Augmented Hopfield network for constrained generator scheduling

Many scheduling algorithms do not incorporate all the physical constraints of the problem. However, as their operational environments change many power systems are operated closer to physical limits and scheduling algorithms that consider all constraints are required. This paper presents an augmented Hopfield neural network scheduling algorithm that unifies the unit commitment and generation dispatch functions. This algorithm successfully considers ramp rate, transmission and fuel constraints in addition to the more common constraints. Results show that feasible solutions can be obtained in highly constrained circumstances