The convergence of artificial intelligence and digital twin technology is emerging as a transformative solution, enabling power producers to shift from static control systems to dynamic, self-optimizing operations that can predict failures, automate decisions, and continuously learn from real-world performance data.

The energy landscape is undergoing a fundamental transformation, driven by the urgent need to decarbonize, improve efficiency, and ensure grid reliability. As power producers grapple with integrating intermittent renewable resources, maintaining aging infrastructure, and complying with evolving regulatory demands, one technological advancement is emerging as a critical enabler of operational excellence: artificial intelligence (AI)-enhanced digital twins.

Digital twins are not just static representations of physical systems. They are dynamic, real-time ecosystems, functioning as living assets that reflect and model responses to ongoing real-world changes in operations. When infused with AI, these systems can be more than diagnostic tools, they enable predictive insights and support self-optimizing operations across fossil, nuclear, and renewable generation facilities.

Digital Twins: Laying the Foundation for Autonomous Operations

Digital twins are virtual models of physical assets, systems, or processes that are continuously updated with real-time data (Figure 1). In the power sector, they help operators understand the condition and performance of equipment such as turbines, boilers, nuclear reactors, and photovoltaic panels. By integrating AI, operators can extend their capabilities to forecast failures, optimize performance, and even automate certain decisions.

1. Digital twins help bridge physical and digital operations, supporting on-the-ground teams with predictive insights, historical data, and automated decision support. Courtesy: AVEVA

A report by Guidehouse Insights forecasted the global market for digital twins in the energy industry to reach nearly $2.5 billion annually by 2031, up from $331 million in 2022. This projected growth reflects rising investments in AI-integrated digital twin technologies to support asset optimization, predictive maintenance, and the transition to more sustainable, data-driven operations. As investments continue to grow, the introduction of AI integrations is expected to take the technology to new heights and introduce greater optimization for the energy industry.

The AI Advantage: Advancing from Reactive to Predictive to Autonomous

Traditional automation in power plants relies heavily on fixed control logic and manual intervention. In contrast, AI-enhanced digital twins use machine learning, data analytics, and sensor inputs to enable a variety of automatic functions. They can predict equipment degradation and reduce unplanned downtime by allowing operators to schedule maintenance before assets fail. They can also adjust operations to improve efficiency and reduce emissions, balancing conflicting operational priorities like maximizing output while minimizing wear on critical components. Most importantly, they continuously learn from new data, improving performance over time.

This evolution from reactive to predictive, and eventually to autonomous, operations is gradually unfolding, offering real-world benefits for plant performance and reliability. According to a study from Deloitte, predictive maintenance can reduce maintenance costs by 25% and unplanned outages by up to 70%, improving equipment uptime and operational continuity. This increase in availability can make all the difference for power plants during critical moments, particularly during the summer months, when power grid demand is often at its peak.

Fossil Generation: Enhancing Efficiency and Reliability

Despite the growth of renewables, fossil generation remains an important part of the grid mix, especially during demand surges. In combined cycle gas turbine and coal-fired plants, AI-powered digital twins enable proactive monitoring by detecting early signs of component wear, combustion instability, or system inefficiencies. These tools also optimize heat rate and combustion parameters under varying load conditions, helping improve overall fuel utilization and emissions performance.

One McKinsey & Company case study found that AI-enabled optimization delivered a 2% efficiency improvement within three months at a power plant in Texas, resulting in $4.5 million in annual fuel savings and 340,000 tons of avoided carbon emissions. When this technology was expanded to 67 units across 26 plants, average efficiency gains rose 1%, annual savings exceeded $23 million, and approximately 1.6 million tons of carbon emissions were avoided annually. This growth in both efficiency and cost savings shows that scalable AI-driven optimizations can yield substantial benefits, reinforcing the business case for wider deployment of intelligent digital twin technologies in fossil generation.

At AVEVA, we’ve seen similar gains from AI-powered digital twins in fossil generation. For example, in collaboration with a large operator in the refining sector, digital twin solutions allowed real-time process optimization, delivering more than $2 million in annual savings per crude distillation unit and achieving a payback period of less than one year.

Nuclear Generation: Improving Safety and Decision Making

Nuclear power plants operate under strict safety and regulatory conditions, often facing compliance reporting that requires real-time insights into plant performance. Digital twins have long supported training and simulation in this space, but the integration of AI introduces new layers of insight and automation. Added benefits from AI integration include predictive analysis of component fatigue, automated compliance data synthesis, enhanced emergency scenario modeling, sustainability metrics, and more sophisticated anomaly detection across critical systems.

Just a few years ago, the U.S. Department of Energy’s Office of Nuclear Energy identified digital twins as a priority innovation area, highlighting their capacity to boost safety margins, lower operational costs, and provide plant operators with deeper decision-making support. These federal investments underscore the important role AI-enabled digital twins can play in advancing both operational excellence and regulatory confidence in the nuclear energy industry.

For nuclear power plant operators, AI-powered digital twins can deliver measurable returns by improving safety and streamlining decision-making. AVEVA recently partnered with a leading global nuclear operator to showcase how AI-driven digital twins are transforming engineering capabilities. The solution enabled global teams to collaborate within a shared digital twin environment, unifying design disciplines and 3D models to eliminate silos, reduce data inconsistencies, and boost engineering efficiency, which helped lead to safer, more informed operations.

Renewable Generation: Navigating Variability with Intelligence

Renewable energy sources such as wind, solar (Figure 2), and hydropower each present unique operational challenges that require intelligent forecasting and control to maximize resource management, efficiency, and reliability. Hydropower operations, for instance, must balance water resource management with fluctuating electricity demand, while also accounting for environmental factors such as prolonged heat waves and meeting regulatory requirements. AI-enhanced digital twins can support these requirements by integrating real-time sensor data and historical operational information to optimize turbine performance, predict equipment maintenance needs, streamline critical reporting, and manage water flows more effectively.

2. Artificial intelligence-enhanced digital twins give solar operators real-time performance insights, which helps teams predict equipment failures, optimize output, and streamline compliance reporting in the shift toward autonomous operations. Courtesy: AVEVA

During the COVID-19 pandemic, a major infrastructure operator deployed AI-powered digital twin technology to remotely commission essential facilities in the Middle East. This strategy enabled real-time testing, performance analysis, and optimization despite travel restrictions. By providing remote teams with access to key performance indicators, the project achieved more timely commissioning, better collaboration, and long-term operational improvements—all while helping reduce risk and accelerate delivery schedules in an extremely complex macroeconomic environment.

Grid Integration: Addressing Challenges on the Path to Autonomy

While the benefits of AI-enhanced digital twins are significant, integrating these technologies can introduce challenges. Successful use of AI requires the right data. Data must be clean, consistent, and contextualized to generate reliable results. In mission critical settings, plant operators need to understand the basis for recommendations generated by AI systems. This explainability is critical to building trust and ensuring safety, both of which are critical foundations for successful AI adoption.

Change management also plays an important role, as adoption of new tools requires training and alignment across operational teams. In addition to ensuring teams have insight into why and how AI makes decisions, cybersecurity must also be a top priority. Digital twins rely on real-time data and connectivity, making them lucrative targets for cyber threats. According to KnowBe4’s 2024 global infrastructure report, critical infrastructure faced more than 420 million cyberattacks from January 2023 to January 2024, a 30% increase from the prior year. This surge highlights the need for robust, scalable cybersecurity solutions that integrate seamlessly with existing systems.

Industry Momentum: Demonstrating Progress and Results

Utilities and energy producers are realizing measurable benefits from integrating AI-enhanced digital twins into their operations. In the U.S., a power plant achieved a 44% reduction in plant startup and shutdown times using digital twins, resulting in notable fuel savings and improved operator training. Similarly, a power company streamlined operator training and cut commissioning costs for a new facility, while another accelerated the startup of a greenfield plant by incorporating simulation early in the development process.

Reflecting this broader industry trend, the 2025 EY Future of Energy Survey found that 50% of oil and gas, and chemicals companies are already using digital twins to manage assets, with 92% either implementing, developing, or planning new digital twin-based applications within the next five years. These examples highlight the expanding role of AI-integrated digital twins in driving smarter, more adaptive, and more resilient energy systems across critical industries.

Responsible Innovation: Keeping AI Grounded in Operational Value

The practical application of AI in the power sector must be firmly grounded in reliability, efficiency, and safety. Its true value comes from enhancing—not replacing—operator expertise, enabling better decision-making with contextual awareness, and reducing risks through advanced insights and analytics.

AI-enhanced digital twins offer a pragmatic and scalable path forward, helping power producers improve asset performance, optimize maintenance, and manage increasing operational complexity. Their success relies on careful implementation, transparency to build trust, and a strong foundation of data governance, cybersecurity, and operational discipline.

As energy generation becomes more distributed, decarbonized, and digitalized, AI-infused digital twins have the potential to revolutionize power plant operations by shifting from static control methods to dynamic, adaptive, and autonomous systems. This transformation is increasingly becoming our present-day reality. By integrating AI-enhanced digital twins as an enabler of digital operations, the power industry can speed its journey toward more resilient, sustainable, and intelligent energy systems.

—Arti Garg is Chief Technologist with AVEVA.