Coordination of Fast and Slow Grid Edge Resources: A Model Predictive Control Approach

Power system evolution is fueled by the increased integration of grid-edge resources (GERs), such as behind-the-meter solar + storage and flexible demands. These resources' un-certainties and geographical spread add considerable complexity to grid operations regarding grid flexibility usage. The coordination of GERs mitigates these challenges and turns them into assets for balancing grid demand and providing necessary grid services. GERs have distinct responding characteristics; some react in real time, and others require scheduling in advance. With multi-time-scale optimal coordination, these GERs may function as loads or power sources, providing essential grid services to enhance the grid's robustness and reliability. This study presents a multi-time-scale coordination model for GERs, optimizing the coordination of each resource in response to grid demands. We employ the Model Predictive Control (MPC) framework to model fast and slow responses of battery energy storage systems and slow responses of HVAC. Our findings demonstrate the effectiveness of GERs in offering grid services and highlight the advantages of the multi-time-scale MPC model in coordinating resources with different responding characteristics.