While AI can process vast amounts of data & suggest optimal actions in milliseconds, the grid operator remains the ultimate authority. Every time an operator tweaks an AI recommendation, based on local knowledge or experience, the system learns.
Data & insights flow up from the Physical Grid, through layers of AI models & knowledge integration, to inform strategic decisions. These decisions vetted by human judgment flow back down to the Grid Control layer, becoming executable commands that optimize grid operations.
Every cycle – from data to decision to execution – makes the system smarter, safer, and more efficient. In this cycle Grid isn’t just reacting to challenges, it’s proactively learning from them.
>Drivers – Lack of self-optimization, no autonomous learning, manual adjustments required.
>Solutions - Self-improvement, adaptability, efficiency.
>Foundational Models – A foundational model for power grids would be a large-scale, pre-trained model that captures the dynamics, physics & operational patterns of power systems. These would be domain-specific, trained on vast amounts of power system data.
>GridBERT: Model for Grid Sensor Data. Time-series data from PMUs, SCADA, weather systems. Predict sensor values for the next time step.
>GridGPT: A Language Model for Power Systems. Millions of operational logs, incident reports, maintenance records, academic papers, regulatory documents. Infer implicit knowledge i.e. understands N-1 violation.
>GridDiffusion: A Generative Model for Grid Scenarios. Generate realistic "what-if" scenarios for planning & training ie high renewable penetration.
>GridFormer: A Transformer for Grid Topology. Historical grid topologies, switching operations, and resulting system states. Optimize grid reconfiguration for market objectives.
>Technologies – Integrating with RAG, Graph RAG, and RAFT for Self-Learning.
>Knowledge Integration with Graph RAG:
Continuous learning: With processing of the real time data and reading
operators actions & reports it updates & overtime graph becomes a living
model.
>Decision Making with RAFT: Analyze recent changes & simulate scenario. Evaluate impact of the recent changes & reconfigure feeders & load patterns.
Continuous Learning & adaptation: After each RAFT process model learns & adapts to the changes & solutions.
>Human-in-the-Loop with RAG: Despite all this automation, grid operators remain crucial. They query the system using natural language. GridGPT, enhanced by RAG, provides answers grounded in the latest grid state (from Graph RAG) & historical knowledge.
The Result: A Self-Learning Grid:
LET THE GRIDS LEARN FOR THEMSELVES: https://lnkd.in/gt7nhb7Z
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