# Building Cyber-Resilient Smart Grids with Digital Twins and Data Spaces
## 2 Digital Twins and Data Spaces
### Data Spaces
Each simulated device performed a sequence of CRUD operations, with a total of 50 requests per operation type to ensure a comprehensive assessment. The script recorded the time taken for each operation, and these metrics were aggregated to calculate the average latency for each CRUD operation across varying levels of client concurrency. The significance of this experiment lies in its realistic simulation of IoT device behaviour, providing a robust model for understanding how the Orion Context Broker performs under different loads. This is particularly relevant for smart grid applications where the timely processing of data from numerous sources is essential for maintaining grid stability and responding to dynamic conditions. The results, shown in Figure 6, are useful for identifying performance bottlenecks and optimizing the Context Broker's configuration for real-world deployment. Figure 6: _Cont._

## 5 Related Work

The increasing focus on cybersecurity in smart grids, due to the criticality of the energy infrastructure, has led to diverse approaches in recent research. [PERSON] et al. [19] provide a comprehensive exploration of cyberattacks in smart grids, emphasizing the importance of multi-criteria decision making for cybersecurity options. They propose an analytical hierarchy process to evaluate cybersecurity measures, including those incorporating artificial intelligence, which is crucial for enhancing the resilience of smart grids against cyber threats. [PERSON]. [20] presents a survey on the application of deep learning for cybersecurity in smart grids. The authors review the latest advancements in deep learning techniques and their application in detecting and addressing cyber threats in smart grids, providing a framework for enhancing cyberattack detection. [PERSON] and [PERSON] [21] introduce the Virtualized Experiential Learning Platform (VELP) for smart grids and operational technology cybersecurity. They demonstrate how virtualization technologies, including digital twins and AI, can be utilized in hands-on training programs, which is essential for the development of a skilled workforce in the era of Industry 4.0. Moreover, [PERSON] et al. [22] highlight the role of digital twins in transforming energy systems and smart cities, noting their potential to improve cybersecurity, efficiency, sustainability, and reliability. [PERSON] et al. [23] propose a digital twin approach for smart meters, focusing on solving implementation challenges and enhancing smart grid network security. Their work evaluates the communication performance of a digital twin system in a smart grid application, demonstrating its effectiveness in real-time scenarios. [PERSON] et al. [24] explore a digital twin of the Energy Internet of Things (EIoT) and introduce a data-driven situation awareness paradigm to address the challenges of traditional model-based situation awareness in smart cities. In the broader context of digitalization, [PERSON] and [PERSON] [25] discuss the impact of digital twins on sustainable agriculture, which, while not directly related to smart grids, underscores the versatility and potential of digital twin technology in various sectors. [PERSON] et al. [26] present a smart water grid with a digital twin for water infrastructure management, demonstrating the utility of digital twins in monitoring and managing system efficiency. [PERSON] and [PERSON] [27] analyze the use of artificial intelligence algorithms in the energy sector, including their application in cybersecurity, smart grid management, and energy saving. Their work identifies open research challenges for the practical application of AI in critical energy domains. Lastly, [PERSON] et al. [28] develop an information security model for IoT-enabled smart grids, addressing the need for proactive security measures in the bi-directional data flow within smart grids.