Gregory S. Ledva

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Demand response can provide services to the power network; however, coordination of spatially distributed demand response resources generally requires coping with imperfect communication networks. This work investigates methods to manage communication constraints (e.g., delays and bandwidth limitations), faced by demand response aggregators who manipulate(More)
In this paper, we apply an emerging method, online learning with dynamics, to deduce properties of distributed energy resources (DERs) from coarse measurements, e.g., measurements taken at distribution substations, rather than household-level measurements. Reduced sensing requirements can lower infrastructure costs associated with reliably incorporating(More)
Residential thermostatically controlled loads (TCLs), such as refrigerators and air conditioners, can be used for fast time-scale demand response to manage energy imbalances in power systems. Many direct load control strategies rely on information exchange between a central controller and loads in realtime. This project investigates the effects of limited(More)
This paper presents a novel approach to find patterns in vehicle x-y-z acceleration data for use in prognostics and diagnostics. In this problem, vehicles are assumed to travel on the same routes and often times as a part of convoys but their GPS and other position information has been removed for privacy reasons. The goal of the pattern matching scheme is(More)
Though distribution system operators have been adding more sensors to their networks, they still often lack an accurate real-time picture of the behavior of distributed energy resources such as demand responsive electric loads and residential solar generation. Such information could improve system reliability, economic efficiency, and environmental impact.(More)
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