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RESEARCH PRODUCT

Overgrid: A Fully Distributed Demand Response Architecture Based on Overlay Networks

Ilenia TinnirelloFabrizio GiulianoAlessandra GalatiotoMarco BeccaliGaetano ZizzoDaniele CroceMarina Bonomolo

subject

EngineeringSettore ING-IND/11 - Fisica Tecnica AmbientaleSettore ING-INF/03 - Telecomunicazionibusiness.industry020209 energyDistributed computingOverlay network02 engineering and technologyEnergy consumptionNetwork topologySettore ING-IND/33 - Sistemi Elettrici Per L'EnergiaDemand responseControl and Systems EngineeringDistributed algorithm0202 electrical engineering electronic engineering information engineeringMicrogridElectrical and Electronic EngineeringbusinessReference modelBuilding automationComputer network

description

In this paper, we present Overgrid, a fully distributed peer-to-peer (P2P) architecture designed to automatically control and implement distributed demand response (DR) schemes in a community of smart buildings with energy generation and storage capabilities. As overlay networks in communications establish logical links between peers regardless of the physical topology of the network, the Overgrid is able to apply some power balance criteria to its system of buildings, as they belong to a virtual microgrid, regardless of their physical location. We exploit an innovative distributed algorithm, called flow updating, for monitoring the power consumption of the buildings and the number of nodes in the network, proving its applicability in an Overgrid scenario with realistic power profiles and networks of up to 10,000 buildings. To quantify the energy balance capability of Overgrid, we first study the energy characteristics of several types of buildings in our university campus and in an industrial site to accurately provide some reference buildings models. Then, we classify the amount of ``flexible'' energy consumption, i.e., the quota that could be potentially exploited for DR programs. Finally, we validate Overgrid emulating a real P2P network of smart buildings behaving according to our reference models. The experimental results prove the feasibility of our approach.

https://doi.org/10.1109/tase.2016.2621890