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Apr 4
The increasing frequency of extreme weather events and aging infrastructure is pushing utilities to use digital twins for real-time monitoring and proactive grid management. Utilities no longer treat digital twins as experimental or “nice-to-have” technologies. Instead, they are integrated into grid modernization strategies to enhance resilience, enable predictive maintenance and provide real-time operational insights.
Integrating AI and ML supercharges digital twins, making them more predictive and autonomous. Utilities can benefit from key advancements, including anomaly detection and self-optimizing grids. Anomaly detection involves AI-powered digital twins detecting irregularities before they cause outages. With advanced ML models, digital twins enable self-optimizing power flows, stabilizing the grid and enhancing outage detection and response times.
We talked to utilities experimenting with AI-driven grid automation, or self-optimizing grids, where digital twins continuously optimize load balancing and fault detection. In addition, some utilities are leveraging a hybrid approach, where cloud-based digital twins handle long-term simulations and large-scale analytics. Others use edge-based digital twins to process real-time, high-frequency grid data closer to the source, reducing latency.
While most of the conference focused on transmitting and distributing digital twins, some vendors and clients are piloting substation digital twins with many key substation assets. There were a few examples of a transformer digital twin, one from Hitachi and one from Sand Technologies. But when asking utilities what they want to see next, most replied that they need more end-to-end digital twins with expanded capabilities.
A few examples of what utilities want include a smart grid digital twin with real-time modeling and fault locating on distribution feeders that pinpoint an outage’s exact location and do not rely on engineers to analyze outage events and manually perform fault calculations. Utilities also want digital twins that accommodate distributed energy resources (DERs), with real-time solar, wind, batteries and EVs modeling. Another need is improved customer demand forecasting to predict consumption patterns and integrate demand response. Utilities also need market and regulatory simulations with the ability to test the impact of a policy change on grid stability.
A growing need is to integrate EV use into grid modeling using digital twins to simulate EV charging demand and its impact on grid stability. Another interesting use is workforce training and simulation, using digital twins for operator training and outage response drills.
Other innovative uses include real-time transformer alerts such as oil temperature, oil level, IR scans, oil sample information, maintenance information, total ownership cost and real-time transformers’ risk scoring. Additionally, there was an example of Gen AI on top of the digital twin analysis that provided recommendations for dispatchers or transformer specialists on what steps to take with each new alert or risk score.
Many utilities struggle with data silos and integration. Key challenges discussed by many at the conference included friction with legacy systems, including utility IT/OT systems, which are not compatible with digital twins. Utilities are going through enterprise asset management (EAM) transformations and moving off legacy EAM systems to Maximo MAS 9, SAP Hana and IFS. Some utilities are concerned that when building a digital twin over the next few years, they may be unable to do some key integrations/interfaces from EAM as they move to these new systems.
The good news is that utilities can build digital twins even without these interfaces/integrations. However, they may have to wait to bring in features like total life cycle costs and maintenance information because the upgrade must go first.
Another challenge is a lack of data standardization. Different vendors and platforms use inconsistent data formats. Cybersecurity remains a hurdle as real-time, connected digital twins increase cyber risks, requiring stronger security frameworks. Several discussions also emphasized the need for better collaboration between utilities, technology vendors, regulators and policymakers to ensure that regulatory roadblocks do not prevent the utilization of digital twins.
Explainability and Transparency
Validation and Benchmarking
Continuous Learning and Adaptation
User Engagement and Education
Risk Mitigation and Reliability Measures
Explainability and Transparency
Validation and Benchmarking
Continuous Learning and Adaptation
User Engagement and Education
Risk Mitigation and Reliability Measures
Asset health management for transformers was a major focus at the conference, given the increasing stress on grid infrastructure from electrification, extreme weather and aging equipment. Most transformers in North America are 40+ years old, well beyond their designed lifespan, making transformer health a growing risk.
In fact, many utilities that visited the Sand Technologies’ booth said they experienced 3-7 transformer failures each in 2024. Their leadership has challenged them about the Asset Health Risk Scoring system. Some utilities at the conference shared that they are using digital twins to assess transformer health and prioritize replacements based on the real-time condition data rather than age alone. Others presented strategies for life extension through advanced cooling techniques, oil regeneration and insulation refurbishment.
Regulatory pressure is increasing for utilities to justify capital spending on asset replacement vs. extending asset life. Utilities that implemented AI-driven transformer health monitoring reported:
With more IoT-connected sensors and remote monitoring, cybersecurity is a top concern. Utilities are hardening their OT networks and adopting zero-trust security models to protect transformer monitoring systems. There was also discussion about blockchain-based data validation to ensure the integrity of transformer health data.
Final Takeaway: Asset Health Analytics is Now Essential
Transformer Health management is shifting from reactive maintenance to AI-driven predictive analytics, real-time monitoring and automation. Utilities investing in sensor technology, AI and digital twins are seeing tangible benefits in reliability, cost savings and resilience. The analysis above for transformer health management can be applied to all other asset classes. A digital twin for transformer fleets should also give companies confidence in the use case for the other asset class priorities.
Vegetation management (VM) was an interesting focus at the conference due to its critical role in grid reliability, wildfire prevention and storm resilience. The ability to predict where tree overgrowth would interfere with power lines and using digital twins to model fire risks and optimize de-energization can play vital roles in wildfire prevention. The discussions highlighted advancements in AI-driven analytics, satellite imagery, LiDAR and automation to improve VM efficiency and cost-effectiveness.
For most utilities, vegetation management is the single largest OPEX budget item, mostly outsourced to contractors for cycle trimming work (typically 4-5 years) based on regional species, growth rates and regulatory requirements.
One interesting use of robotics and automation is in autonomous forestry equipment. Utilities showcased robotic tree-trimming machines and AI-guided brush-clearing drones to reduce the need for human crews in hazardous areas. Another innovative use of robotics is line inspection, where autonomous robots crawl power lines and identify vegetation encroachment in real time, reducing the need for manual inspections.
A closely related use is automated herbicide application. Some companies demonstrated AI-driven spraying drones that selectively apply herbicide only to invasive or high-risk vegetation, reducing chemical use.
A key takeaway from the 2025 DISTRIBUTECH® conference was that digital twins are no longer just a future vision — they are moving from concept to reality. Digital twins are actively being deployed/piloted across the grid. However, success depends on AI integration, data interoperability, cybersecurity and regulatory support.
Another key takeaway is that asset health analytics are now essential. Transformer health management is shifting from reactive maintenance to AI-driven predictive analytics, real-time monitoring and automation. Utilities investing in sensor technology, AI and digital twins see tangible reliability, cost savings and resilience benefits.
A final takeaway from the conference is that the industry is moving toward AI-powered, sensor-driven and automated vegetation management strategies for better grid resilience. Utilities that invest in predictive analytics, remote sensing and robotic automation are seeing cost savings, improved reliability and reduced wildfire risk.
Utilities that embrace these technologies will be well-positioned to modernize their grids and ensure energy resilience.
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