One digital twin is not enough: Building a digital twin network for strategic advantage

In the energy industry, one digital twin is not enough. This industry encompasses a wide range of systems, including gas networks, power grids, renewable energy sources, hydrogen networks, carbon capture and storage systems and heat networks. Each of these systems has specific characteristics and operational requirements.

Given the level of complexity and the need for real-time insights and predictive analytics, no single digital twin will ever be able to adequately model and represent these different systems effectively. Energy companies need multiple digital twins, with each focused on the capabilities, characteristics and behaviors of a particular system (and its components).

Connecting digital twins into a connected digital system can help organizations to see the bigger picture. This connectivity helps professionals to understand how changes in the behaviors in one twin impact others in the network. Using this approach, energy companies can enhance decision-making, optimize resource allocation and improve operational efficiency. 

The energy industry is undergoing a transformative shift driven by the need for more sustainable, resilient and efficient energy systems. This need, combined with the introduction of distributed, local energy production systems that can efficiently meet the needs of individual homes and communities. Some of the key themes include:

• Decentralization: The move away from large, centralized power plants to smaller, more local power generation facilities. 
• Decarbonization: Increasing the share of renewables, improving energy efficiency and reduce emissions from existing power generation systems.
• Diversification: Introducing a broader mix of energy sources in the grid to enhance energy security and reliability.

Managing these interconnected systems is complex. The integration of various energy sources, from traditional power plants to renewable energy sources like wind and solar as shown in Figure 1, requires sophisticated coordination and real-time monitoring. The challenge lies in balancing supply and demand, ensuring grid stability, and optimizing energy efficiency while minimizing environmental impact. 

This is where digital twins come in. By creating dynamic, virtual representations of physical systems, they can provide a detailed, real-time view of operations and support predictive modelling, enabling stakeholders to anticipate and mitigate potential issues before they arise. With digital twins, energy companies can monitor performance, predict maintenance needs, and optimize the design and integration of renewable energy sources.

That said, no single digital twin can encapsulate and model an entire organization, at least to the level required for effective decision-making. In the energy sector, the necessity for multiple digital twins is due to the intricate, expansive and diverse nature of operations. These operations include asset lifecycle management, power generation, transmission, distribution and consumption plus the increasingly critical aspect of renewable energy: managing energy capture, storage and support for microgeneration.

A digital twin is a dynamic, virtual representation of a physical object, system or process, faithfully reflecting its real-world counterpart by using up-to-date data. This digital replica employs simulations and machine learning models combined with data analysis and agentic AI to provide profound insights and predictive capabilities.

Digital twins provide the most value when created to support a specific domain or business functions (illustrated in Figure 2). Integrating with relevant systems, data and key performance indicators (KPIs) in that area, digital twins offer real-time visibility, predictive insights and scenario testing to inform decision making.

Organizations in the energy sector should consider building different types of digital twins:

• Design twins focus on the optimal construction and planning of energy networks and assets during the initial phase of capital projects, helping ensure that foundational aspects are robust and well-thought-out. 
  
• Engineering twins provide insights into the potential impact of new capability or changes to the components and capabilities already present in the energy infrastructure 
  
• Operating twins focus on maintenance, resilience, disaster recovery and business continuity of energy assets. They can monitor the condition and performance of assets in real time, predicting maintenance needs and potential failures before they occur. A proactive approach can help reduce downtime, optimize asset utilization and extend the lifespan of equipment.
  
• Planning twins can simulate the impact of design changes or new technologies on existing infrastructure. Engineers can test various scenarios and configurations in a virtual environment, identifying the most efficient and cost-effective solutions without disrupting actual operations. 

Although digital twins share a common base set of information, including weather data, asset performance, financial and even operational insights, they bring a different set of insights. This information can be based on business rules, use cases, perspectives and expected outcomes. 

For instance, weather data can be used in a design twin to help with the placement or orientation of renewable technologies. From an operational twin perspective, the same source can be used to model expected demand patterns and storage requirements, key given renewable power generation is (so) dependent on weather conditions. 

Integrating digital twins across different business functions and systems enables energy companies to create a comprehensive, interconnected view of their operations. This integration allows for seamless data flow and real-time insights into every aspect of the energy value chain, from generation and transmission to distribution and consumption.

Acting together, digital twins can simulate the impact of external factors such as weather events, hardware and equipment failures, regulatory changes and market fluctuations on the energy infrastructure. This capability enables energy companies to develop robust contingency plans and optimize their strategies to mitigate risks and capitalize on opportunities.

For instance, in the face of an approaching storm, a digital network can model the potential impact on the energy grid and recommend pre-emptive measures to minimize disruptions. Imagine a scenario where a wind farm operations twin detects a potential fault during a storm and relays this information to a grid operation twin. In turn, this twin can adjust the grid's load distribution to prevent disruptions.

In addition to operational and maintenance efficiencies, integrated digital twins can help drive innovation in the design and development of new energy solutions. By creating virtual prototypes and running simulations, engineers are able to test and refine their designs. With this method, they can try out different options and understand the potential impact on the grid of those changes before committing to any costly physical prototypes.

The strategic implementation of interoperable digital twins provides a unified, dynamic and detailed view of the organization. This implementation not only drives operational excellence but also offers a competitive advantage in an increasingly data-driven world.

Digital twins have immense potential to transform how organizations operate. From optimizing performance and predictive maintenance to crafting more resilient strategies, the benefits are significant. These advantages are especially clear when twins are interconnected within a digital system, providing a platform for substantial levels of insight and efficiency, which enables real-time decision-making and proactive issue resolution.

However, the widespread adoption of digital twins has been slower than anticipated, despite their significant advantages. The complexities of integrating digital twins with existing systems entail ensuring data accuracy and consistency, identifying data ownership and addressing data security and privacy concerns. These aspects are formidable obstacles that require careful planning and robust solutions.

• Integration of digital twins with existing systems. This process often requires substantial changes to the current OT-IT infrastructure, which can be both time-consuming and costly. Also, ensuring data accuracy and consistency is crucial, as digital twins rely on real-time data to provide accurate simulations and predictions. Any discrepancies in data can lead to incorrect insights and decisions.
• The complexity of creating and maintaining digital twins. Developing a digital twin involves sophisticated modelling and simulation techniques, which require specialized skills and expertise. This aspect can be a barrier for organizations that do not have the necessary talent in-house. Furthermore, the cost of implementation can be high, especially for large-scale projects that involve numerous assets and processes.
• Data security, ownership and privacy. Digital twins often involve sensitive data, and ensuring protection against cyberthreats for this data is paramount. Executives must invest in robust cybersecurity measures to safeguard their digital twin systems.

Despite these challenges, the potential benefits of digital twins are too significant to ignore. They enable organizations to gain a unified, dynamic and detailed view of their operations, driving operational excellence and offering a competitive advantage in an increasingly data-driven world. The integration of (multiple) digital twins across business functions providing a comprehensive view of operations, facilitating real-time decision-making and proactive issue resolution.

IBM Consulting®, with its unparalleled expertise in digital transformation and in working directly with the energy industry, has developed the Energy Hub, a platform for the energy industry. Built on Microsoft Azure and Azure AI, this platform helps energy clients to address many of these challenges more efficiently. Also, clients are able to create digital twins that unlock the power of data, AI and ML to deliver a real competitive advantage.

IBM® Energy Data Hub is embedded and integrated with an energy company's existing operational technology (OT) and IT systems and processes (shown in Figure 4). This system consists of a service that supports efficient collection, aggregation, validation and interpretation of operational data. Also, it includes a set of interfaces that provide the foundation for a wide range of clean energy applications. 

These applications include digital twins for renewables, power generation and medium voltage and low voltage (MV/LV) networks, which are powered by agentic AI. This approach redefines the workflow and processes across the energy system and can help improve network operations and asset planning. Imagine an integrated digital network that streamlines operations, enhances data-driven decision-making, and fosters seamless collaboration across the industry.