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

TL;DR

This paper introduces a joint recovery framework that improves the real-time observability of distributed energy resources at the grid edge by combining smart meter data with limited phasor measurements, leveraging load sparsity and locational PV patterns.

Contribution

It presents a convex load recovery method that integrates heterogeneous measurements and exploits load and generation characteristics for enhanced grid-edge visibility.

Findings

Effective in identifying EV charging events.

Accurately infers behind-the-meter PV output.

Reduces computational complexity of load recovery.

Abstract

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.