In my first blog in this series on innovation in T&D utilities, I looked at why business need must be the underlying driver of innovation. In this post, I want to explore one of today’s hottest areas of innovation, not just in T&D but across the wider energy and resources landscape: the “digital twin.”
If you haven’t heard of digital twin before, you soon will. According to our latest Industry X.0 research, more than 46 percent of the utilities executives surveyed said they are using digital twins to improve operational efficiency. While precise definitions for digital twins may vary in different sectors, within the T&D context my preferred one is: “A digital representation of the way the various network elements and participants behave and interact, enabling an infinite range of “what-if?” scenarios to be tested out.” The result: more accurate forward visibility and better-informed decisions.
A good way to think about the digital twin concept is as an evolution from advanced asset analytics. Across the industry, there are well-established use cases where analytics emulates the behavior of network assets and identifies when they’re most likely to fail or need repairing. Digital twin brings this insight to the next level, by simulating all the different network components as well as everything going on between and around them.
Up to now, it’s mostly been used by T&D utilities for modelling and assessing long-term strategic scenarios and investment decisions, such as: “What would be the impact on our network if we invested in electric vehicle charging infrastructure in a particular region?” Or: “What would happen to the generation mix if consumer demand for energy from renewable sources doubled overnight?”
But digital twins can also be put to other uses. Recently I was chatting with a client from a European distribution network operator (DNO), and he came up with a quite different view of where digital twin’s greatest value lies. He thought its most useful role would be in managing operations in near-real time, receiving live telemetry information to understand the behavior of network assets and work out the ideal configuration to optimize them.
There are no right or wrong answers here. The choice of how you use digital twin may simply come down to where your greatest need might be at the time.
The key to its power lies in its ability to model impacts on the network from changes in a vast range of internal and external parameters and components—from rises in distributed generation to shifting consumer demand, and from variations in population growth rates to changes in regulatory policies.
As well as being the source of its power, the sheer number and diversity of inputs also create the biggest challenge around digital twin: ensuring it takes all the relevant factors into account. In an environment like a T&D network, where everything is interconnected and interdependent, how can you be sure you’ve taken account of every possible variable? And even when you’re certain of that, how can you be sure you’re modelling the appropriate response to every change? Such issues may mean it will be some time before many companies fully trust their digital twins as the basis for major investment or near-real-time operational decisions.
But that time will come, and I believe it’s not far off. Within the next few years, those T&D players with the most effective digital twins will have a significant competitive advantage over those still using traditional decision-making methods.
That’s the prize on offer. To help your company position itself in the vanguard of digital twin innovation, here are three recommendations:
Digital twin is a concept that’s set to take off rapidly in T&D. Those players who embrace it first will have a vital head start.