Artificial Neural Network for Unit Commitment on Networks with Significant Energy Storage

Adding energy storage capacities equivalent to several hours of demand can lower operating costs of independent power providers and reduce reliance on the regional utility network. Energy storage compounds the mixed-integer unit commitment problem by coupling the scheduling decisions within the dispatch horizon, making otherwise straightforward problems highly computationally intensive. A sufficiently trained artificial neural network (ANN) can altogether void the mixed-integer unit-commitment problem and facilitate dispatch receding horizon control in applications with significant energy storage assets. Neural networks with varying layers are trained to replicate the optimal binary unit commitment status of the micro-grid generators as determined by a non-linear mixed integer solver. The combined approach proposed uses an ANN to determine the unit-commitment solution and quadratic programming to determine the active generation and storage set points. The accuracy in replicating the mixed-integer unit commitment exhibits depreciating improvement with additional network layers. The single layer ANN reduced dispatch time by a factor of 40 while replicating unit commitment with 95.3% accuracy. A two-layer ANN reduces dispatch time by a factor of 20 with a 97.3% accuracy.