Learning‐based load control to support resilient networked microgrid operations

Networked and interconnected microgrids can improve resilience of critical end-use loads during extreme events. However, the frequency deviations in microgrids during transient events are significantly larger than those typically seen in bulk transmission systems. The larger frequency deviations can cause a loss of inverter-connected assets, resulting in a loss of power to critical end-use loads. Grid Friendly ApplianceTM (GFA) controllers can mitigate the transient event effects by engaging end-use loads. This paper presents a method to select set-points for end-use loads equipped with GFA controllers, while minimizing the interruptions to end-use customers. An online (i.e. real-time), device-level algorithm adjusts individual GFA controller frequency setpoints based on the operational characteristics of each end-use load and on the changing grid dynamic characteristics to selectively engage the load for mitigating the switching transients. The adaptive gradient-descent-based algorithm does not require control or coordination amongst end-use devices for adapting frequency set-points. The method is validated using dynamic simulations on a modified version of the IEEE 123-node test system with three microgrids using the GridLAB-DTM simulation environment. The improved dynamic stability achieved through the engagement of GFAs support the switching operations necessary for networked microgrid operations.