AIOTI WG Energy has published the paper Edge driven Digital Twins in distributed energy systems: Role and opportunities for hybrid data driven solutions.
The full document can be found here.
The paper explores the intersection of Digital Twins and Edge Computing within the context smart grids in the electricity sector in the EU. The goal is to specifically focus on medium and low voltage networks environments. The use cases’ primary objectives are to enhance visibility for more advanced grid monitoring and management, with a particular emphasis on integrating the grid edge through digital twins. This integration allows for real-time monitoring, enabling advanced grid management methods and creating opportunities for active consumer participation while increasing grids´ capacity.
Digital Twin technology relevance for electricity sector topics is well defined, although use cases are still evolving. This paper focuses on the edge computing driven use cases: including real time production monitoring, production control; performance prediction at different time scales; human robot interaction for assets monitoring; drones-based monitoring, optimisation of asset management and production planning. In services related to the renewable production power plants, applications of Digital Twin include predictive maintenance, fault detection and various diagnostics, state monitoring, equipment performance prediction, and virtual testing.
The ongoing evolution of the Internet of Things (IoT) has led to data generation at the network’s edge, particularly in low voltage distribution networks where smart metering requirements and data processing can be efficiently managed on site through edge computing. The paper provides cases where edge computing is a viable solution for handling heterogeneous data with performance requirements such as low latency, crucial for the increasing controllability demands in low-voltage distribution networks.
The integration of Digital Twins at the grid edge is positioned as a key enabler for developing a more efficient energy ecosystem with evolving business models. The devices at the edges of electricity grids serve as physical infrastructure for sector integration, connecting with transport sector (electromobility) and heating & cooling along with dynamic buildings components. Digital Twins driven by edge computing are also expected to provide essential data for infrastructure planning across sectors, fostering the development of cross-sectoral business use cases. The need for data interoperability between different sectors is emphasised as demonstrated in a number of the ongoing research and innovation projects and within the evolving business models.
The paper outlines both challenges and opportunities associated with edge computing and Digital Twins in the distributed energy sector. Challenges include the technical maturity of digital twin technology, lack of plug and play deployment tools, and a shortage of digital skills in the energy sector. Regulatory issues, data privacy, and security concerns are also identified, which requiring innovative policies and regulatory experimentation. On the other hand, opportunities for future growth lie in scalability.
The opportunities for future growth in AI and Digital Twins within the distributed energy systems are detailed, emphasising scalability, federated Digital Twins models, linkage to the European Data Space, and the need for determining useful data for collection.
The integration of AI and digital twins presents multiple opportunities in areas such as advanced predictive maintenance for distributed renewable generation, grid integration and management, demand-response optimisation, microgrid development, renewable energy forecasting, energy storage optimisation, decentralised energy trading, energy efficiency improvement, and data driven decision making planning and operations. There are also policy and regulatory impacts expected as part of adaptations of these solutions at various geographical levels to increase energy hybridisation, for example for green hydrogen production and sector coupling at municipal and regional level.
The discussion on AI and edge driven Digital Twins emphasises the importance of data quality and quantity for accurate representations, robust and reliable predictive capabilities, adaptive learning, and improved decision making. The paper suggests that organisations should invest in data collection, storage, and management to support effective implementation, impacting various applications such as energy optimisation, predictive maintenance, and processes optimisation.
Case studies exemplify the practical applications of Digital Twins and AI in energy production, including energy optimisation of microgrids, AI powered wind farm optimisation, AI models enhanced building management, demand-response platforms, AI-enhanced grid management, AI-driven energy forecasting, and AI-based grid automation. These case studies demonstrate the versatility and potential impact of integrating AI and digital twins in various aspects of the energy sector.
We invite interested communities ‘ representatives to reach out to us, join the discussion on these topics and share the related use case. This is a dynamic document that can accommodate in the future additional use cases and outcomes of the related research projects.