Advancing Power Systems: Harnessing the Potential of Artificial Intelligence and Disturbed Ledger Technology

Abstract

The 21st century has witnessed a major transformation within the energy systems, which was facilitated by the exponential growth of renewable and decentralized energy resources. This transformation was the driving factor towards the deregulation and the subsequent green shift within the energy industry. Meanwhile, various new technological developments such as AI, ML, DLT, and IoT were getting integrated into the energy sector as a means to mitigate the challenges posed by the volatile renewable energy sources. Thus, the resulting transformation can be denoted as digital green shift, where two parallel transformations occur simultaneously. The digitalization of the energy industry, which is highly reliant on various enabling technologies such as AI, ML, and DLT aims to provide better fault detection, predictive maintenance, and forecasting in order to mitigate the volatility of renewable resources and provide increased grid stability and security. However, this digital shift also introduces various cybersecurity risks. Thus, necessitating the modeling of modern power systems as Cyber-Physical-Social Systems where the transformation of energy industry can be incorporated via the paradigm of 5Ds of energy (deregulation, decentralization, decarbonization, digitalization, democratization). This PhD thesis focuses on the investigation of the digitalization aspect of the 5Ds of energy, with a particular focus on the utilization of enablers of deep digitalization, such as DLT, AI, and ML from the point of energy informatics both in TSO and DSO levels. The proposed research questions focus on the unexplored applications of such technologies within the energy industry, their applications, techno-economical and environmental aspects, and their challenges. The thesis is structured to provide a comprehensive understanding of the performed research. Firstly, the introduction regarding the evolution of the energy industry is given and the research questions are denoted. Later on, the methodology overview is introduced with the necessary background information on the utilized deep digitalization enablers. As the next step, the results of the performed scientific publications are summarized and detailed, both within the TSO and DSO domains. In the last part, a reflective conclusion discussing the implications of the performed research, and the answers to the research questions are presented. Additionally, a list of publications that are associated with the performed PhD work is provided, detailing the contributions to the field.