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

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 types of measurements. The proposed approaches integrate the lowresolution smart meter data collected for every load node with the fast-sampled feeder-level measurements provided by limited number of 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 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 these grid-edge DERs.

Multi-Area Distribution System State Estimation via Distributed Tensor Completion

“…) < ∞, which implies that (16a) holds for i = 1, • • • , N . And for any i = 1, • • • , N, j ∈ N (i), by(14) we directly have (16b) hold.…”

“…The authors of [8] presented a data-driven, learning-based neural network that can accommodate several types of measurements as well as pseudo-measurements. Also, [9] proposed a novel neural network model that uses the physical structure of distribution power systems; [10] leveraged a deep neural network by incorporating physical information of the grid topology and line/shunt admittance; [11]- [13] proposed the constrained matrix completion method by combining the conventional matrix completion model with the power flow constraints; and [14] developed a spatio-temporal learning approach to enhance the observability of DERs; however, these machine learning methods require pseudo-measurements or accurate knowledge of the network model (topology of the grid or the bus admittance matrix). Unfortunately, pseudo-measurements [15] can introduce large estimation errors [16], and accurate distribution system topology is difficult to obtain because of frequent distribution grid reconfigurations and insufficient knowledge about the status of the network [17], [18].…”

Data-driven Modeling for Distribution Grids Under Partial Observability

“…Unlike transmission-level loads, residential loads are known to have sudden changes due to large appliance activities or other DERs; see e.g., [11]. These changes are mostly infrequent, but can cause large variations of load profiles.…”

“…Recognizing this issue, [7], [8] simply neglects the unobserved nodes by assuming their power injections are slowly varying. Nonetheless, this assumption fails to hold for residential customer loads which consist of sudden, large changes due to the household activities; see e.g., [11]. Other approaches [12]- [15] address the partial observability issue by using the second-order statistics of nodal voltage and injection, which do not contain the dynamics from fast data samples.…”