AI Virtual Power Plant Orchestration

Coordinates distributed assets (DERs, storage, flexible loads) with AI to deliver grid services and maximize aggregated value.

The Problem

“Optimize DER fleets for grid and markets”

Organizations face these key challenges:

Uncertain DER availability and performance (customer opt-outs, EV mobility, inverter/battery limits) causing under-delivery and penalties

Inability to respect feeder-level constraints in real time (thermal/voltage limits), leading to conservative curtailment or operational risk

Fragmented telemetry and control across vendors/protocols with inconsistent data quality, increasing manual effort and reducing dispatch confidence

Impact When Solved

5-15% uplift in VPP market and DR revenues via tighter forecasting and co-optimized dispatch10-25% reduction in renewable curtailment and congestion-related constraints through location-aware orchestration30-60% reduction in operational workload and 5-10% improvement in battery lifetime using degradation-aware control

The Shift

Real-World Use Cases

AI in Energy Industry: Smart Grid Optimization and Energy Management

This is like giving the entire power system—power plants, grids, and large customers—a real‑time ‘autopilot’ that constantly predicts demand, reroutes electricity, and tunes equipment so you use less fuel, waste less energy, and keep the lights on more reliably.

Time-SeriesEmerging Standard

8.5

AI-Driven Virtual Power Plant Scheduling with CUDA-Accelerated Parallel Simulated Annealing

This is like having a super-fast, very patient planner that tries thousands of different ways to turn distributed energy resources (like solar, batteries, small generators) on and off to find the cheapest and most reliable daily schedule—using a gaming-class graphics card (GPU) to test many options in parallel.

Computer-VisionEmerging Standard

8.5