Smart Inverter Grid Probing for Learning Loads: Part I — Identifiability Analysis

Abstract

Distribution grids currently lack comprehensive real-time metering. Nevertheless, grid operators require precise knowledge of loads and renewable generation to accomplish any feeder optimization task. At the same time, new grid technologies, such as solar photovoltaics and energy storage units, are interfaced via inverters with advanced sensing and actuation capabilities. In this context, this two-part work puts forth the idea of engaging power electronics to probe an electric grid and record its voltage response at actuated and metered buses, to infer nonmetered loads. Probing can be accomplished by commanding inverters to momentarily perturb their power injections. Multiple probing actions can be induced within a few tens of seconds. In Part I, load inference via grid probing is formulated as an implicit nonlinear system identification task, which is shown to be topologically observable under certain conditions. The conditions can be readily checked upon solving a max-flow problem on a bipartite graph derived from the feeder topology and the placement of probed and nonmetered buses. The analysis holds for single- and multiphase grids, radial or meshed, and applies to phasor or magnitude-only voltage data. Using probing to learn nonconstant-power loads is also analyzed as a special case.