# Enhancing the Spatio-temporal Observability of Grid-Edge Resources in Distribution Grids

arXiv: https://arxiv.org/abs/2102.07801 · HTML: https://mlsanity.com/paper/2102.07801/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 43 references that are in the map.
- First posted on arXiv 15 Feb 2021.

## Builds on (most cited first, 8 of 8)

- [Load-Flow in Multiphase Distribution Networks: Existence, Uniqueness, Non-Singularity and Linear Models](https://mlsanity.com/paper/1702.03310/) (2017)
- [Matrix Completion for Low-Observability Voltage Estimation](https://mlsanity.com/paper/1801.09799/) (2018)
- [Convergence of the Z-Bus Method for Three-Phase Distribution Load-Flow with ZIP Loads](https://mlsanity.com/paper/1605.08511/) (2016)
- [State Estimation in Power Distribution Systems Based on Ensemble Kalman Filtering](https://mlsanity.com/paper/1712.01317/) (2017)
- [Enhancing Observability in Distribution Grids using Smart Meter Data](https://mlsanity.com/paper/1612.06669/) (2016)
- [Dynamic Distribution State Estimation Using Synchrophasor Data](https://mlsanity.com/paper/1901.02554/) (2019)
- [Secure Integration of Electric Vehicles with the Power Grid](https://mlsanity.com/paper/1905.01035/) (2019)
- [Enhancing the Spatio-Temporal Observability of Residential Loads](https://mlsanity.com/paper/1910.00984/) (2019)

## Built on by (most cited first, 1 of 1)

- [Data-driven Modeling for Distribution Grids Under Partial Observability](https://mlsanity.com/paper/2108.08350/) (2021)

## Abstract (arXiv)

Enhancing the spatio-temporal observability of distributed energy resources (DERs) is crucial for achieving secure and efficient operations in distribution grids. This paper puts forth a joint recovery framework for residential loads by leveraging the complimentary strengths of heterogeneous measurements in real time. The proposed framework integrates low-resolution smart meter data collected at every load node with fast-sampled feeder-level measurements from limited number of distribution phasor measurement units. To address the lack of data, we exploit two key characteristics for the loads and DERs, namely the sparse changes due to infrequent activities of appliances and electric vehicles (EVs) and the locational dependence of solar photovoltaic (PV) generation. Accordingly, meaningful regularization terms are introduced to cast a convex load recovery problem, which will be further simplified to reduce the computational complexity. The load recovery solutions can be utilized to identify the EV charging events at each load node and to infer the total behind-the-meter PV output. Numerical tests using real-world data have demonstrated the effectiveness of the proposed approaches in enhancing the visibility of grid-edge DERs.