Real-time PV hosting capacity analysis of distribution networks using pilot buses data and neural networks

This paper presents a feed-forward neural network (NN) for hosting capacity analysis (HCA) in real-time using information from a limited number of buses, addressing the challenge of data scarcity and the need for efficient HCA with minimal input data. A limited number of buses in the distribution network is identified, referred to as pilot buses, using an approach that maximizes observability and controllability while minimizing voltage deviations at load buses to ensure network robustness. A large number of loading and electrical generation scenarios are generated using Monte Carlo simulation to train the NN with voltage information as inputs and HC as output. This trained NN can then predict the hosting capacity for distribution networks within seconds with new inputs, offering a highly efficient and practical solution for distribution utilities. The effectiveness of the proposed approach is demonstrated on the IEEE 123 bus distribution network and a rural feeder in New South Wales, Australia. Results show that the model predicts hosting capacity in 0.04 s for the IEEE 123 network and 0.18 s for the real network, with an average error of less than 1%, showcasing its accuracy and reliability. This rapid and precise prediction capability enhances distribution network management and operational efficiency for utilities. • A feed-forward neural network is proposed for quick hosting capacity analysis. • Only voltage information from pilot buses is used to train the neural network. • The method is tested on the IEEE 123 bus system and a rural feeder in Australia. • The proposed method shows promising results and requires fewer inputs.