## 4 Technological Challenges and Integration Issues
### Real-Time Modeling, Simulation, and Data Integration at Scale
One of the defining features of a digital twin is its ability to perform parallel what-if analyses, such as assessing the effects of line outages, load spikes, or market disruptions, while simultaneously maintaining real-time monitoring of the actual system [20]. These predictive capabilities require high-performance computing, often supported by hybrid cloud and edge computing architectures. In such setups, local processing manages time-sensitive tasks, while cloud infrastructure handles more computationally intensive forecasting [5]. The European Commission's Digitalization of Energy Action Plan explicitly acknowledges this need and promotes the use of cloud and edge computing to enable the real-time operation of digital twins across the energy system [1]. Flexibility and adaptability of the simulation platform are also essential. As the grid evolves, with increasing integration of inverter-based resources, hydrogen infrastructure, and demand-side technologies, the software supporting the digital twin must evolve accordingly [20]. Traditional monolithic simulation tools are not well suited to this task. Instead, modular and containerized microservices architectures, where components can be updated or replaced independently, are emerging as the preferred design approach [5]. This enables model developers to upgrade specific parts of the digital twin incrementally without disrupting overall functionality, and to add support for new technologies as they are introduced [24]. Accuracy and trustworthiness are foundational to operational adoption. Operators must be able to rely on the twin's outputs for situation awareness and decision-making. This requires rigorous verification and validation (V&V) processes, continuous calibration against real-world measurements, and mechanisms for anomaly detection [20]. Early implementations of digital twins will likely operate in parallel with human operators, building confidence through repeated validation over time [5]. Emerging technologies such as artificial intelligence and machine learning offer promising opportunities to accelerate simulations and improve model responsiveness. For example, surrogate models trained on physical simulations can approximate power flow solutions much faster than conventional solvers. However, the use of these techniques raises concerns related to transparency, interpretability, and physical plausibility, which require careful integration into trusted simulation workflows. In summary, enabling real-time, high-fidelity modeling and simulation of the European electricity grid at scale is an immense undertaking. It requires not only advanced computational and data infrastructure but also carefully designed model architecture, real-time data handling, multi-domain integration, and strong organizational coordination. Projects such as TwinEU and broader European initiatives like Destination Earth will play a central role in developing and validating the necessary technologies, architectures, and processes. As the European Commission has noted, the grid digital twin will not be built in a single step. Instead, it will emerge through a progressive and iterative process, beginning with pilot projects and gradually expanding to system-wide implementation. This will involve continuous refinement of models and platforms and steady advancement in the ability to replicate the energy system in real time.