What is a Digital Twin?
A digital twin is a virtual environment designed to be a representation of a physical construct. The advent of digital twins is the result of progress in the fields of Artificial Intelligence, Virtual Reality and Augmented Reality. Its emergence actually predates modern advancements in these areas as its history can be traced back to the 20th century publication “Mirror Worlds” by David Gelernter. At the turn of the early 21st century, Dr. Michael Grieves became the first to utilize the idea in the manufacturing industry. The term itself was popularized in 2010 by NASA’s John Vickers.
The digital twin market is projected to grow from $6.9 billion in 2022 to $73.5 billion by 2027, emphasizing its rising significance in the operations of the modern world.
How Does a Digital Twin Work?
Digital twins function by receiving real-time data from sensors built into the physical environment. Using deep learning and other AI techniques, the data can be analyzed to generate insights that determine the actions of the real-world environment. In this way, the digital twin and the real-world counterpart function as a closed-loop system. Compared to traditional simulations, digital twins can be used to simulate multiple processes from a variety of angles to produce richer quality insights.
The data used to build digital twins is usually representative of the real-world behaviors or states of the infrastructure and may include design specifications, real-time feedback, business data, historical analysis, maintenance records, etc.
In terms of deployment stage, digital twins can be grouped into three:
Digital Twin Prototype: This is built before the real-world infrastructure.
Digital Twin Instance: This is used to run simulations for already constructed infrastructure.
Digital Twin Aggregate: This utilizes DTI information to run performance analysis.
Types of Digital Twin
There are four broad types of digital twins namely:
Component Twin: This is a representation of the most basic functional unit of a physical system.
Asset Twin: This models the interaction between two or more components.
System Twin: This is a higher-level twin model that models the performance of a combination of assets.
Process Twin: Finally, a process twin shows how systems relate to create a picture of the whole infrastructure.
The Importance of Digital Twin Grids to the Energy Sector
Not all processes will benefit from having digital twin counterparts. However, power grids are sufficiently complex enough to require the utility of digital twins to mirror and optimize their performance.
Broadly speaking, digital twins are important because they allow for the attainment and maintenance of efficiency and ensure more in-depth research and development. This is done by predicting downtime, adapting to dynamic circumstances and testing design improvements before deploying.
The typical power grid has several moving parts, including asset management, operations, GIS, outage management, etc. These require a ton of data management which sometimes is riddled with inconsistencies that can cause sub-optimal system performance and system-wide blackouts. Digitization offers the opportunity to overcome these challenges by enhancing grid connectivity and improving data management.
Digital twin grids are important for the following reasons:
Demand forecasting - The data received from the physical power grid can be interpreted by the digital twin to more accurately predict consumer demand for electricity. This can help to reduce storage requirements and blackouts.
Fault modelling – According to Markets and Markets’ economic forecast, the economic growth of the digital twin grid will be led by the predictive maintenance application. Digital twin grids can be used to diagnose impending grid collapse which is vital for prompt intervention. This analysis can be used to implement a predictive maintenance process. Furthermore, external attacks such as cyberattacks can be detected and responded to.
Operations management – Importantly, digital twin grids can be used for the routine day to day operations of the physical grid. Hypothetical and historical simulations can be run to predict the behavior of the real-world counterpart in similar situations.
Business operations – Digital twin grids can be a vital bridge between the energy companies and the customers. They can be used to provide visual projections of the extent of value they deliver and how it will best be tailored to the needs of the consumers based on data of customer profiles, behaviors and preferences. The analysis can be custom for a geographical region or for an individual consumer.