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Author:Liassas, Georgios
Title:Privacy Enhancing Mechanisms in the Smart Grid
Publication type:Master's thesis
Publication year:2013
Pages:70      Language:   eng
Department/School:Perustieteiden korkeakoulu
Main subject:Tietokoneverkot   (T-110)
Supervisor:Aura, Tuomas ; Probst, Christian W.
Instructor:Vasirani, Matteo ; Wijaya, Kurniawan
Electronic version URL: http://urn.fi/URN:NBN:fi:aalto-201409252675
OEVS:
Electronic archive copy is available via Aalto Thesis Database.
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Location:P1 Ark Aalto  2422   | Archive
Keywords:smart grid
demand response
privacy
dynamic pricing
incentives
dirential privacy
aggregation
utility
Paillier
homomorphic
PET
Abstract (eng): The Smart Grid constitutes a hot research topic, nowadays, due to the potential that is has to further improve and optimize the power generation, delivery and consumption.
The set of components it is comprised of, such as the smart meters, as well as the advanced communication technologies it incorporates, renders it capable of bringing significant societal benefits and high reliability in reference with its orderly operation.

An interesting technology in the context of the Smart Grid is the Demand Response.
This technology attempts to change the way that the electricity customers used to perceive the power consumption by engaging them in an interaction with the energy producer.
Essentially, the customers are asked to adapt their power needs based on the state of the power grid.
In that way, the energy capacity or resources could be shared more efficiently and unpleasant incidents, such as power outages, could be prevented.
In return, the utility company offers monetary incentives, rewarding in this way the customers' power curtailment efforts.

Nevertheless, the fulfillment of the DR goals requires the exchange of information between the utility company and the customers.
From the customers' point of view this interaction might be privacy invasive.
Consequently, DR programs could not be widely accepted by the public before the privacy concerns are alleviated.
This thesis investigates the trade off between the privacy and the efficiency of a DR mechanism by simulating the stakeholders and their interactions in a mock DR environment.
ED:2014-09-16
INSSI record number: 49705
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