Non-cooperative games to control learned inverter dynamics of distributed resources

We propose a control scheme via a non -cooperative linear quadratic differential game to coordinate the inverter dynamics of Distributed Energy Resources (DERs) in a microgrid (MG). The MG can provide regulation services in support to the upper -level grid, in addition to serving its own load. The control scheme is designed for the MG to track a power reference, while each DER seeks to minimize its individual cost function subject to learned inverter dynamics and load perturbations. We use a nonlinear high-fidelity model developed by Sandia National Laboratories to learn inverter dynamics. We determine a Nash strategy for the DERs that uses state estimation of a Loop Transfer Recovery. Results show that the control scheme enables savings up to 9.3 to 208 times in the DERs objective cost functions and a time -domain response with no oscillations with up to 3 times faster settling times relative to using droop and PI control.