This paper presents a two-layer distributed energy resource (DER) coordination architecture that allows for separate ownership of data, operates with data subjected to a large buffering delay, and employs a new measure of power quality. The two-layer architecture comprises a centralized model predictive controller (MPC) and several decentralized MPCs each operating independently with no direct communication between them and with infrequent communication with the centralized controller. The goal is to minimize a combination of total energy cost and a measure of power quality while obeying cyberphysical constraints. The global controller utilizes a fast AC optimal power flow (OPF) solver and extensive parallelization to scale the solution to large networks. Each local controller attempts to maximize arbitrage profit while following the load profile and constraints dictated by the global controller. Extensive simulations are performed for two distribution networks under a wide variety of possible storage and solar penetrations enabled by the controller speed. The simulations show that (i) the two-layer architecture can achieve tenfold improvement in power quality relative to no coordination, while capturing nearly all of the available arbitrage profit for a moderate amount of storage penetration, and (ii) both power quality and arbitrage profits are optimized when the solar and storage are distributed more widely over the network, hence it is more effective to install storage closer to the consumer.

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This paper analyzes two different transportation electrification charging schemes, i.e., an embedded wireless power transfer system and an overhead catenary wire system, for use in range extension of electric vehicles on rural highways. The efficiency, feasibility, and benefits of the two schemes are examined. Electric vehicles currently lack widespread popularity mainly due to battery limitations, especially for long distance travel. The rural highway charging methods presented here can greatly increase the range of electric vehicles while decreasing battery sizes. Average modeling approaches for power electronics and vehicle usage were developed in MATLAB/Simulink to compare the two systems, each at two power levels. 30 kW and 48 kW were chosen to demonstrate the differences between power levels, both capable of maintaining a positive net charge on a dynamic electric vehicle.Component efficiencies, energy transfer levels, and installation percentages for the various models were determined. The models were applied to California highway I-5 to show immense potential savings over gasoline vehicles. It was shown that catenary charging is cheaper and has higher energy transfer than wireless; however, it has difficulty servicing all vehicle types, has visible wires, and requires more maintenance. A small scale hardware prototype of the WPT system was created in order to demonstrate the feasibility of power transfer at the proposed relative distances and speeds.

Distributed devices such as mobile phones can produce and store large amounts of data that can enhance machine learning models; however, this data may contain private information specific to the data owner that prevents the release of the data. We wish to reduce the correlation between user-specific private information and data while maintaining the useful information. Rather than learning a large model to achieve privatization from end to end, we introduce a decoupling of the creation of a latent representation and the privatization of data that allows user-specific privatization to occur in a distributed setting with limited computation and minimal disturbance on the utility of the data. We leverage a Variational Autoencoder (VAE) to create a compact latent representation of the data; however, the VAE remains fixed for all devices and all possible private labels. We then train a small generative filter to perturb the latent representation based on individual preferences regarding the private and utility information. The small filter is trained by utilizing a GAN-type robust optimization that can take place on a distributed device. We conduct experiments on three popular datasets: MNIST, UCI-Adult, and CelebA, and give a thorough evaluation including visualizing the geometry of the latent embeddings and estimating the empirical mutual information to show the effectiveness of our approach.