AI Peer-To-Peer Energy Trading

Matches local buyers and sellers and optimizes bids/offers using AI while respecting network and settlement constraints.

The Problem

“Inefficient local energy trading and grid congestion”

Organizations face these key challenges:

DER volatility and uncertainty: inaccurate PV/load/EV forecasts cause imbalance costs, curtailment, and poor participant experience

Grid constraint risk: unmanaged local exports/imports can overload transformers/feeders and create voltage excursions, limiting DER hosting capacity

Complex settlement and trust: high transaction volumes, meter data quality issues, and potential gaming increase reconciliation effort and dispute rates

Impact When Solved

5-15% peak reduction on constrained feeders, enabling measurable capex deferral (often $0.5M-$5M per substation area depending on upgrade scope)10-25% higher prosumer revenues (or bill savings) by selling locally at higher value periods and reducing low-value exports/curtailment30-60% lower program OPEX via automated forecasting, matching, fraud/anomaly detection, and near-real-time settlement

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