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Electric distribution systems around the world are seeing an increasing number of utility-owned and non-utility-owned (customer-owned) intelligent devices and systems being deployed. New deployments of utility-owned assets include self-healing systems, microgrids, and distribution automation. Non-utilityowned assets include solar photovoltaic generation, behind-the-meter energy storage systems, and electric vehicles. While these deployments provide potential data and control points, the existing centralized control architectures do not have the flexibility or the scalability to integrate the increasing number or variety of devices. The communication bandwidth, latency, and the scalability of a centralized control architecture limit the ability of these new devices and systems from being engaged as active resources. This paper presents a standards-based architecture for the distributed power system controls, which increases operational flexibility by coordinating centralized and distributed control systems. The system actively engages utility and non-utility assets using a distributed architecture to increase reliability during normal operations and resiliency during extreme events. Results from laboratory testing and preliminary field implementations, as well as the details of an ongoing full-scale implementation at Duke Energy, are presented. INDEX TERMS Distributed control, microgrids, power distribution, power system protection, smart grids.

This report addresses the potential use of phasor measurement units (PMUs) within electricity distribution systems, and was written to assess whether or not PMUs could provide significant benefit, at the national level. We analyze examples of present and emerging distribution-system issues related to reliability, integration of distributed energy resources, and the changing electrical characteristics of load. We find that PMUs offer important and irreplaceable advantages over present approaches. However, we also find that additional research and development for standards, testing and calibration, demonstration projects, and information sharing is needed to help industry capture these benefits.Scoping Study on Research and Development Needs for Distribution-System Phasor Measurement Units │iii

Computer and communication network attacks are commonly orchestrated through Wireless Access Points (WAPs). This paper summarizes proof-of-concept research activity aimed at developing a physical layer Radio Frequency (RF) air monitoring capability to limit unauthorized WAP access and improve network security. This is done using Differential Evolution (DE) to optimize the performance of a "Learning from Signals" (LFS) classifier implemented with RF "Distinct Native Attribute"(RF-DNA) fingerprints. Performance of the resultant DE-optimized LFS classifier is demonstrated using 802.11a WiFi devices under the most challenging conditions of intra-manufacturer classification, i.e., using emissions of like-model devices that only differ in serial number. Using identical classifier input features, performance of the DE-optimized LFS classifier is assessed relative to a Multiple Discriminant Analysis / Maximum Likelihood (MDA/ML) classifier that has been used for previous demonstrations. The comparative assessment is made using both Time Domain (TD) and Spectral Domain (SD) fingerprint features. For all combinations of classifier type, feature type, and signal-to-noise ratio considered, results show that the DEoptimized LFS classifier with TD features is superior and provides up to 20% improvement in classification accuracy with proper selection of DE parameters. Track: Real world applications.