Digital twin · Glossary

A digital twin is a digital representation of a real object or system. What makes it special: it is coupled to its real-world counterpart through data connections and is regularly aligned with its actual state. The international standard ISO/IEC 30173 of November 2023 describes the term across industries as follows: a digital twin is the digital representation of a target entity, whose data connections bring the physical and the digital state closer to each other at an appropriate synchronization rate. The standard counts connection, integration, analysis, simulation, visualization and optimization among the typical capabilities. A single twin does not have to possess all of them.

In the power system, a digital twin can represent very different things: an individual piece of equipment, a substation, a distribution grid, a transmission grid, a storage unit, a power plant, a building or an industrial facility. Accordingly, the standard distinguishes twins of components, assets, systems and processes. A digital twin is more than a graphical representation. It links data, models, measured values, status information and rules with one another.

The basis is a coherent model of the object under consideration. For a power grid, this includes, for example, lines, transformers, switching states, protection systems, feed-in units, consumers, measuring points and the connections between them. In addition, there are time-dependent data such as load profiles, feed-in profiles, temperatures, switching signals or sensor measurements. Depending on the purpose, the twin also contains calculation models, for example for load flow calculation, for short-circuit analyses, for the ageing of equipment or for the simulation of operating states.

This is where a digital twin differs from an ordinary database. A database stores master data, such as the year of construction, location, manufacturer or rated values of a transformer. A digital twin links such information with the current state and with a model. From this, it can be derived which states are possible or to be expected. A control system is also not automatically a digital twin: it displays and controls ongoing operation. A digital twin can use this data. As a rule, however, it is additionally meant to help estimate what a switching action, a maintenance decision or a changed feed-in pattern would cause.

Distinction from model, dashboard and simulation

The term is used for very different digital applications. A dashboard that displays measured values is not yet a digital twin. A 3D visualization of a facility is part of a digital twin only if it is linked with technical data, status information and evaluation functions. A simulation model can be part of a digital twin. Without a connection to current or regularly updated real-world data, however, it remains a theoretical model. According to ISO/IEC 30173, it is precisely this data connection to the real object that is the decisive feature.

The word "real time" is also often used imprecisely. The standard does not require real time, but a synchronization rate appropriate to the purpose. Not every digital twin therefore works on a second-by-second basis. For operational grid operation, current measured values and short update intervals may be necessary. For asset management, grid planning or maintenance strategies, daily, monthly or annual data are often sufficient, provided they are reliable and suitable. A good twin is therefore not primarily characterized by fast data transmission. What matters is that the temporal resolution matches the decision it is meant to prepare.

In the power system, three types of representation can also be distinguished. A topological model describes which equipment is connected to which, and how. A physical model describes electrical and thermal quantities such as voltage, current, active and reactive power or heating. An economic model represents costs, revenues, maintenance effort or investment needs. A high-performing digital twin does not have to contain all three levels. However, it should disclose which levels it covers and which it does not.

Relevance in the power system

Digital twins are becoming more important because the power system is becoming more fine-grained and dynamic and more dependent on data. In the past, a large part of grid operation could be organized with a few central power plants, easily predictable loads and generous safety reserves. Today, many decentralized units feed in, such as photovoltaics, wind energy and battery storage. Flexible consumers are added to this. Heat pumps, charging points for electric vehicles and the electrification of industrial processes change load profiles. For distribution system operators, it is therefore becoming more important to know the actual state of their grids precisely. In its 2023 Grids Action Plan, the EU Commission also emphasizes that grids must adapt to a more decentralized, more digitalized and more flexible system.

A digital twin can help identify grid congestion more precisely and better assess countermeasures. An example: a grid operator knows which line becomes thermally overloaded in which feed-in situation, where voltage limits could be violated and which flexibilities are available locally. It can then plan grid reinforcement, switching actions or operational interventions in a more targeted way. Closely connected to this is flexibility: it can only be used for the benefit of the system if one understands how it acts in the specific grid.

The digital twin is also relevant for maintenance. Many pieces of equipment in the power grid are long-lived: transformers, cables, switchgear and overhead lines are used for decades. Their condition cannot be read from age alone. Load, ambient temperature, switching operations, moisture, maintenance history and faults influence ageing. A digital twin can bundle this information so that maintenance is oriented more strongly toward the actual condition. This can avoid failures, save unnecessary maintenance and help to prioritize investments sensibly.

Grid planning also benefits. Anyone who wants to connect new generation plants, charging infrastructure or heat pumps must know whether the existing grid is sufficient or must be expanded. A digital twin can run through scenarios for this: what happens if many households in a local grid charge their cars at the same time? How does a battery storage unit act at a particular grid node? How does the residual load in an area change if more photovoltaics are added? Such questions cannot be answered reliably with average values once local bottlenecks and the simultaneous occurrence of loads play a role.

European initiatives

At EU level, the digital twin of the power grid is a flagship political project. In the Action Plan on the digitalisation of the energy system (COM(2022) 552), the European Commission announced that it would support the European transmission and distribution system operators in building a sophisticated virtual model of the European electricity grid. In December 2022, ENTSO-E, the association of transmission system operators, and the EU DSO Entity, the representation of distribution system operators, signed a corresponding letter of intent.

According to the Commission, the Horizon Europe project TwinEU began in January 2024. It has a budget of EUR 25.2 million, of which EUR 20 million is EU funding. The aim is a pan-European concept based on a federation of local twins. This means not a single central copy of the grid, but an association of many independent models. This fits the growing requirements for TSO-DSO coordination.

Data quality, responsibilities and limits

A digital twin is only as reliable as its data and models. Incorrect grid plans, outdated switching states, incomplete master data or implausible measured values lead to wrong results. In the power grid, this can have serious consequences, because even small model errors can strongly distort load flow calculations or congestion analyses. It is therefore not enough to collect data. It must be checked, versioned, correctly assigned and transferred into a technically sound model structure.

Organizational questions follow from this. Grid operators, plant operators, metering point operators, manufacturers, service providers and platform providers each hold different data, not all of which may be used freely. Personal consumption data fall under data protection. Operational data of critical infrastructure must be protected against misuse and cyberattacks. The EU initiative also expressly describes the envisaged grid twin as cybersecure. Manufacturer data are, moreover, often accessible only via proprietary interfaces. A digital twin therefore needs clear rules on data access, data security, responsibility and liability. If a simulation leads to a wrong operational decision, it must be traceable which data were used and who was responsible for maintaining, checking and approving the model.

Regulation and cost recognition also play a role. A digital twin costs money: for sensors, data management, software, interfaces, model maintenance and specialist staff. Digital solutions are not automatically sensible. Their benefit must be demonstrated in concrete tasks, such as better grid planning, lower failure risks, faster connection procedures, more efficient maintenance or fewer interventions in generation and consumption. The EU Commission expects that digital solutions such as grid optimization at distribution grid level can reduce additional expenditure for expanding existing infrastructure. In regulated grids, it remains to be clarified which expenditure is recognized and what incentives grid operators have to actually integrate digital tools into their processes.

A common misunderstanding: that the digital twin is a complete copy of reality. No model captures all properties of a real system. It selects, abstracts and simplifies. A short-circuit calculation needs different details than a maintenance forecast. For voltage control in the low-voltage grid, different measuring points are important than for long-term investment planning. The quality of a digital twin is therefore shown not by as many details as possible, but by how well the question, the model and the available data fit together.

Linking operation and planning

In the power system, operation and planning were long more strongly separated. Planning worked with assumptions, safety margins and expansion scenarios. Operation reacted to current states, faults and switching requirements. With increasing decentralization, both areas are growing together: experience from operation provides better planning data, and planning models help operation to anticipate critical situations. A digital twin can support this exchange. To do so, it must not be regarded as a one-off software project, but must be maintained permanently as technical infrastructure. In this sense, ISO/IEC 30173 emphasizes that a twin can accompany the entire life cycle of its counterpart.

Conflicts arise where technical possibilities, market rules and political responsibilities do not fit together. A digital twin can show, for example, that a certain flexible load would ease a local bottleneck. However, it does not yet follow that the grid operator may use this flexibility, that a suitable market product exists, that the costs are regulated or that the plant operator has an incentive to take part. The digital representation makes courses of action visible. It does not replace rules on access, remuneration and responsibility.

For the energy transition, the digital twin should therefore be assessed soberly. It can help to utilize existing grids better, to plan expansion more precisely and to manage equipment according to its actual condition. It does not remove the physical limits of the grid, and it does not replace necessary grid expansion. It does, however, improve the information base for technical and economic decisions. A digital twin is valuable when it turns data into a verifiable statement about a specific physical system and when this statement can be used in a real decision-making process.

Sources

ISO/IEC 30173 Edition 1.0 2023-11 – Digital twin – Concepts and terminology (Leseprobe) — ISO/IEC JTC 1/SC 41 (Leseprobe über SEK Svensk Elstandard); 2023-11. Abschnitt 3.1.1 mit Anmerkungen 1 und 2
Digitalising the energy system – EU action plan, COM(2022) 552 final — Europäische Kommission (EUR-Lex); 2022-10-18. Abschnitt Promoting investments in digital electricity infrastructure
Grids, the missing link – An EU Action Plan for Grids, COM(2023) 757 final — Europäische Kommission (EUR-Lex); 2023-11-28. Einleitung
Commission welcomes cooperation between ENTSO-E and EU DSO Entity on the digital electricity grid twin — Europäische Kommission, GD Energie; 2022-12-20. Pressemitteilung
Key actions for digitalising energy — Europäische Kommission, GD Energie; unbekannt. Abschnitt Promoting investments in digital electricity infrastructure

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Edited by eCMS on 11.11.2026 19:04