How third-generation AI-powered digital twins can save energy
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Inflation is the highest it’s been in years, and the bottom line is that everything — especially energy — is more expensive. Energy costs were low at the height of the COVID-19 pandemic, so using resources more efficiently became a back-burner issue. But that’s changed; western governments need to cope with a new, far more expensive bottom line — especially now, as North America and Europe head into a potentially cold, dark winter.
And, while governments have adopted a number of longer-term plans to ensure reliable energy supplies, energy providers need more immediate solutions that will enable them to ensure as robust and steady a supply as possible.
Enter third-gen digital twins
One important method that utilities employ to reduce resource usage and waste is digital twin technology: Advanced artificial intelligence (AI) systems that provide clear models that can help ensure that the lights stay on and the heating systems remain on-line. But digital twins come with a significant cost, requiring many technologies and many experts to be effective.
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Enter third-generation digital twins: Systems that determine the best ways to reduce resource use, but can be operated and controlled by utility staff via a standard control interface without the need to find and hire AI experts. These advanced digital twins collect all available data and enable users to develop “what if” scenarios. In energy production, for example, they could determine how plants could operate more safely, with higher quality; be faster and cheaper; and use energy and resources efficiently.
Energy firms have long embraced basic digital twin technology; a virtual model of all operating parts in a system provides insight on how different systems work together and where there are potential problems, such as leakage or inefficient usage. Using those insights, staff can adjust operations to avoid problems or maximize production and efficiency, saving their customers and decreasing resources for their production needs.
Digital twins can also help energy firms save money by predicting potential problems due to equipment breakdowns. By closely examining the relationship between components, systems can determine if there is any fluctuation in power usage, production or any other aspect of the system, and alert staff to potential problems.
Advancing digital twin abilities
Current digital twins are based on the First Principles mathematical model, which applies laws of physics, — such as properties of materials and the relationship between them — to understand and provide controls over a process.
In energy production, for example, that would entail bringing in data from all sources and evaluating how real-world changes would impact the process, essentially covering all aspects of production and enabling managers to determine how best to deploy resources. According to industry experts, energy firms that have deployed digital twins increased operating reliability by as much as 99%; saved as much as 40% on maintenance; and decreased expenses by $11 million by preventing failures.
Digital twins currently in use indeed do provide a great increase in efficiency and reliability, but they come at a price. The systems to provide models that update themselves based on data entail dozens of technologies — most of which have to be licensed at great expense. And it must be operated by individuals with a deep knowledge of AI systems — a resource that itself is in very short supply.
Despite all that, many utilities have begun utilizing digital twins, and it’s likely that many more will do so in coming years, as the need to reduce resource-use grows more acute. But while the digital twin technologies most utilities use will certainly reduce waste and maximize resource use, it won’t cut costs.
The technologies that must be licensed and the high salaries AI experts need to be paid guarantee that, although there is likely to be more power available, it’s going to be more expensive. And smaller utilities that can’t afford those costs, or that serve jurisdictions where power costs are capped by regulators, may not be able to benefit from digital twin technology, at any cost.
Ensuring a steady flow of power
The solution for those utilities — and the industry in general — lies in implementation of advanced third-generation digital twins, which automatically provide updates based on data as it comes in, even if that data does not fit the model. With these advanced systems, energy firms can map out all aspects of operations in a plant, a grid or a series of grids based on real-world data — with production or deployment of energy adjusted on the fly. And all data and controls reside in a standard interface that can be understood and controlled by staff, including those not trained in AI management.
The system can be trained to identify ways to optimize operations. If optimization is possible, then the AI algorithms can be trained based on Generative Algorithms and Diffusion Models, similar to what is used in the natural language processing (NLP) space.
Unlike NLP, however — which is generally used to create pictures, texts, music and videos — this application of the technology helps solve problems in industrial plants, manufacturing systems and power plants. And many of the real-world problems they address can be used to develop solutions essential to achieve zero carbon goals.
Thus, if a power station is out of commission due to storm damage, a third-generation digital twin can automatically funnel power to connected substations to make up for the shortfall — temporarily reducing energy availability in areas where there is less usage or demand. The advanced technology provides clear models that will help ensure that the lights stay on and the heating systems remain online. Staff can respond to challenges and crises in real-time, using a standard interface, ensuring as steady and efficient a flow of power as possible.
“Living” digital twins
Third-generation digital twins can also help make maintenance more efficient. By collecting and analyzing data as it comes in and matching it to a constantly updated model, producers can tell right away if there is a problem and trace it to a specific piece of equipment — giving repair crews the opportunity to repair or replace it before it fails. These systems also make scaling much easier, providing clear data on how real-world changes to systems, such as satisfying additional demand that immediately requires the deployment of additional resources.
The key to achieving this is to develop a “living” digital twin which is constantly updated based on incoming data — a great advance over previous generation digital twins, which produced static models that did not automatically adjust themselves.
With those automatic updates, energy producers can prevent losses and ensure maximal usage of resources. In an era when those resources are harder to acquire, producers need all the help they can get — and advanced third-generation digital twins, using AI algorithms, can help them accomplish those goals.
Ralf Haller is executive VP at NNAISENSE.
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