Dynamic forecasting and adaptation for demand optimization in the smart grid
conference contribution
posted on 2012-10-05, 08:08 authored by Eamonn O'Toole, Siobhán ClarkeThe daily peaks and valleys in energy demand create
inefficiencies and expense in the operation of the electricity
grid. Valley periods force utilities to curtail renewable energy
sources such as wind as their unpredictable nature makes it
difficult to maintain line frequency across the network within
target bounds. Peak periods require additional generators
that remain dormant during other periods. Smoothing this
demand cycle is one of the fundamental challenges of the
Smart Grid, requiring flexibility and coordination between
actors throughout the Grid. This paper describes the Smart
Grid as a multi-layered system and proposes a cross-layered
dynamic adaptation approach to facilitate this flexibility and
coordination. This method uses a hierarchical taxonomy to
identify appropriate adaptation actions in response to identified
mismatches, supported by a run-time predictive statistical
framework to predict mismatches, enabling timely adaptations
to be triggered.
History
Publication
Software Engineering for the Smart Grid (SE4SG), 2012 International Workshop;pp. 30-33Publisher
IEEE Computer SocietyNote
peer-reviewedOther Funding information
SFIRights
“© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”Language
EnglishExternal identifier
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