Design of Resource Agents with Guaranteed Tracking Properties for Real-Time Control of Electrical Grids

We focus on the problem of controlling electrical microgrids with little inertia in real time. We consider a centralized controller and a number of resources, where each resource is either a load, a generator, or a combination thereof, like a battery. The centralized controller periodically computes new power setpoints for the resources based on the estimated state of the grid and an overall objective, and subject to safety constraints. Each resource is augmented with a resource agent that a) implements the power-setpoint requests sent by the controller on the resource, and b) translates device-specific information about the resource into an abstract, device-independent representation and transmits this to the controller. We focus on the resource agents (including the resources that they manage) and their impact on the overall system’s behavior. Intuitively, for the system to converge to the overall objective, the resource agents should be obedient to the requests from the controller, in the sense that the actually implemented setpoint should be close to the requested setpoint, at least on average. This can be important especially when a controller that performs continuous optimization is used (because of performance constraints) to control discrete resources (for which the set of implementable setpoints is discrete). In this work, we formalize this type of obedience by defining the notion of c-bounded accumulated-error for some constant c. We then demonstrate the usefulness of our notion, by presenting theoretical results (for a simplified scenario) as well as some simulation results (for a more realistic setting) that indicate that, if all resource agents in the system have c-bounded accumulatederror, the closed-loop system converges on average to the objective. Finally, we show how to design a resource agent that provably has c-bounded accumulated-error for various types of resources, such as resources with uncertainty (e.g., PV panels) and resources with a discrete set of implementable setpoints (e.g., heating systems with heaters that each can either be switched on or off).