2027 Event Agenda

The utility industry has invested heavily in digital twins, ADMS, OMS, and DERMS platforms to support an increasingly complex grid. Yet as distributed energy resources, electrification, and emerging AI-driven load growth accelerate, a fundamental question is emerging: can utilities trust the network models that drive these systems?

Connectivity errors, incorrect customer associations, phase assignment inaccuracies, undocumented network changes, and model drift continuously accumulate as distribution systems evolve. While utilities routinely measure reliability, asset health, and power quality, few continuously measure the integrity of the network model itself.

To address this challenge, AEP Texas and Sensewaves have deployed a Dynamic Grid Model Integrity (DGMI) framework across the AEP Texas distribution network, serving more than 1.1 million customers. Building on previous work focused on AI-powered grid visibility and digital twin generation, this initiative addresses a new challenge: transforming digital twins into trusted network models that can support increasingly critical operational and planning decisions.

The DGMI framework continuously reconciles AMI measurements, SCADA observations, engineering records, operational history, and external grid signals against the documented network model. By combining these diverse sources of operational evidence with power-system physics and AI-driven analytics, the platform continuously evaluates whether the model remains consistent with observed grid behavior and identifies where confidence is gained or lost.

The session will demonstrate how this approach supports the continuous validation and improvement of network connectivity, electrical phasing, customer-to-transformer associations, switching topology, and overall operational consistency. Dynamic observability is further enhanced through virtual telemetry generated across non-instrumented assets, providing additional evidence to validate both the static structure and dynamic behavior of the network.

Beyond discrepancy detection, the initiative introduces a closed-loop model assurance process in which model issues are identified, prioritized, corrected, and revalidated over time. More importantly, it establishes Continuous Model Assurance as a new operational capability for utilities. Similar to how utilities continuously monitor reliability, asset health, and power quality, AEP Texas has begun implementing processes to continuously assess and improve the quality of the network model itself. The session will demonstrate how model integrity metrics can be used to monitor model drift, measure progress, and ultimately make network model quality a measurable operational KPI.

The presentation will also illustrate how greater confidence in the network model translates into better operational and planning outcomes, including more accurate outage management, improved hosting capacity assessments, enhanced DER integration studies, and stronger support for electrification and emerging AI-driven load growth.

Rather than asking whether a utility has a digital twin, this session explores a more important question: can utilities continuously measure, improve, and validate trust in the network model that drives their operational and planning decisions?