Digital twins are a common planning tool used to enrich complex systems such as power grid analytics models. They serve as an integration and enrichment layer that can transform source data from disparate sources to fulfill a wide spectrum of functions. These can range from long-term system planning to real-time grid operations, post-mortem analyses of past events, and simulations of hypothetical scenarios.

A utility’s business needs will drive the degree of fidelity a digital twin must meet. That is why definitions of a particular digital twin will vary with respect to its complexity and context (see Figure 1). 

Data generally describes a snapshot in time. Acknowledging the purpose of a digital twin is to merge data from various contexts into a series of sequential system states, the accuracy of the digital twin’s representation greatly depends on keeping these data sources synchronized. The complexity is due to multiple factors, including periodicity of data capture, the horizon of the data and its availability.

Another important aspect impacting the fidelity of a digital twin state (see Figure 2) is whether it is based on actual or hypothetical input. The more a digital twin is based on historical data (versus forecasts) and an “as-operated” state (versus “as-planned” or other hypothetical scenarios), the greater the fidelity.

Digital twins hold great promise for utilities, but getting them off the ground has never been easy. Utilities often struggle with messy or incomplete data, grid models that require constant upkeep, and the sheer complexity of stitching everything together. Those challenges have kept many digital twin efforts stuck in the pilot phase rather than scaling into everyday operations.

This is where artificial intelligence (AI) can play an enabling role. AI helps reduce the friction of building and maintaining a digital twin. It can automate data cleanup, flag inconsistencies, and even keep grid models tuned and current as new information comes in. It takes on the behind-the-scenes work that otherwise might demand extensive manual effort.

AI agents can be useful to standardize how the digital twin data is accessed and processed in the interest of feeding conventional analytics. Complex agentic AI use cases with scenario optimization and exhaustive computational recommendations also can be implemented to make a digital twin a stronger foundation for advanced analytics.

The results make digital twins more practical and sustainable. AI does not replace the value of a digital twin; it makes it easier to obtain that value by clearing hurdles that utilities have long struggled with. Supplementing their work with AI means utilities do not have to spend as much time managing data curation and access problems; they can focus instead on using the digital twin to improve reliability, efficiency and customer outcomes. 

A digital twin starts where the data from enterprise source systems is made available for ingestion. It ends with a unified data layer describing the state of various aspects of the power grid. This means a digital twin is an enabling layer for analytics, not an end product that produces results that are inherently actionable.

Practically speaking, the implementation of a digital twin can serve multiple purposes. What follows are some potential utility imperatives and highlights of how an AI-enabled digital twin can address those needs.

Streamline basic power distribution modeling activities, empowering engineers to focus on advanced, high-value tasks instead of menial model creation and access activities.

• Create, validate and correct the “as-planned” and “as-operated” power flow models from GIS topology and NMS switch status.
• Allocate power flow models with AMI and SCADA measurements for base-case power flow models.

Access up-to-date information, as an advanced distribution management system (ADMS) is only as valuable as how recent and valid the power distribution model is. Making decisions on a stale model is like flying blind.

• Monitor whether protective device settings remain adequate as new interconnections and circuit build-out occur ad-hoc.
• Validate and correct the “as-operated” model systemwide on an ongoing basis, even for non-SCADA devices.
• Forecast loading per circuit section to readily produce circuit reconfiguration recommendations for the present state.

Enable the interconnection team to approve or deny applications, or right-size system upgrades requirements, with more reliable capacity impact forecasts.

• Avoid fast-tracking applications that should have required a detailed system impact analysis but didn’t because the model did not reflect the latest installed and/or planned distributed energy resources (DERs).
• Determine system upgrade requirements that reflect reality and avoid oversizing or undersizing assets.
• Quantify the loading and power quality impact of data centers beyond the point of interconnection with a richer and more complete model.

Use of a common source of unified data as the foundation for multiple purposes means various teams operate with the same context in mind.

• Maintain consistency in analytics context across departments to reduce the risk of studies producing conflicting or indefensible results.
• Enhance analytics outcomes with full context from disparate sources instead of conventional siloed views of data.
• Readily share complex information through simpler overviews across departments to avoid rework or conflicting decision-making due to inconsistent data sources.
• Enable a simplified “single pane of glass” through a unified set of data sources, merging operational dashboards within a common context.

Prioritize inspection and maintenance budget using real condition–based asset management analytics.

• Calculate asset degradation estimates from the historical, “as-operated” load profiles across power distribution assets, switching operations and fault current exposure.
• Account for the rejuvenating impact of asset replacement and recent inspection and maintenance activities, using AI to extract insights from work order notes.

Monitor the impact of changes to circuit configuration, connected load and generation, and device deployment on arc flash potential across an entire system to promote safety for line workers.

• Review the incident energy per primary line section, accounting for the most current topology configuration.
• Access readily available systemwide load flow, short circuit and arc flash analysis, greatly reducing barriers to accessing information for all users.

Make insights accessible to field crews so they are better informed for daily tasks.

• Access asset-specific information on the fly (e.g., install date, maintenance history, fault exposure and cause).
• Access part and full device inventory and lead time, as well as applicability of planned replacement and capacity expansion programs.
• Access environmental trends such as recent migratory patterns and weather macrometrics.