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We target the problem of controlling electrical microgrids with littleinertia in real time. We consider a central controller and a number ofresources, where each resource is either a load, a generator, or a combinationthereof, like a battery. The controller periodically computes power setpointsfor the resources based on the estimated state of the grid and an overallobjective, and subject to safety constraints. Each resource is augmented with aresource agent that a) implements the setpoint requests sent by the controlleron the resource, and b) translates device-specific information about theresource into a device-independent representation and transmits this to thecontroller.  We focus on the resource agents and their impact on the overall system'sbehavior. Intuitively, for the system to converge to the objective, theresource agents should be obedient to the requests from the controller, in thesense that the actually implemented setpoint should be close to the requestedsetpoint, at least on average. This can be important especially when acontroller that performs continuous optimization is used (for the sake ofperformance) to control discrete resources (which have a discrete set ofimplementable setpoints).  We formalize obedience by defining the notion of $c$-boundedaccumulated-error. We then demonstrate its usefulness, by presentingtheoretical results (for a simple scenario) and simulation results (for a morerealistic setting) that indicate that, if all resource agents in the systemhave bounded accumulated-error, the closed-loop system converges on average tothe objective. Finally, we show how to design resource agents that provablyhave bounded accumulated-error for various types of resources, such asresources with uncertainty (e.g., PV panels) and resources with a discrete setof implementable setpoints (e.g., on-off heating systems).

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