Smart Inverter Grid Probing for Learning Loads: Part I - Identifiability Analysis
Siddharth Bhela, Vassilis Kekatos, Sriharsha Veeramachaneni

TL;DR
This paper proposes a method for inferring non-metered loads in distribution grids by actively probing the system with smart inverters and analyzing the voltage response, ensuring system observability under certain conditions.
Contribution
It formulates load inference as a nonlinear system identification problem and provides a topological observability condition based on a max-flow analysis, applicable to various grid configurations.
Findings
Load inference is topologically observable under certain conditions.
The observability condition can be checked via a max-flow problem.
The approach applies to single- and multi-phase, radial, and meshed grids.
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 non-metered 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…
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Taxonomy
TopicsPower System Optimization and Stability · Optimal Power Flow Distribution · Smart Grid Energy Management
