Distribution System Topology Detection Using Consumer Load and Line Flow Measurements

This work presents a topology detection method combining home smart meter information and sparse line flow measurements. The problem is formulated as a spanning tree detection problem over a graph given partial nodal and edge flow information in a deterministic and stochastic setting. In the deterministic case of known nodal power consumption and edge flows we provide sensor placement criterion which guarantees correct identification of all spanning trees. We then present a detection method which is polynomial in complexity to the size of the graph. In the stochastic case where loads are given by forecasts derived from delayed smart meter data, we provide a combinatorial Maximum a Posteriori (MAP) detector and a polynomial complexity approximate MAP detector which is shown to work near optimum in low noise regime numerical cases and moderately well in higher noise regime

VADER: Visualization and Analytics for Distributed Energy Resources

Enabling deep penetration of distributed energy resources (DERs) requires comprehensive monitoring and control of the distribution network. Increasing observability beyond the substation and extending it to the edge of the grid is required to achieve this goal. The growing availability of data from measurements from inverters, smart meters, EV chargers, smart thermostats and other devices provides an opportunity to address this problem. Integration of these new data poses many challenges since not all devices are connected to the traditional supervisory control and data acquisition (SCADA) networks and can be novel types of information, collected at various sampling rates and with potentially missing values. Visualization and analytics for distributed energy resources (VADER) system and workflow is introduced as an approach and platform to fuse these different streams of data from utilities and third parties to enable comprehensive situational awareness, including scenario analysis and system state estimation. The system leverages modern large scale computing platforms, machine learning and data analytics and can be used alongside traditional advanced distribution management system (ADMS) systems to provide improved insights for distribution system management in the presence of DERs