AI Applications in Smart Grid Management

Overview

Apply AI to real smart-grid challenges: integrate variable renewables, manage DERs, anticipate outages, and maintain voltage/power-quality—without overwhelming control rooms. You’ll translate raw grid data into reliable, actionable recommendations for operators and planners.

Curriculum

• Data landscape: AMI/MDMS, SCADA, PMU/synchrophasors, OMS, GIS, weather & markets.
• Forecasting: feeder/substation load, rooftop PV, EV charging, & price sensitivity.
• Outage analytics: storm/vegetation risk models, fault localization, crew prioritization.
• DER orchestration: demand response, VPP signals, flexibility estimation & verification.
• Power quality & efficiency: Volt/VAR optimization, loss reduction, harmonic insight basics.
• NTL & anomalies: pattern mining on AMI reads, tamper signatures, topology cues.
• Real-time to day-ahead: streaming features, windowing, latency & failover patterns.
• Dashboards & workflows: operator alerts, root-cause hints, and explainability.
• Case study: feeder-level pilot from data ingest to dispatchable actions.

Benefits of Attending

• Battle-tested blueprints to deploy grid AI fast—minimal disruption to existing systems.
• Reusable features & KPIs for reliability, PQ, DER, and outage use-cases.
• Clear governance/MLOps practices for regulated utilities.
• Confidence to brief execs and regulators with transparent, explainable results.

What You Will Learn

By the end of this program, you will be able to:

• Design pipelines that blend AMI, SCADA, PMU, weather & market data for grid AI.
• Build load/solar forecasts and detect outages, anomalies, and NTL patterns.
• Support Volt/VAR, DER scheduling, and demand response with AI recommendations.
• Deliver operator-ready dashboards and explainable alerts for control-room workflows.

Practitioner Profile