Giusti, Alessandro and Salani, Matteo and Di Caro, Gianni and Rizzoli, Andrea Emilio and Gambardella, Luca Maria (2014) Restricted neighborhood communication improves decentralized demand-side Load management. IEEE Transactions on Smart Grids, 5 (1). pp. 84-91. ISSN 1949-3053
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Abstract
We address demand-side management of dispatchable loads in a residential microgrid by means of decentralized controllers deployed in each household. Controllers simultaneously optimize two possibly conflicting objectives: minimization of energy costs for the end user (considering a known, time-dependent tariff) and stabilization of the aggregate load profile (load flattening). The former objective can be optimized independently by each controller. On the other hand, the latter could benefit from a communication infrastructure that allows the controllers to explicitly exchange information and coordinate. To study how different levels of communication pervasiveness affect system performance, we developed a realistic microsimulation environment accounting for the behavior of residents, dispatchable and non-dispatchable household loads, and the effects on the distribution network. We considered a generic model of communication among household controllers, not tied to any specific technology, and based on the partitioning of the households in a number of groups (neighborhoods). Controllers within the same neighborhood enjoy full connectivity, but cannot interact with controllers outside of their neighborhood. Through extensive simulation experiments, we observed that even communication neighborhoods constituted by as few as 3-4 households are sufficient to effectively stabilize the aggregate network load profile, with minimal bandwidth consumption. Increasing the neighborhood size leads to comparatively negligible performance improvements. We conclude that effective load flattening can be achieved with minimal requirements of communication infrastructure and transmitted information.
Item Type: | Scientific journal article, Newspaper article or Magazine article |
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Subjects: | Engineering > Electronic & electrical engineering > Electrical power Engineering > Electronic & electrical engineering > Control systems Computer sciences > Artificial intelligence > Machine learning |
Department/unit: | Dipartimento tecnologie innovative > Istituto Dalle Molle di studi sull’intelligenza artificiale USI-SUPSI |
Depositing User: | Andrea Emilio Rizzoli |
Date Deposited: | 27 Feb 2014 13:55 |
Last Modified: | 23 May 2016 11:41 |
URI: | http://repository.supsi.ch/id/eprint/3736 |
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