Physics-informed AI digital twins and agents for electric transmission and distribution system automation

ThinkLabs AI develop specialized AI digital twins for grid power flow, and AI agents to automate utility planning and operations processes. The electric grid is getting too complex and changing too quickly for traditional utility processes. Automation is necessary for a safe, reliable, affordable and sustainable energy system. Much like autonomous driving for the grid, we've developed specialized physics-informed AI digital twins to automatically and continuously "read the roads" on grid power flow, manage it within the lanes for congestions and voltage violations, generate routing directions in automated planning, and driving assistance with a copilot for operations. Example use cases include automated interconnection studies, planning automation, model validation, operational planning, real time operational situational awareness, optimal dispatch, auto-dispatch, and event management.

ThinkLabs has developed AI agents specialized for the automation of mission-critical grid operations, modeled after the same design patterns as autonomous vehicles:

- Learning system – novel physics-informed AI that learns the complexity of grid power flow (e.g., reading the roads) and utility workflow (e.g., learning how to drive).

- Pre-training – training the AI on numerous potential grid scenarios (e.g., load growth, DERs, two-way power flow), such that the AI is instantly ready for variable and unpredictable conditions in runtime.

- Speed and scale – 8760 3-phase AC unbalanced power flow and state estimation performed in seconds to minutes, reducing entire system studies to minutes and hours, scalable across entire networks in utility service territories.

- Generative – AI learns from training and operational experience and recommends optimal solutions to grid constraints, complementing human intuition, knowledge, and trial-and-error approaches.

- Agentic – perceive, reason, and act to automate planning and operational workflows, being trained on grid power flow, standard practices, and operator experience.

While AI has been prevalent in grid use cases such as forecasting, visual intelligence, and asset management, for AI to solve fundamental challenges with grid planning and operations, it must be able to perform power flow analysis. ThinkLabs has pioneered a method to integrate the fields of power systems engineering with AI, known as “physics-informed AI.” This means AI that is trained by, works with, and integrates into engineering systems (e.g., CYME, PSSE, PSLF, ADMS).