AI-Driven Smart Grid Optimization
The grid was destabilizing due to rapid renewable adoption. Human operators couldn't react fast enough to wind/solar fluctuations.
The national body responsible for electricity transmission faced stability challenges with the influx of intermittent renewable energy sources.
•National-scale grid
•High renewable penetration
•Stability instability
•Zero-downtime mandate
Load Balancing Agent
RL agents controlling substations to balance frequency in milliseconds.
Demand Response
Automated signaling to industrial consumers to shed load during peaks.
Renewable Forecasting
Hyper-local weather models predicting solar/wind output 24h ahead.
1
Intermittency
Solar/wind volatility risked grid collapse without rapid buffering.
2
Cyber-Physical Risk
AI control loops had to be air-gapped from public networks.
18%
reduction in energy wastage from balancing inefficiencies
99.999%
grid uptime achieved over 12 months
40%
drop in manual intervention events
200MW
of virtual capacity unlocked via demand response
We deployed a deep reinforcement learning control plane that predicts supply drops and automatically rebalances loads across 500+ substations.
PythonTensorFlowRayTimescaleDBKubernetes
