Artificial Intelligence for Hosting Capacity Analysis: A Systematic Literature Review

Md Tariqul Islam and M. J. Hossain ()
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Md Tariqul Islam: School of Electrical and Data Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia
M. J. Hossain: School of Electrical and Data Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia

Energies, 2023, vol. 16, issue 4, 1-33

Abstract: 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.

Keywords: artificial intelligence; machine learning; deep learning; hosting capacity; impact factors; optimisation; distributed energy resources (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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