Artificial Intelligence for Hosting Capacity Analysis: A Systematic Literature Review
Distribution network operators face technical and operational challenges in integrating the increasing number of distributed energy resources (DER) with the distribution network. The hosting capacity analysis quantifies the level of power quality violation on the network due to the high penetration...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/4/1864 |
| _version_ | 1797621268287062016 |
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| author | Md Tariqul Islam M. J. Hossain |
| author_facet | Md Tariqul Islam M. J. Hossain |
| author_sort | Md Tariqul Islam |
| collection | DOAJ |
| description | Distribution network operators face technical and operational challenges in integrating the increasing number of distributed energy resources (DER) with the distribution network. The hosting capacity analysis quantifies the level of power quality violation on the network due to the high penetration of the DER, such as over voltage, under voltage, transformer and feeder overloading, and protection failures. Real-time monitoring of the power quality factors such as the voltage, current, angle, frequency, harmonics and overloading that would help the distribution network operators overcome the challenges created by the high penetration of the DER. In this paper, different conventional hosting capacity analysis methods have been discussed. These methods have been compared based on the network constraints, impact factors, required input data, computational efficiency, and output accuracy. The artificial intelligence approaches of the hosting capacity analysis for the real-time monitoring of distribution network parameters have also been covered in this paper. Different artificial intelligence techniques have been analysed for sustainable integration, power system optimisation, and overcoming real-time monitoring challenges of conventional hosting capacity analysis methods. An overview of the conventional hosting capacity analysis methods, artificial intelligence techniques for overcoming the challenges of distributed energy resources integration, and different impact factors affecting the real-time hosting capacity analysis has been summarised. The distribution system operators and researchers will find the review paper as an easy reference for planning and further research. Finally, it is evident that artificial intelligence techniques could be a better alternative solution for real-time estimation and forecasting of the distribution network hosting capacity considering the intermittent nature of the DER, consumer loads, and network constraints. |
| first_indexed | 2024-03-11T08:53:25Z |
| format | Article |
| id | doaj.art-fd8a92b1ec0b4d7280561736a43fe76c |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T08:53:25Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-fd8a92b1ec0b4d7280561736a43fe76c2023-11-16T20:18:51ZengMDPI AGEnergies1996-10732023-02-01164186410.3390/en16041864Artificial Intelligence for Hosting Capacity Analysis: A Systematic Literature ReviewMd Tariqul Islam0M. J. Hossain1School of Electrical and Data Engineering, University of Technology Sydney, Sydney, NSW 2007, AustraliaSchool of Electrical and Data Engineering, University of Technology Sydney, Sydney, NSW 2007, AustraliaDistribution network operators face technical and operational challenges in integrating the increasing number of distributed energy resources (DER) with the distribution network. The hosting capacity analysis quantifies the level of power quality violation on the network due to the high penetration of the DER, such as over voltage, under voltage, transformer and feeder overloading, and protection failures. Real-time monitoring of the power quality factors such as the voltage, current, angle, frequency, harmonics and overloading that would help the distribution network operators overcome the challenges created by the high penetration of the DER. In this paper, different conventional hosting capacity analysis methods have been discussed. These methods have been compared based on the network constraints, impact factors, required input data, computational efficiency, and output accuracy. The artificial intelligence approaches of the hosting capacity analysis for the real-time monitoring of distribution network parameters have also been covered in this paper. Different artificial intelligence techniques have been analysed for sustainable integration, power system optimisation, and overcoming real-time monitoring challenges of conventional hosting capacity analysis methods. An overview of the conventional hosting capacity analysis methods, artificial intelligence techniques for overcoming the challenges of distributed energy resources integration, and different impact factors affecting the real-time hosting capacity analysis has been summarised. The distribution system operators and researchers will find the review paper as an easy reference for planning and further research. Finally, it is evident that artificial intelligence techniques could be a better alternative solution for real-time estimation and forecasting of the distribution network hosting capacity considering the intermittent nature of the DER, consumer loads, and network constraints.https://www.mdpi.com/1996-1073/16/4/1864artificial intelligencemachine learningdeep learninghosting capacityimpact factorsoptimisation |
| spellingShingle | Md Tariqul Islam M. J. Hossain Artificial Intelligence for Hosting Capacity Analysis: A Systematic Literature Review Energies artificial intelligence machine learning deep learning hosting capacity impact factors optimisation |
| title | Artificial Intelligence for Hosting Capacity Analysis: A Systematic Literature Review |
| title_full | Artificial Intelligence for Hosting Capacity Analysis: A Systematic Literature Review |
| title_fullStr | Artificial Intelligence for Hosting Capacity Analysis: A Systematic Literature Review |
| title_full_unstemmed | Artificial Intelligence for Hosting Capacity Analysis: A Systematic Literature Review |
| title_short | Artificial Intelligence for Hosting Capacity Analysis: A Systematic Literature Review |
| title_sort | artificial intelligence for hosting capacity analysis a systematic literature review |
| topic | artificial intelligence machine learning deep learning hosting capacity impact factors optimisation |
| url | https://www.mdpi.com/1996-1073/16/4/1864 |
| work_keys_str_mv | AT mdtariqulislam artificialintelligenceforhostingcapacityanalysisasystematicliteraturereview AT mjhossain artificialintelligenceforhostingcapacityanalysisasystematicliteraturereview |