• Machine Learning
  • Digitization
  • Utility Digitization
  • Utility Transformations
  • Utility Transformations

Digital Twinning in the Energy Industry

Pritil Gunjan
Jul 17, 2018

Digitization, decentralization, and decarbonization are the key megatrends effecting the energy landscape. Advances in clean energy technologies mean that the energy landscape is increasingly decentralized and distributed. Smart and connected assets can be optimized by leveraging operational data. Asset performance and efficiency gains are the leading use cases energy generators.

An exciting trend in the energy industry is the rise of digital twins. Digital twinning enables an operator to map directly physical assets to a digital representation. The key building blocks of digitization—such as the Internet of Things, sensor technologies, virtual reality, smart platforms, and connected solutions—can synthesize extensive asset data and information.

Digital twins have a dynamic data model containing data attributes of the actual physical asset. These attributes are associated with sensors that measure multiple variables to represent real-world operating conditions and key information such as the installation date or OEM. They incorporate simulations, data analytics, and machine learning capabilities to reduce the actual development time or to optimize asset performance.

The CEO of General Electric (GE) Digital stated that the company saved $1 billion (€0.85 billion) in 2017 through productivity gains from using its Predix platform and similar artificial intelligence based applications for its factories, power plants, aircraft, and energy turbines. GE is currently piloting a digital wind farm concept, which records the configuration of each wind turbine prior to procurement and construction. Once the farm is built, each virtual turbine is fed data from its physical equivalent, and software enables power production optimization at the plant level by adjusting turbine-specific parameters, such as torque of the generator or speed of the blades. Similarly, Siemens can use digital twinning across the equipment lifecycle to simulate, predict, and optimize the product and production system of a digital representative even before investing in actual physical prototypes and assets.

Business Value of Digital Twins

  • Real-time predictive analytics: Digital twinning provides asset owners with enhanced real-time analysis and critical efficiency parameters, it also prevents downtime by extending its application to predictive maintenance and efficiency optimization. Predicting equipment failures and non-performance of assets using digital representation, asset owners can improve uptime and excessive physical repair costs especially in cases where scheduled calendar maintenance and reactive repairs increase asset downtime.
  • Demand response (DR): Distributed generation technologies come with their own set of challenges. Managing the variability and intermittency of wind and solar is important to improve asset performance and to keep the asset running at peak performance. Asset operators can optimize DR management by monitoring wind speed and forecasting demand-supply aggregation.
  • Remote asset management: Operators can now visualize an assets performance in their environments based on different virtual scenarios, thereby reducing service costs. Physical proximity is no longer necessary to get information about asset performance. Installations in remote and island locations are not always easily accessible and digital twins can provide the best solution to drive asset performance in these situations.
  • Research and innovation: Operators can build a digital representation of the equipment and simulate how it might behave under different scenarios. Digital twins can demonstrate the effect of changes in design, scenarios, environmental conditions, and other variables, eliminating the need for a physical prototype. Decoding critical interdependencies, data insights, and preproduction equipment analytics will act as additional inputs for research and innovation into newer technology and products.

Digital Twinning Boosts the Bottom Line

Most importantly, digital twinning is not limited to distributed power generation. Its application extends to conventional power generation—coal, gas, and nuclear facilities—where digital twins can unlock exciting opportunities across asset utilization, predictive maintenance, and DR. This technology can minimize the negative effect of equipment failures and downtime to save millions of dollars.