Quantum-AI-Powered Software Platform
Quantum-Security Constrained Unit Commitment
The security constraints unit commitment (SCUC) is a powerful scheduling technique used in power markets for daily planning. SCUC is a framework that combines two common algorithms in the electricity industry: Unit Commitment (UC) and Economic Dispatch (ED), while adding a new dimension – Security.
We provide quantum solutions for the unit commitment (UC) problem. UC is a mixed-integer, nonconvex, nonlinear, NP-hard problem, used to determine the on/off schedule and dispatch of system generation units in order to supply a forecasted load. UC is the most critical optimization problem in power systems, in which even small improvements in its optimality can translate into millions of dollars of savings for the grid operators and customers.
Quantum-Energy Management System
The Energy management system tracks and manages customers' energy usage. This system is designed to help companies monitor, manage, and ultimately reduce electricity customers' energy consumption and boost energy savings.
The power flow (PF) problem is one of the most fundamental power system problems. The power flow problem is a numerical analysis based on the physics of the grid, which is the keystone of electric utilities’ decision-making in grid operation, control, and planning.
Quantum-Optimal Power Flow
We provide quantum solutions for the Optimal Power Flow (OPF) problem. OPF is a critical optimization problem in power system operation that determines the most economical dispatch of available generation units in the system to meet a given demand considering the physical constraints of the transmission network.
Quantum- Security Constrained Optimal Power Flow
The security constrained optimal power flow (SCOPF) problem, which is the cornerstone of economic operations in power grids, minimizes the system generation cost by meeting the power balance and satisfying grid physical constraints. We provide a quantum solution for this ever more critical problem to improve both solution optimality and computation time.