Abstract
The purpose of a digital twin (DT) is to gain insight into and predict the performance of a physical product, process, or piece of infrastructure. Numerous advantages accrue from the energy industry's adoption of DT technology, such as improved asset performance, higher profits and efficiencies, and less harmful effects on the environment. This paper's goal is to present a literature evaluation that classifies DT principles, usage patterns, and benefits in the energy sector. A thorough literature review covering the past decade of studies on DT in the energy sector was conducted. The originality of this study is in-depth examination of DT's use across the whole energy value chain from power generation and storage to energy usage in buildings, transportation, and industrial applications. From this analysis, it was clear that there is a growing interest in using DT in the energy industry and minimizing energy use is the primary focus of the literature on digital twins. Growth of DT technologies will be aided by recent developments in machine learning and artificial intelligence, as well as the development of more sophisticated control systems, allowing for the enhancement of energy system efficiency and effectiveness, thereby fostering the clean energy transition, and reshaping the future of energy.