Context
Control rooms in utilities are equipped with multiple platforms such as ADMS (Advanced Distribution Management System), DERMS (Distributed Energy Resource Management System), and EMS (Energy Management System), each with numerous analytical modules. Operators face cognitive overload as they must manually initiate, parameterise, and interpret studies across fragmented tools.
Challenge
Operators are challenged by latent analysis gaps, decision latency under distributed energy resource volatility, tool fragmentation, and loss of institutional knowledge. There is no autonomous agent to orchestrate analytical modules, resulting in missed proactive interventions and increased cognitive burden.
AI Solution
The solution is an agentic AI layer that orchestrates existing ADMS, DERMS, and EMS analytical modules. It continuously ingests telemetry, detects anomalies, and uses a large language model-based planner to select and sequence analytical functions. The agent synthesises results, generates recommendations with full traceability, and presents them to the operator, who retains final authority.
Impact & evidence
The solution has resulted in a 40-65% reduction in operator decision time, a five to eight times increase in analytical studies per shift, a shift in proactive intervention ratio from approximately 20/80 to 65/35, a 70-85% recommendation acceptance rate, up to 60% fewer voltage violation events, 15-25% reduction in unplanned OLTC/capacitor cycling, up to 22% reduction in DER curtailment, and over 50% reduction in mean time to situational awareness.
Implementation / Adoption
Implementation involves inventory of analytical modules, adapter development, perception layer calibration, agent reasoning fine-tuning, shadow operation, assisted operation pilot, and commercial rollout. The process spans approximately 12 months from kick-off to full rollout.
Data
Required data includes real-time and historical telemetry from SCADA, AMI, DERMS, EMS, GIS, weather feeds, market data, and incident reports. Technical components include tool adapters, perception models, agentic planner, orchestration runtime, and operator interface. Data cleansing and preparation were necessary.
Future enhancements
Planned enhancements include expanding the tool library to outage management and long-term planning, multi-agent coordination, integration with utility knowledge bases, federated learning across pilots, and automation of the operator feedback loop.