Innovation in Energy Transition: Lessons from Digital Twin and Industry 4.0 Deployments

Abstract

Digital twin (DT) technologies, tightly coupled with Industry 4.0 methodologies, are emerging as fundamental enablers for innovation and optimization in renewable and low-carbon energy systems. Unlike traditional offline simulation, DTs support a live, bidirectional connection to physical assets and networks, enabling continuous monitoring, prediction, and control throughout the asset lifecycle. This paper reviews recent deployments of DTs in wind, solar, battery energy storage, smart grids, and microgrids, and discusses their implications for geothermal and other subsurface energy systems in the context of the global energy transition. A structured narrative review of recent DT and Industry 4.0 case studies was conducted, focusing on those that demonstrate (1) implementation in operational or near-operational settings; (2) contribution to energy forecasting, grid integration, asset management, or predictive maintenance; and (3) explicit integration of IoT, machine learning, and cloud or edge platforms. Approaches were categorized by methodological type, including real-time simulation and co-simulation, model-driven and semantic architectures, and AI-enabled analytics, and by thematic focus on wind farms, solar projects, battery storage, smart grids, microgrids, and regional multi-energy systems. Reported innovation outcomes in efficiency, reliability, flexibility, and decarbonization were synthesized to identify cross-cutting lessons and gaps. The review finds that DTs are already delivering measurable benefits in operational efficiency, reliability, resilience, and integration of intermittent renewables. Examples include cloud-based DTs for MW-scale photovoltaic (PV) plants with AI-driven health diagnostics and fault detection, real-time microgrid DTs for resilience assessment and cyber-physical security, and distribution-level DT operating systems that combine robust state estimation with renewable forecasting and bad data correction (Livera et al., 2022; Jamieson et al., 2022; Zhi et al., 2024). A climate-adaptive DT co-simulation framework using deep reinforcement learning demonstrates reductions in energy losses and improvements in post-fault recovery time under extreme weather conditions (Addo et al., 2025). Battery energy storage is increasingly being modeled within these DTs, and multi-energy DT platforms highlight the potential to extend similar concepts to geothermal and underground systems (Li et al., 2023). Nevertheless, storage-centric and geothermal-specific DTs remain underdeveloped in the open literature. Persistent challenges include the complexity and scalability of large-scale deployments, interoperability between legacy operational technologies and modern digital platforms, data quality and governance issues, and validation of AI-enabled control functions in safety-critical environments. Based on the synthesized findings, the paper proposes an integrated, multiscale DT framework for smart, decarbonized energy systems and outlines research priorities, including standardized semantic models, storage-centric and geothermal DT development, and safe, explainable AI-in-the-loop control.