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ADVANTAGE Research Projects

Project 1: Providing users with rich feedback information to the user, consisting of detailed demand behavioural patterns and forecasting – not just simple reporting the current and past energy consumption.
Project 2: Tackling the practical issue of forecasting and planning of user consumption both at individual and aggregated level based on the limited set of available measurements.
Project 3: Design of smart grid networking solutions that take into account M2M traffic constraints.
Project 4: Use of advanced signal processing techniques in order to the enhance PLC for smart grid use.
Project 5: Exploring the design space of communications and signal processing solutions in order to create effective load clustering solutions.
Project 6: Using process information and analytic methods at low levels of granularity (at individual load/generator level) in order to create flexible and adaptive smart demand side response system.
Project 7: Research and design of novel control algorithms needed for the effective integration of distributed generators.
Project 8: “Soft” (upper layer) enhancement of the existing wireless standards in order to obtain required reliability performance, while leaving the physical interface unchanged.
Project 9: Design of bus signalling solutions that which exploit only existing power lines and communication capabilities of standard micro grid elements.
Project 10: Design of real-time state estimation algorithms that take into account substantially enlarged measurement set.
Project 11: Augmentation of an actual, operating distributed management solution by incorporation of communication technologies and interfaces envisaged for future smart grids.
Project 12: Design of a load clustering solution for demand side management optimization that considers a substantially enlarged base (i.e., close to 100%) of subscribed users, as opposed to the current research studies that consider just a limited end-user subscription.
Project 13: Efficient data gathering, storing and processing of exceptionally huge amounts of smart metering data, and assessing its value

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