Digital Twin (DT)

The new Digital Twin (DT) approach in production and Asset Management claims to optimize the value chains in various industry sectors, such as in automotive manufacturing and energy products. It can be understood as a digital representation of a physical asset e.g. a substation. This digital picture of installed power equipment may be used as to operate complex grid infrastructure more accurate. There are quite a few products and services available, but its trade-off for implementation in TSO’s core business needs to be proofed.


Technology Types

There are three main purposes to implement a DT:

  • A Product Digital Twin – to guarantee reliable design in product development and improvements;
  • A Production Digital Twin – to improve production planning and manufacturing;
  • A Performance Digital Twin – to capture, analyse and act on data while an asset is on operation.

From a TSO’s perspective, the Performance DT is most relevant. Here, an Asset Owner gathers necessary data to feed modelling and simulation software solutions to support decision making at several operational levels, e.g. System Operation or maintenance activities.

Another argument to implement such a DT for several purposes is to have one sophisticated way to stack and use data from various sources – having therefore one “source of truth” available.


Components & enablers

The DT infrastructure consists essentially out of 3 parts:

  • The Engine is responsible for operation of the central multi-user database as well as several data management functions.
  • The installed sensors and corresponding interfaces are the components obtain data and transfer within another.
  • The implemented User Interface is providing sufficient functionality such as graphical data visualization.

Advantages & field of application

The DT approach promises to improve utilization of data gathered from e.g. market participants and energy traders, generation units and power plants as well as TSO’s grid infrastructure. It can be discussed as a useful tool to affect data-driven performances in future power grids; appropriately in Power System Operation and its grid planning or in Power System Economics and its local flexibility markets.


Technology Readiness Level

TRL Score 6 – technology demonstration


Research & Development

The main developments were promoted not academically but in industry sectors. Therefore, leading companies as Siemens AG and General Electric Corp. offer different products and software solution to Asset Owners and other stakeholders.


Best practice performance

Together with Siemens AG, Fingrid introduced an electrical DT model (ELVIS) to affect their asset and operation management as well as infrastructure investment planning. Meanwhile, the DT model is used to develop several investment scenarios considering different policy frameworks. In conclusion, the data collection and verification process takes less than 20 % of the time that it used to take.

The American Electric Power (AEP) established a similar project to obtain a reduction of time and costs associated with grid and market modelling efforts manually. Furthermore, an advanced data governance foundation to support its investment strategy were implemented.


Best practice application


References

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