Digital Twins
Overview
The pan-European Transmission system is a Cyber-Physical System (CPS) with standards already framing their development: the International Electrotechnical Commission (IEC) 60870-5-101 [1] and the IEC 60870-5-104 [2]. Today, the IEC standard 61850 is the successor of such developments [3]:
The Digital Twin (DT) is not a new paradigm, but to date there are many different definition and models of it [4]. The IEC and The International Organization for Standardization (ISO)/SC 41 committee have released the ISO/IEC 30173 “Digital Twin - Concepts and Terminology” [5] that defines the DT as “the digital representation of a target entity with data connections that enable convergence between the physical and digital states at an appropriate rate of synchronization”. VDE released an open access study entitled “The Digital Twin in the Network and Electricity Industry” [6].
A DT of the pan European Distribution and Transmission system may follow the functional description summarised in the diagram below. It can be mapped to the 5C architecture such as “Standardized data protocols” at the Smart Connection Level (I), “AI-based decision support systems” at Cognition Level (IV) and “Back up procedures for automated systems” on the Configuration Level (V). However, it is the Cyber Level (III) that contains the concept of the DT and unlocks the full potential of the Cognition (IV) and Configuration Levels (V).
The Digital Twin, or virtual and dynamic representation of a real asset, originates in the design and construction process carried out using the Building Information Modelling (BIM) methodology. Digital Twin may support a variety of use cases, however, full deployment of a digital twin with complete operational integration is not the norm.
![Functions and architectures of a DT [7]](/.netlify/images?url=_astro%2Foverview.DV_iWUg4.png&w=2056&h=1043&dpl=6930c70a3e4b2400087511f8)
Ultimately, the potential benefits of Digital Twin (DT) technology depend on the specific use cases (UCs) and the level of deployment maturity; while DTs can support a wide range of applications, current implementations among DSOs are often limited to data acquisition and monitoring rather than full operational integration.
Benefits
A DT located within each DSO and TSO has the following benefits:
- Acquires and assimilates observational data from the asset (e.g. data from sensors or manual inspections) (Levels I and II)
- Uses this information to continually update its internal models so that they reflect the evolving physical system with its own computing capability. This is a synergistic multi-way coupling between the physical system, the data collection, the computational models, and the decision-making systems (Levels II and IV)
- It then runs these up-to-date internal models for analysis, prediction, optimisation, and control of the physical system due to an appropriate computing capacity. (Levels V)
- Improve the efficiency of the transmission network reducing the dispatching costs
- 4-6 weeks reduction in delivery time for brownfield and greenfield substation projects
- Compliance report of each HV substation with respect to lightning protection and clearance issues
- Enhanced asset management through validated and CIM compliant asset data by a digital inventory
- Better decision-making through comprehensive data integration and visualization
- Reduced safety risks by minimizing physical site visits for routine inspections
- Modernization of work environment through state-of-the-art VR technology
- Supports EU policy targets for grid resilience, efficiency, and decarbonization
By ensuring that digital twins can communicate and share data effectively, organisations can enhance decision-making, optimise operations, and foster an environment for innovation.
The development of a pan-European DT is motivated by Digitalising the energy sector - European Union action plan. It will help develop a competitive market for digital energy services and digital energy infrastructure that are cyber-secure, efficient and sustainable. It will ensure the interoperability of energy data, platforms and services. A framework for the development of a federated DT at European level is being pursed in the TwinEU European project [8].
Challenges
Challenges to reach the scope when implementing DTs within DSOs and TSOs:
- Exchanging data and leveraging information coherently and efficiently, regardless of the platform individual systems are built on
- Ability of DTs to operate within larger ecosystems, connecting with the “internet of things (IoT)” devices, enterprise systems and other digital twins to create a comprehensive and interconnected digital representation of physical assets, processes and systems
- Interoperability within digital twins and TSOs/DSOs to underpin the ability to achieve a holistic understanding of the operational ecosystem
- Poor documentation of aging grid infrastructure delays brownfield projects
- Poor asset data quality creates mistrust and leads to inefficiencies across the operator and supply change by additional validation steps
- Knowledge transfer gaps increase as experienced personnel retire
- Siloed data systems across substation planning, operation and maintenance slow down cross-team collaboration
Current Enablers
To aid DSOs and TSOs in their DT development, several enablers exist:
- Increasing standardization through IEC 61850 [9] for substation automation
- Development of Common Information Model (CIM) standards facilitating system interoperability (IEC 61970 [10], IEC 61968-1 [11] and IEC 63325-301 [12]).
- Regulatory push toward grid modernization and digitalization across Europe
- Market trends toward condition-based and predictive maintenance
Furthermore, the DT is often thought of as a system-of-systems, a set of several enabling technologies that construct a virtual representation of a physical entity and support a continuous bidirectional communication loop between the twins.
The most common enabling technologies for DT development are listed below:
- Broadband connections
- Internet of Things
- Cloud Computing
- Edge Computing
- Artificial Intelligence
- Real Time Digital Simulation
- High Performance Computing
- Big Data
- Data Spaces
- Hardware-In-The-Loop
Ultimately, the key enabler for developing Digital Twins is the system operator itself, who must define a clear business case for their implementation. Once this foundation is established, the realization of the Digital Twin is typically supported by technology vendors—providing the necessary components, IT solutions, and expertise in the generic technologies that enable the creation of interconnected and integrated systems.
Applications
DSO
| Location: Germany | Year: 2024 |
|---|---|
| Description: The use case is implemented together with Westnetz in Arnsberg as part of the German demonstrator of the TwinEU project. The Intelligent Grid Platform (IGP) was commissioned in Q1 2024 with operational start planned for Q4 2026. In response to rapidly rising feed-in and load numbers in distribution grids, Westnetz and envelio are developing an end-to-end congestion forecasting solution under §§ 14a and 14c EnWG (where § 14a enables DSOs to temporarily throttle high-power, controllable loads to prevent grid overloads in exchange for reduced fees, and § 14c mandates transparent publication of grid data and tariffs). The main objective is proactive grid stabilization in view of the rising feed-in and load volatility. The solution being developed by Westnetz together with envelio enables next-day grid congestion forecasting via the IGP's digital twin. Based on these forecasts, an envelope curve is defined to identify grid bottlenecks that are likely to occure the following day and derive suitable flexibility measures for market participants. Individual elements of this solution - with the focus on the measures related to §14a EnWG - are already in operational use at several envelio's customers. Further elements - such as envelope curve definition - are currently at the advanced stage of development. | |
| Design: The Intelligent Grid Platform (IGP) serves as the central data hub: it ingests real-time telemetry and topology data from the DSO, including measurement data from local substations. It also integrates external weather data and, where available, smart meter data. These inputs are used by the digital twin of the grid to produce next-day congestion forecasts, compute the power envelope curve, and deliver results via standardized interfaces to Flexible Service Providers (aggregators). German EnWG framework (§§ 14a & 14c) influenced the design of this implementation. As mentioned above, it provides the legal basis for controllable-load throttling and mandatory publication of grid data and tariffs, enabling both flexibility activation and market transparency. Furthermore, Network Code on Demand Response (NC DR), driven by ACER, is set to establish common rules and data models for flexibility services across member states, paving the way to scale the solution beyond Germany. Due to its state-of-the-art character, the project must first establish robust algorithms for envelope-curve projection. At the same time, standard interface specifications and data schemas need to be defined to ensure seamless integration between the IGP, DSOs, aggregators and downstream energy management systems. Parallel to these technical advances, a comprehensive end-to-end remuneration scheme is under development to align incentives for end customers. Finally, scaling the solution beyond Germany will involve adapting to other national grid codes and tariff regimes, and leveraging the forthcoming Network Code on Demand Response (NC DR) under ACER to set uniform rules and data models across member states. | |
| Result: The solution is set to deliver on its core objective of leveraging congestion forecasting and management via digital twins to contribute to end-to-end flexibility and grid stability. In the upcoming E.ON-Lab pilot, currently in preparation, Westnetz will supply live grid data to envelio's IGP, which will generate the next-day congestion forecast for Arnsberg. That forecast will be then handed off to the aggregator's HEMS, which - with a one-off activation - will temporarily throttle an EV charging session. | |
| Technology Readiness Level (TRL): TRL 5 | |
| References: | |
| Location: Germany | Year: 2023 |
|---|---|
| Description: The use case was implemented at Überlandwerk Mittelbaden GmbH & Co. KG, a regional grid operator based in Lahr, Baden-Württemberg (Germany). The Intelligent Grid Platform (IGP) was commissioned in Q3 2023 and went into fully operational use in Q2 2024. The project aimed to accelerate and automate the processing of grid connection requests, particularly for small-scale PV systems, in response to rising application volumes and new regulatory timelines set out in Germany's Solar Package I. The solution focused on replacing formerly manual steps with standardized, automated processes by deploying two of the IGP's modules: Grid Transparency application to improve visibility into available grid capacity, and Connection Request to enable automated technical evaluations of requests. | |
| Design: The solution was based on the modular structure of the Intelligent Grid Platform. Grid data from grid operator's GIS and EDM was consolidated using the IGP's GridHub - data integration and orchestration layer of the platform - to build a computable digital grid model covering both low and medium voltage levels. This served as the foundation for technical evaluations within the Connection Request application. A key technical component of the project was the integration of the epilot customer portal. To minimize data traffic and streamline communication between systems, a push-based status update mechanism was introduced. Traditionally, such updates would rely on repeated polling via a GET API, which can lead to high traffic and delays. In this project, a standardized endpoint was developed and implemented instead, allowing status changes — such as from “calculated” to “reserved” — to be communicated in real time to the customer portal. This ensured transparency for customers and helped grid planners quickly identify whether further steps were needed for a request. Germany's Solar Package I significantly influenced the design of this implementation. New legal obligations — including shorter response timelines and the requirement to offer a digital connection portal from January 2025 — made automation and digital integration a necessity for DSOs. | |
| Result: Within eight months of going live, Überlandwerk Mittelbaden processed approximately 4,000 grid connection requests, of which around 3,500 were for PV systems. The average processing time per request was reduced from up to 3 hours to approximately 15 minutes. This time saving significantly reduced the workload of grid planners, who had previously spent substantial time on manual data handling and evaluations. The efficiency gain also helped unlock internal resources for strategic tasks, including structural grid expansion planning. Further operational benefits included:
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| Technology Readiness Level (TRL): TRL 9 | |
| References: | |
| Location: Norway | Year: 2024 |
|---|---|
| Description: Built and deployed a hybrid grid knowledge graph of Lede's grid. Lede is one of Norways biggest DSOs with 220+ customers ) Construction, validation of hybrid graph integration with data lake deployment during second half 2024. Testing, improvements, utilization and semantic extensions first half 2025. Developed queries as reusable dynamic data products delivered from a managed digital twin. Training and familiarization of Lede analytics and data pipeline teams. Preparations for further operational utilization. | |
| Design: Built as an integrated part of Lede's CIM based grid model. Running in Lede Azure environment. Azure ADX backend for measurement data. | |
| Result: Constructs a CIM based RDF hybrid knowledge graph (100+ million triples) including measurements in less than 2 minutes. 100+ Million triples graph model that provides prompt query response (data products) for Lede's data analytics team. Static model queries responds within sub-second timeframe. Data queries are generally bounded by network throughput and dependent on result size. Navigational helpers and extensions originating from application needs analytics needs added to the graph model (ontology). | |
| Technology Readiness Level (TRL): TRL 7-8 | |
| References: | |
| Location: Italy | Year: 2024 |
|---|---|
| Description: Benetutti is the first Sardinian “Smart Community”, operating an MV/LV distribution network that supplies 2 000 residents, 3.7 GWh yr⁻¹ of electricity demand, 102 PV plants (1.5 MW), emerging battery systems, and smart meters based on the Meters & More protocol. To move from reactive to predictive grid operation, the municipal DSO partnered with STAM to deploy EnPOWER - a cloud-native, modular digital-twin platform (TRL 7) that mirrors the physical grid, its renewable assets and each Point of Delivery (POD) in near real time. Objective
How the objective is reached
Collectively, these elements transform the Benetutti grid into a living model that delivers actionable insights, unlocks dormant flexibility, and positions the DSO to integrate future community batteries, EV chargers and demand-response services with minimal additional investment. | |
| Design: EnPOWER follows a five-layer, cloud-native stack:
Country-specific factors Italy's ARERA rules require smart-meter interoperability and strict data-protection; rural Sardinian feeders with 40 % PV penetration need high-resolution voltage insight; national incentives for energy communities reward peak-shaving and self-consumption, functions natively supported by the twin. Implementation challenges & mitigation
| |
| Result: Since going live (2024) the Benetutti pilot has delivered the following measured or directly logged gains:
These outcomes confirm that the digital-twin approach gives the rural DSO faster situational awareness, tangible peak reduction and easier regulatory reporting, while remaining fully scalable for the forthcoming battery and EV-charger roll-out. | |
| Technology Readiness Level (TRL): TRL 7 | |
| References: | |
TSO
| Location: Germany | Year: 2023-2024 |
|---|---|
| Description: 2x Amprion 380kV substations In a proof of concept, a Digital Twin of two existing substations is developed, focusing on the integration and validation of diverse data sources such as Common Information Model (CIM) data, laser scans, and on-site nameplate pictures. The objective is to create a Digital Twin in Power Systems (DTiPS), enabling a wide range of applications. | |
| Design: Three steps design process:
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Result:
The Digital Twin can serve as a starting point for future construction projects, which can be implemented directly within the digital BIM model. Versioning allows different states to be saved, loaded, and edited. On average, 4-6 weeks of project time will be saved through a validated Building Information Model (BIM) model. 3D visualization, especially for Augmented Reality (AR)/Virtual Reality (VR) applications, is enabled. This allows access to the substation without the need for personnel to be physically present—via a VR headset—or enhances reality by attaching additional information to real-world objects using AR glasses. This option can cut on-site inspections significantly. Data from different sources, such as asset management systems and the scanned Digital Twin, can be compared. This enables the identification and separation of objects that can or cannot be automatically matched. | |
| Technology Readiness Level (TRL):
TRL 7 - System prototype demonstration in operational environment
| |
| References: | |
| Location: The Netherlands | Year: 2024-2026 |
|---|---|
| Description: A quarter of the Dutch grid owned by TenneT Netherlands TU Delft owns a DT which is already operational to simulate a quarter of the Dutch grid. In the near future, this version is set to be replaced by a digital copy of the whole network operated by Tennet. | |
| Design: Four steps design process:
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Result:
A quarter of the Dutch electricity grid has already been modelled and tested using the digital twin. The DT allows to simulate short circuits, overloads, and cyberattacks without real-world consequences. Physical components in the ESP Lab (such as transformers and cables) react to virtual faults, enabling realistic testing. The DT allows for studying how hydrogen may impact the energy system. The DT excels at handling multi-variable “what-if” scenarios, such as: large-scale adoption of HVDC technology, introduction of flex-aggregators and IoT-coordinated electric vehicle charging, simultaneous changes in supply and demand dynamics. Based on the results of scenarios testing grid operators such as TenneT can use insights from the DT to plan future developments. “Cousins” of the digital twin, which are simplified versions, are used for education and exploratory research. These allow for safe experimentation without compromising sensitive operational data. The northern Netherlands demo shows that a full national twin is feasible. | |
| Technology Readiness Level (TRL):
TRL 7 - System prototype demonstration in operational environment
| |
| References: | |
| Location: Italy | Year: 2024-2026 |
|---|---|
| Description: Defence System Use Cases Construction, validation of hybrid graph integration with data lake deployment during second half 2024. In a proof of concept related to the TwinEU Project, a Digital Twin (DT) of selected portions of the Sardinian high-voltage grid will be developed to simulate and assess tailored defence system logics, aimed at managing power system stability and security, and preventing major events and blackouts. Additionally, the DT of Distributed Energy Resources (DERs) will be implemented to evaluate the impact on the Transmission of DER intervention (e.g. virtual islanding). | |
| Design: The simulation will be carried out using Hardware-in-the-Loop (HIL) techniques. A Real-Time Simulator (RTS) will be interfaced with dedicated physical devices to rigorously test protection and control logics, as well as operational strategies, in a protected environment and under real-like conditions. | |
| Result: Expected Results are mainly related to the following points:
Joining the simulation results obtained from the TSO and DSO models, the main goal will be to evaluate the impact of the defence system on the network. The DT excels at handling multi-variable “what-if” scenarios, capable to allow detailed sensitivity analysis of different scenarios, to evaluate the impact of new strategies. | |
| Technology Readiness Level (TRL):
TRL 5 - Technology validated in relevant environment
| |
| References: | |
| Location: | Year: |
|---|---|
| Description: The use of the Building Information Modeling (BIM) methodology in the design and construction processes in order to enable the Digital Twin of the electric national grid. | |
| Design: The application of the Building Information Modeling (BIM) methodology begins with the design and construction assignments in compliance with Italian legislative requirements. In compliance with the sector regulations, the “Capitolato Informativo” (Exchange Information Requirements - EIR
Among the documents included in the Exchange Information Requirements there is the PIR (Project Information Requirements), which contains the information needed to implement the project asset model. The content of the requested information models is intended, among other things, to enable the digitalization of TSO's asset management with the objective of ensuring the connection and consistency between the geometric/information content of the Information Models and the TSO management database. The TSO therefore defined the relevant level of detail (LOD according to the UNI 11337 standard) by specifying design and construction requirements in terms of information (LOI according to the UNI 11337 standard) and geometry (LOG according to the UNI 11337 standard) of the model to be produced (Required Data Model). This was based on current needs (concept of Level of Information Need or LOIN introduced by ISO 19650-1). The information models that are required to be produced follow the international standard IFC (ISO 16739) which is a standardized digital description of the built assets sector. | |
| Result: BIM Information Models, in IFC format, compliant with TSO specifications. | |
| Technology Readiness Level (TRL): TRL 5 | |
| References: D.lgs. n. 36/2023, Codice dei contratti pubblici, - Italian law UNI11337 Gestione digitale dei processi informativi delle costruzioni - Technical standards developed by UNI - Italian Standards Organization UNI EN ISO 19650-1:2019 Organizzazione e digitalizzazione delle informazioni relative all'edilizia e alle opere di ingegneria civile, incluso il Building Information Modelling (BIM) - Gestione informativa mediante il Building Information Modelling - Parte 1: Concetti e principi UNI EN ISO 16739-1:2024 Industry Foundation Classes (IFC) per la condivisione dei dati nell'industria delle costruzioni e del facility management - Parte 1: Schema di dati | |
R&D Needs
When considering the interconnected transmission and/or distribution networks, there are prerequisites to be met to ultimately develop Pan-European DT:
- Investment costs: TSO and DSOs are sometimes not incentivised to implement the DT because of its envisioned development costs and difficult-to-quantify Return on Investment. In fact, it is challenging to estimate the real cost of the DT because of its multi-disciplinary nature at the smart connection level (I): The IEC 61850 must support the DT concept by incorporating standardised metadata within an object-oriented structure.
- At the data-to-information Level (II): Data are the basis for the creation and operation of DTs. There are different maturity levels of data processing, starting with basic analytics such as Excel but also more sophisticated levels such as the data enrichment with big data infrastructure. DTs need a specific maturity level on data.
- Interoperability and vendor Independency: each TSO and DSO operates a legacy system. Interoperability is the foundation required to develop effective and operationally federated DTs for each system operator. This feature acts as a glue that allows diverse systems, devices and applications to communicate and work seamlessly.
Organisational and process challenges of DT development:
- Demanding expectations at the target level (high risk of disappointment). A common misunderstanding about the DT is that the digital copy should reflect the physical twin in its entirety, as well as that it should collect and elaborate all of its data in real-time. However, these performances are not currently feasible in some cases, and certainly not always necessary.
- Standardisation represents another challenge that can decelerate the development of DT. The standardisation of platforms and interfaces necessary to exploit the full potential of DT is achieved through cross company collaboration and the networking of the DTs. A standard form shall be used to build, save and execute the model of the DT.
- Ambiguity in responsibility of DT within a system operator
- Data ownership and governance
- Data security
The technology is in line with milestones “Advanced reconfiguration and control of network and assets” and “Digital twin for optimisation of assets maintenance and replacement in operation” under Mission 1, milestones “Digital twin for monitoring and enhanced dynamic grid representation” and “Digital twin for grid control” under Mission 4 and milestone “Digital Twin application for enhanced grid flexibility” under Mission 5 of the ENTSO-E RDI Roadmap 2024-2034.
Technology Readiness Level (TRL)
TSO TRL 7 for digital twin
DSO TRL 5-7 for digital twin
References
M. Sjarov et al., “The Digital twin concept in industry - A review and systematization,” in 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), (Vienna, Austria), 2020, pp. 1789-1796, doi: 10.1109/ETFA46521.2020.9212089.
ISO. “ISO/IEC 30173:2023 Digital twin — Concepts and terminology.” iso.org [online]
S. Mihai et al., "Digital Twins: A Survey on Enabling Technologies, Challenges, Trends and Future Prospects," IEEE Communications Surveys & Tutorials, vol. 24, no. 4, pp. 2255-2291, Fourthquarter 2022, doi: 10.1109/COMST.2022.3208773.