The transition towards net-zero emissions is driving a profound transformation in energy systems, particularly through the shift from centralized to distributed generation [1]. This shift introduces both the opportunities and challenges of integrating renewable energy sources (RES) and distributed energy resources (DER) into existing grids. Digital Twin (DT) technology, which creates dynamic, virtual replicas of physical systems, presents a powerful solution to address these complexities by offering real-time insights, predictive maintenance, and optimization strategies tailored to the needs of smart energy systems [2]. This review analyzes the current applications of DT technology in the energy sector, with a particular focus on its role in supporting RES and DER integration, essential for enhancing energy system efficiency and stability in variable energy environments. We examine the kinds of DTs in energy systems, from passive models that simulate pre-set scenarios to more active, adaptive systems that support real-time decision-making and control. This review highlights the progress in combining physics-based models with data-driven AI approaches to develop DTs that can predict energy production, manage demand fluctuations, and coordinate distributed sources effectively. The integration of AI allows energy systems to respond dynamically to changing inputs from diverse energy resources, such as solar and wind. In addition to examining key advancements, the presentation discusses current limitations, including data availability, and the need for standardization in DT implementations.