2026 Event Schedule

At Distributech 2024, we introduced a novel AI-powered framework within the ADMS ecosystem, enabling DSOs to achieve low-voltage (LV) and medium-voltage (MV) grid visibility using existing GIS and AMI data—without deploying additional hardware. The method leveraged advanced AI and constraint-based algorithms to infer accurate grid connectivity and deliver virtual telemetry for unmonitored assets, offering DSOs a cost-effective, rapidly deployable alternative to traditional modeling and sensing strategies. Crucially, this holistic approach to grid visibility unlocks a wide range of actionable insights through analytics—enabling DSOs to transition from static monitoring to proactive, data-driven decision-making.

For Distributech 2026, we present the full-scale, enterprise-grade deployment of this solution across the AEP Texas distribution grid, now covering over 1.1 million end customers. This marks a major milestone in operationalizing AI-powered grid intelligence across a large, heterogeneous live utility network.

In this session, we will present the latest advancements and expanded use cases achieved through this deployment, showcasing how DSOs can move beyond visibility toward truly predictive and data-driven operations. Highlights include:

• Enterprise-Scale Digital Twin Generation: Using AI-driven topology inference and constraint-based modeling, the system automatically builds and maintains a fully connected and computable grid model, even in the presence of incomplete or inconsistent GIS and metering data.
• Full-Layer Virtual Telemetry: Advanced AI estimation techniques generate real-time voltage, load, and energy flow values across non-instrumented assets—including secondary transformers, cables, and switchgear—eliminating the need for new field sensors. The accuracy of these estimates has been rigorously measured and validated throughout the scale-up, consistently exceeding operational thresholds across both rural and urban grid segments.
• Grid-Wide Predictive Intelligence: New modules provide short-term forecasting of voltage violations, congestion points, and asset stress, empowering proactive operations and enhancing reliability under increasing DER and load variability.
• AMI-Driven Outage Restoration Support: Real-time anomaly detection and inferred connectivity are used to detect and localize outages during extreme weather events, reducing restoration times and improving field coordination.
• DER Integration and Grid Reinforcement Planning: High-resolution, system-wide visibility supports dynamic hosting capacity analysis and targeted infrastructure upgrades.
• Acceleration of Grid Planning and Design: Automated, continuously updated models reduce the time and effort needed for load flow studies, scenario analysis, and investment decisions.

We will also provide an outlook on emerging use cases under active testing, including revenue protection through AMI anomaly detection and wildfire prevention via inactive line identification.

Finally, we will share real-world results from AEP Texas, illustrating how AI-based methods can be successfully scaled to enterprise level, integrated with ADMS and OMS systems, and used to enhance both operational and planning functions. This presentation offers a proven, actionable pathway for DSOs to evolve from fragmented visibility to comprehensive, predictive grid intelligence—using the data they already have.