Digital Twins: Turning complexity into better decisions

Modern transmission and distribution electrical grids are the most complex machines ever built. They span continents and encompass numerous interconnecting components and subsystems—while intricately balancing energy demand and fluctuating supply.

Not only are today’s grids complex, they are mammoth in terms of components and their geographical size. There are over one billion operational smart meters worldwide and the cables and lines stretch across 80 million kilometers. In other words: ten roundtrips between earth and moon!

And this complexity is only expected to grow. According to a newly released IEA report—Electricity Grids and Secure Energy Transitions—to reach climate targets and ensure energy security, 80 million kilometers of power lines will have to be replaced or added by 2040.

As power grids increase in complexity and scope, grid operators are turning to digital twins. While digital twins have been applied for decades by an array of industries, they are increasingly being used to help grid operators make strategic planning decisions, optimize operational performance, and manage risks within the context of unprecedented complexity.

3 factors that made grids so complex

1. As the world transitions from fossil fuels to renewables, grids need a better equipment to handle the variability of energy sources from wind, solar and hydroelectric.
2. The growing threat of severe weather caused by climate change is putting an additional strain on antiquated electric infrastructures globally.
3. 40GW of rooftop solar panels have been installed worldwide in 2022. This massive, fuzzy, intermittent deployment of solar energy injected into the grid has brought major challenges in power quality and load forecast management.

To handle these growing challenges, power grid operators have turned to digitization to improve the operational management of networks. Smart meters and IoT sensors provide operators with valuable data; yet, they add an additional layer of complexity.

Digital twins: From grid knowledge to understanding

With this increasing complexity and the overwhelming flow of real-time data, digital twins are proving pivotal to the operation of smart grids. They are used in order to:

• Simulate ‘what-if’ scenarios to understand, for example, operational outcomes of varying decisions
• Manage and foresee maintenance needs
• Avoid or limit grid downtime
• Help operators present data-backed asset investment plans.

The power of digital twins is their capacity to virtually reproduce the multi-scale interactions and correlations between organizations, thus providing a more holistic view of the grid and avoiding decisions made in silos. This gives decision-makers of any given department, such as engineering, planning, and operations, the ability to stimulate the consequences of various decisions and their impact throughout the organization. As such, calculated decisions are made based on implications, expected outcomes, and trade-offs and not just on past knowledge and experience.

Digital twins are revolutionizing grid management, as demonstrated by the landmark initiative to build a digital twin of Europe’s electricity grid. One of the initiative’s key aims is fostering innovative technologies in the race to ensure the readiness of the electricity grid for the drastic increase of renewable energy and resiliency to future shocks (such as climate and cyber-attacks).

6 key areas where digital twins are revolutionary

There are six key areas where the deployment of IoT-connected instrumentation sensors together with digital twins are providing impactful benefits and value to grid operators.

Digital twins represent a significant paradigm shift in electrical grid management. They facilitate all aspects of the business and operational mission of grid operators. They are paving the way for more reliable, resilient, efficient, and sustainable power grids, thus enabling the industry to meet its ambitions to be at the forefront of the transition to clean and decarbonized energy.