December 22, 2016
Power System Resilience to Natural Disasters
Recent events, such as Superstorm Sandy, have highlighted the need to improve the resilience of the electricity grid. Rather than focusing on individual components, this project considers the resilience of the overall system. It will also develop techniques to assess the relative value of measures aimed at hardening various components or facilitating the repair and…
Installation of PV panels, smart inverters and batteries on the UW campus
Besides installing a significant amount of PV panels, we are equipping them with smart inverters that will help control the voltage on the UW campus. We are also installing a battery energy storage system and testing algorithms for controlling all of these devices.
Profitability of Energy Storage in a Competitive Environment
This project investigates techniques to assess the profitability of deploying distributed energy storage in competitive electricity markets. After reviewing the rules that have been implemented for the integration of storage, we are developing optimization models of increasing accuracy and complexity to determine the optimal location and size of batteries.
Shared Use of Energy Storage Devices
Because the cost of battery energy storage systems is still high, justifying their installation requires multiple revenue streams. This project investigates how a battery installed by a distribution utility can be used not only to assist in the operation of the distribution network but also to reduce congestion in the transmission network operated by another…
Energy Positioning: Control and Economics
Distributed storage makes possible the positioning of energy at strategic locations in the transmission network. This storage capacity can be used to relieve congestion (spatio-temporal arbitrage) as well as for real-time system balancing and post-contingency corrective actions in power systems with a substantial proportion of renewable generation.
November 15, 2016
On-chip single photon detectors for hybrid photonic quantum networks
Semiconductor-diamond nanophotonic transmitter for long-distance communication
September 23, 2016
Neuromorphic Computing
Neuronal networks are capable of processing specific data and tasks optimally and in ‘real time’. Many of these problems are computationally expensive to solve with current computing systems. We thereby develop algorithms and architectures inspired from the design principles of neurobiological networks to solve these problems more efficiently. Example project: Continuous training of recurrent networks.
Predictive Computational Modeling of Neuronal Networks
Neuronal networks are capable to fuse sensory information into activity, which encodes particular behaviors. Some of these behaviors are unique and robust, e.g., locomotion or directional flight. We thereby study how neural circuits that facilitate sensory are designed by modeling their networks and investigate the building blocks, robustness, optimality and controllability of these systems. Example…
Network architecture from data
We introduce methods for inference of black-box networks’ wiring (with unknown connectivity map). Our tools link between reduction of time-series and reduction of models to produce optimization routines for connectivity calibration. We have recently inferred a prototype of the antennal lobe, primary olfactory processing unit in insects, from multi-neuron recordings, and are currently working on…
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