AI HVDC Transmission Optimization

Machine learning for high-voltage DC transmission system optimization

The Problem

“Optimize HVDC dispatch, losses, and congestion constraints”

Organizations face these key challenges:

Manual, experience-driven HVDC setpoint tuning cannot keep pace with 5–15 minute variability in renewables, load, and network topology changes

Conservative transfer limits and simplified HVDC representations in planning/market tools lead to avoidable congestion, redispatch, and renewable curtailment

Multi-objective constraints (thermal, voltage, stability, converter limits, N-1) create a combinatorial problem that is difficult to solve quickly with traditional workflows

Impact When Solved

Reduce congestion and redispatch costs by 5–15% through proactive HVDC control recommendationsCut combined HVDC/AC losses by 0.3–1.0% and improve transfer utilization by 1–3% in constrained periodsLower renewable curtailment by 20–100 GWh/year and improve operational security with faster, scenario-aware decision support

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