AI Charging Infrastructure Planning

The Problem

“Optimize EV charging buildout under grid constraints”

Organizations face these key challenges:

Uncertain, highly localized EV charging demand and peak coincidence causing under/over-sizing of sites

Limited feeder/transformer headroom and long interconnection queues leading to schedule slips and cost overruns

Fragmented data across utility planning, mobility, permitting, and charger operations with inconsistent quality and refresh rates

Impact When Solved

Reduce grid upgrade and make-ready spend by 10-25% via grid-aware site selection and sizingIncrease charger utilization and energy throughput by 15-35% while maintaining service-level targets (e.g., <5% peak-hour wait probability)Cut planning and interconnection iteration time by 20-40% through automated screening, scenario analysis, and prioritized project pipelines

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 Grid Congestion Management

This AI helps optimize the layout of power grids to reduce congestion without increasing costs or carbon emissions.

optimizationgrowing

8.0