Feb 24 2023
Data Analytics

What Is a Digital Twin, and How Can It Benefit Energy and Utility Companies?

The technology can use real-time data to create realistic digital settings for utilities.

Digital twins, while not a totally new concept, are quickly becoming an essential way to navigate complex changes in any environment.

Digital twins are found in many industries, though the tech is proving most useful in the energy and utility sector. This is because energy and utility companies deploy vast networks of infrastructure at scale to build solid infrastructure systems. But before you can use a digital twin at your organization, there are some key distinctions you should know.

What is a Digital Twin and How Does It Work?

Digital twins are a realistic re-creation of an environment in a virtual setting. Built with exacting detail from real-world data, these digital replicas allow teams to simulate, monitor, test or integrate physical devices in a digital environment by using modeling tools, business intelligence or computer-aided design software.

The academic Michael Grieves first developed the idea of a digital twin in the early 2000s while conducting research at the University of Michigan.

Grieves defined a digital twin as an extension of product lifecycle management. “At the time this concept was introduced, digital representations of physical products were relatively immature. In addition, the information being collected about the physical product as it was being produced was limited, manually collected, and mostly paper-based,” Grieves writes in a 2014 white paper.

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Since then, Grieves continues, “the information technology supporting both the development and maintenance of the virtual product and the design and manufacture of the physical product has exploded.”

But Grieves wasn’t the only one to call attention to digital twins. There were various cultural and visual art examples circulating earlier, including David Gelernter’s 1991 book Mirror Worlds, which discussed the possibility of re-creating physical objects in digital forms.

NASA also relied on early concepts of digital twins, particularly when engineers on the ground helped astronauts in orbit successfully navigate a return to Earth during the failed Apollo 13 mission. (However, Grieves has argued that Apollo 13 involved a physical simulation, not a digital twin.) 

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Where Are Digital Twins Used?

Digital Twins are deployed in a variety of ways. For example, NVIDIA uses digital twins to manage its data centers for high-performance computing. And according to a 2022 McKinsey report, digital twins can cut time to market in manufacturing settings by 50 percent.

Retailers are also using tracking technology to build digital twins. CIOs are reporting that this is helpful to understanding how consumers interact with retail spaces. This can also help predict trends before they become mainstream.

That said, the biggest use-cases of digital twins are in the energy and utility industry. Barbara Ryan, executive director of the World Geospatial Industry Council, a trade association that supports the use of geographic research data, says that digital twins allow utility companies to test new approaches before they go public.

“The use of digital twin technology enables innovation with little, if any, investment risk. Trials take place virtually and have no impact on reality,” Ryan says. “Energy and utility professionals get the opportunity to test the innovative solutions in the digital twin and, if proven successful, implement and introduce them into the real system.”

What Are Different Types of Digital Twins?

Digital twins come in an array of forms, but they vary based on the scale of the device and the size of the system being digitally re-created. System types include:

  • Component or part twins. These are smaller twins that are part of larger components, re-created to test functionality. This could be a single part tested in a modeling app and later 3D-printed as a prototype before it is configured into a final design.
  • Asset or product twins. These are complete objects re-created in a digital context that can be tested for long-term capabilities and potential points of failure.
  • System or unit twins. This type of digital twin shows the functionality of an entire system, similar to the way the parts of a factory work together.
  • Process twins. This is the broadcast system and covers the entire organizational process. Spatial digital twins, which rely on geographic data to manage an energy grid, are a good example of a process-type twin used in large energy and utility deployments.

For energy industry use cases, the digital twin will most likely take the form of a system unit or process twins. These two forms are most equipped to replicate across settings,  particularly when it comes to sourcing energy and delivering it to customers.

“Other than receiving real-time updates and monitoring capabilities in this environment, energy and utility professionals can test modifications, apply new methods and derive concrete measures for the real situation,” Ryan says.

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What Kind of Equipment Is Needed for a Digital Twin?

Digital twins require a mix of software and hardware. Information must be gathered in real time through Internet of Things sensors located at the edge that can be brought back into a digital environment. From here, data retrieved from those sensors is visualized using cloud-based platforms such as Amazon Web Services or Microsoft Azure. Businesses can also use NVIDIA for high-end processing and visualization technologies.

The IoT also has helped build a foundation for digital twins that has grown increasingly sophisticated over time. And if combined with synthetic data, businesses can go a step further, planning possible changes to infrastructure before they are implemented in real time.

Arvind Krishnan
A hybrid twin increases the accuracy of simulations by reducing the errors to near-zero.”

Arvind Krishnan Industry Analyst, Lifecycle Insights

Digital Twins vs. Simulation

You might be wondering if a digital twin is the same thing as a simulation. The answer is no, not really. Though similar, they have one important difference: Simulations are generally fully constructed environments built on existing data points, while digital twins are built on real-time data being gathered by sensors.

According to the British engineering nonprofit TWI, the addition of real-time data is critical to a research-driven simulation. A digital twin is aimed “to deliver improved operational efficiency, the automation of manual tasks, increased data analysis, training, and validation.”

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Virtual Twin vs. Digital Twin

Virtual twins are another facet to consider. A virtual twin is a kind of simulation that adds physics to the digital environment.

Arvind Krishnan, an industry analyst for the simulation-focused research firm Lifecycle Insights, notes in a 2021 article that though different, digital and virtual twins are complementary. This has led to an emerging concept called the “hybrid twin,” which can be even more powerful.

“A hybrid twin increases the accuracy of simulations by reducing the errors to near-zero,” Krishnan writes. “As a result, it enables you to study large systems or a system of systems. This would be completely impractical using physics-based simulation models alone.”

How Can Digital Twins Help Utilities?

Digital twins have grown in sophistication over the years, but there is still potential to improve processes within the energy and utility industry sector. If implemented correctly, digital twins could help utility companies deliver infrastructure data in real time and reduce data silos.

As a result, companies such as Oracle are starting to embrace digital twin solutions. Ryan says that if digital twins were adopted across the energy and utility industry, companies could “reduce and eliminate data silos and data flow disruptions that occur because data is stored in digital tools disconnected from each other.”    

Once these data silos are removed, relevant data can be shared with stakeholders faster. This dramatically improves operations and increases “efficiency in information flow,” Ryan says. It will also help organizations gain a better understanding of the energy grid.

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Digital twins also can reduce energy waste and help with renewable energy solutions. Ryan predicts that “spatial digital twins will be a big push for planners and policymakers,” particularly when it comes to cutting transmission costs and carbon footprints in the long run.

For consumers, digital twins offer more transparency into energy consumption and utility service issues. Customers can “access information and transact with their utility in real time,” Ryan says. With the rise of mobile apps and online portals, that data might prove a necessary element of customer service.

For organizations curious about digital twin technology, it’s worth seeking out the support of CDW specialists. With deep knowledge of the utility space, a CDW team can ensure your deployment is successful so that you can experience the full value of a digital twin.

Jackie Niam/Getty Images

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