Hsan Hadj Abdallah |
| author_sort | Mohamed Ali Zdiri |
| collection | DOAJ |
| description | 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.
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| first_indexed | 2024-04-24T14:22:27Z |
| format | Article |
| id | doaj.art-50136bc9657d42f39cb81cee39c05af3 |
| institution | Directory Open Access Journal |
| issn | 2241-4487 1792-8036 |
| language | English |
| last_indexed | 2024-04-24T14:22:27Z |
| publishDate | 2024-04-01 |
| publisher | D. G. Pylarinos |
| record_format | Article |
| series | Engineering, Technology & Applied Science Research |
| spelling | doaj.art-50136bc9657d42f39cb81cee39c05af32024-04-03T06:14:17ZengD. G. PylarinosEngineering, Technology & Applied Science Research2241-44871792-80362024-04-0114210.48084/etasr.6818Optimizing Solar PV Placement for Enhanced Integration in Radial Distribution Networks using Deep Learning TechniquesMohamed Ali Zdiri0Bilel Dhouib1Zuhair Alaas2Hsan Hadj Abdallah3CEM Laboratory, Engineering School of Sfax, TunisiaCEM Laboratory, Engineering School of Sfax, Tunisia Department of Electrical Engineering, Faculty of Engineering, Jazan University, Saudi ArabiaCEM Laboratory, Engineering School of Sfax, Tunisia 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. https://etasr.com/index.php/ETASR/article/view/6818RDNPVDLRNN-LSTMload flow |
| spellingShingle | Mohamed Ali Zdiri Bilel Dhouib Zuhair Alaas Hsan Hadj Abdallah Optimizing Solar PV Placement for Enhanced Integration in Radial Distribution Networks using Deep Learning Techniques Engineering, Technology & Applied Science Research RDN PV DL RNN-LSTM load flow |
| title | Optimizing Solar PV Placement for Enhanced Integration in Radial Distribution Networks using Deep Learning Techniques |
| title_full | Optimizing Solar PV Placement for Enhanced Integration in Radial Distribution Networks using Deep Learning Techniques |
| title_fullStr | Optimizing Solar PV Placement for Enhanced Integration in Radial Distribution Networks using Deep Learning Techniques |
| title_full_unstemmed | Optimizing Solar PV Placement for Enhanced Integration in Radial Distribution Networks using Deep Learning Techniques |
| title_short | Optimizing Solar PV Placement for Enhanced Integration in Radial Distribution Networks using Deep Learning Techniques |
| title_sort | optimizing solar pv placement for enhanced integration in radial distribution networks using deep learning techniques |
| topic | RDN PV DL RNN-LSTM load flow |
| url | https://etasr.com/index.php/ETASR/article/view/6818 |
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