Energy grid load balancing

Generator dispatch and storage optimization with market-moving cost and contract data kept confidential.

Who

An independent system operator, large utility, or industrial energy buyer managing dispatch across a generation and storage portfolio.

The problem

Optimal generator dispatch, storage charge/discharge, and demand-response activation given forecasts, transmission constraints, market prices, and emissions targets. The unit-commitment problem is classically NP-hard.

What ArcaQ does

QUBO over discrete unit-commitment decisions, with continuous dispatch handled by classical post-processing on the quantum-optimized commitment.

Expected result (published benchmarks)

Published QAOA benchmarks for unit commitment indicate 1–4% reduction in total generation cost over classical heuristics, with corresponding emissions reductions.

Why confidentiality matters

Generation costs, contract terms, and transmission constraints have market-moving sensitivity in deregulated electricity markets. They stay sealed.

Tier fit

Reserve or Grand Reserve.

The performance ranges below are drawn from published academic and industry benchmarks for the relevant problem class — QAOA portfolio-optimization studies, VQE chemistry benchmarks, and quantum-annealing logistics case studies. They are not ArcaQ measurements. Results vary substantially with problem size, constraint density, and the specific algorithm and hardware used. ArcaQ-specific results will be published after hardware validation.