Quantum Computing for Power Grids

Apply quantum algorithms to power flow optimization, unit commitment, and grid stability analysis.

+

Quantum Grid Optimization

Course Material

This course uses Quantum Grid OS, an open-source Python library for quantum computing applications in power systems.

Visit quantumgridos.com

Power Grid Applications

Optimal Power Flow

QAOA for OPF problems
Congestion management
Loss minimization

Unit Commitment

Generator scheduling
Reserve optimization
Cost minimization

Renewable Integration

Stochastic optimization
Storage scheduling
Demand response

Course Preview

Quantum OPF with QuantumGridOS12:20

What You Will Learn

Quantum computing basics for power engineers
Formulating grid problems as QUBO
Using QuantumGridOS library
Optimal power flow with QAOA
Unit commitment optimization
Hybrid quantum-classical solvers

Delivery Format

Live Online Sessions

Hands-on coding with real grid models

4-Week Program

8 sessions, 2 hours each

Small Cohorts

Maximum 10 participants

Ready for Quantum Grid Optimization?