Artificial Intelligence Techniques for Predicting Transmission Network Congestion: A Comprehensive Review

Abstract

 One of the most significant factors that has contributed to the development of the electrical market is the deregulation of electricity transmission systems. This process is aimed at creating efficient markets and ensuring that the electricity supply is distributed fairly. However, open access can lead to system overload and can clog the network. It is essential to quickly determine the availability of a transmission network's transfer capability during a time-consuming activity in the system. This paper presents a variety of AI techniques that can help identify ATC, including deep learning, natural language processing, and machine vision. This study explores various methods and approaches that can be used to forecast the congestion in the transmission network. It also reviews the literature and examines the outcomes of different AI techniques when it comes to calculating ATC.