AI Distributed Energy Resource Management (DERMS)

AI-driven management and optimization of distributed energy resources including solar, storage, and demand response integration.

The Problem

“Orchestrate DERs reliably amid volatility and constraints”

Organizations face these key challenges:

Limited real-time visibility into behind-the-meter DER output, state of charge, and customer load, causing conservative operating margins and unnecessary curtailment

Volatile, uncertain net load and DER availability (weather-driven PV, stochastic EV charging) leading to forecast errors, congestion, and higher imbalance costs

Complex, feeder-specific constraints and heterogeneous device capabilities (inverters, batteries, thermostats, EVs) make scalable, compliant dispatch difficult with rules-based control

Impact When Solved

Feeder-aware dispatch reduces overload/voltage violations and enables 5–15% peak reduction in targeted constraint zones10–30% less renewable curtailment through granular, real-time optimization and dynamic export limits3–8% reduction in balancing and ancillary service costs plus 30–60% fewer manual dispatch actions via automated orchestration

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