On Identification of Distribution Grids
This work addresses the need for accurate distribution grid models to control distributed energy resources, which is critical for grid operators and utilities, though it is incremental as it builds on existing lasso methods for a specific domain.
The paper tackles the problem of identifying distribution grid models, which are often unavailable or outdated, by jointly estimating model parameters and operational structure from synchrophasor measurements using lasso-based convex programs. The result is validated through power flow studies on four three-phase radial distribution systems with real household demands, showing efficacy in event detection and localization.
Large-scale integration of distributed energy resources into residential distribution feeders necessitates careful control of their operation through power flow analysis. While the knowledge of the distribution system model is crucial for this type of analysis, it is often unavailable or outdated. The recent introduction of synchrophasor technology in low-voltage distribution grids has created an unprecedented opportunity to learn this model from high-precision, time-synchronized measurements of voltage and current phasors at various locations. This paper focuses on joint estimation of model parameters (admittance values) and operational structure of a poly-phase distribution network from the available telemetry data via the lasso, a method for regression shrinkage and selection. We propose tractable convex programs capable of tackling the low rank structure of the distribution system and develop an online algorithm for early detection and localization of critical events that induce a change in the admittance matrix. The efficacy of these techniques is corroborated through power flow studies on four three-phase radial distribution systems serving real household demands.