This paper describes high-level findings from an innovative network of high-precision phasor measurement units (PMUs), or micro-PMUs (μPMUs), designed to provide an unprecedented level of visibility for power distribution systems. We present capabilities of the technology developed in the course of a three-year ARPA-E funded project, along with challenges and lessons learned through field deployments in collaboration with multiple electric utilities. Beyond specific applications and use cases for μPMU data studied in the context of this project, this paper discusses a broader range of diagnostic applications that appear promising for future work, especially in the presence of high penetrations of variable distributed energy resources.

Effective monitoring and management applications on modern distribution networks (DNs) require a sound network model and the knowledge of line parameters. Network line impedances are used, among other things, for state estimation and protection relay setting. Phasor measurement units (PMUs) give synchronized voltage and current phasor measurements, referred to a common time reference (coordinated universal time). All synchrophasor measurements can thus be temporally aligned and coordinated across the network. This feature, along with high accuracy and reporting rates, could make PMUs useful for the evaluation of network parameters. However, instrument transformer behavior strongly affects the parameter estimation accuracy. In this paper, a new PMU-based iterative line parameter estimation algorithm for DNs, which includes in the estimation model systematic measurement errors, is presented. This method exploits the simultaneous measurements given by PMUs on different nodes and branches of the network. A complete analysis of uncertainty sources is also performed, allowing the evaluation of estimation uncertainty. Issues related to operating conditions, topology, and measurement uncertainty are thoroughly discussed and referenced to a realistic model of a DN to show how a full network estimator is possible.

Large-scale integration of distributed energy resources into distribution feeders necessitates careful control of their operation through power flow analysis. While the knowledge of the distribution system model is crucial for this analysis, it is often unavailable or outdated. The recent introduction of synchrophasor technology in low-voltage distribution grids has created ample 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 admittance parameters and topology of a polyphase 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 and synthetic household demands.

We present a method for joint phase identification and topology recovery in unbalanced three phase radial networks using only voltage measurements. By recovering phases and topology jointly, we utilize all three phase voltage measurements and can handle networks where some buses have a subset of three phases. Our method is theoretically justified by a novel linearized formulation of unbalanced three phase power flow and makes precisely defined and reasonable assumptions on line impedances and load statistics. We validate our method on three IEEE test networks simulated under realistic conditions in OpenDSS, comparing our performance to the state of the art. In addition to providing a new method for phase and topology recovery, our intuitively structured linearized model will provide a foundation for future work in this and other applications.

Increasing levels of distributed solar photovoltaic (PV) generation has created significant technical challenges in distribution networks, particularly regarding voltage regulation. Small-scale behind-the-meter battery storage is a key element in effectively overcoming such challenges, while offering financial benefits for customers. In this paper, we propose an optimization-based approach for dispatching power from residential-scale battery storage for grid support and customer savings, considering only local (behind-the-meter) measurements. The proposed approach takes into account the physics of the grid and both real and reactive power (coupled) for voltage regulation and grid loss reduction. The optimization-based approach, termed Local-Quadratic Program (L-QP), is formulated based on the linear power flow equations (LinDistFlow). The proposed L-QP is benchmarked against an approach in which real power is used to maximize the customer benefit, while reactive power is dedicated for voltage regulation. Numerical simulations with the IEEE 13 and IEEE 123 Node Test Feeders, which include realistic time-varying data for residential load and PV generation, demonstrate the technical advantages of the proposed approach.

The knowledge of line parameters is fundamental for building a network model that can become the basis for all the monitoring and management applications. Network line impedances are employed, for instance, for the state estimation or for protection applications in relay setting. The Phasor Measurement Units (PMUs), thanks to the synchronized voltage and current phasor measurements, provide the accurate information that can be used to evaluate the network parameters of the monitored branches. Nevertheless, the impact of instrument transformers (ITs) makes the parameter estimation a hard task. For this reason, in this paper an estimation paradigm is proposed that includes the estimation of both systematic errors (mainly due to ITs) and line parameters. The proposed method, particularly focused on distribution systems, is designed to perform simultaneous estimations across different branches of the network, aiming at exploiting the links between the involved parameters, due to topology and operating conditions. The estimation is based on a sound and detailed model of the uncertainty sources.