10,939 research outputs found
Smart Meter Privacy with an Energy Harvesting Device and Instantaneous Power Constraints
A smart meter (SM) periodically measures end-user electricity consumption and
reports it to a utility provider (UP). Despite the advantages of SMs, their use
leads to serious concerns about consumer privacy. In this paper, SM privacy is
studied by considering the presence of an energy harvesting device (EHD) as a
means of masking the user's input load. The user can satisfy part or all of
his/her energy needs from the EHD, and hence, less information can be leaked to
the UP via the SM. The EHD is typically equipped with a rechargeable energy
storage device, i.e., a battery, whose instantaneous energy content limits the
user's capability in covering his/her energy usage. Privacy is measured by the
information leaked about the user's real energy consumption when the UP
observes the energy requested from the grid, which the SM reads and reports to
the UP. The minimum information leakage rate is characterized as a computable
information theoretic single-letter expression when the EHD battery capacity is
either infinite or zero. Numerical results are presented for a discrete binary
input load to illustrate the potential privacy gains from the existence of a
storage device.Comment: To be published in IEEE ICC201
Integration of Legacy Appliances into Home Energy Management Systems
The progressive installation of renewable energy sources requires the
coordination of energy consuming devices. At consumer level, this coordination
can be done by a home energy management system (HEMS). Interoperability issues
need to be solved among smart appliances as well as between smart and
non-smart, i.e., legacy devices. We expect current standardization efforts to
soon provide technologies to design smart appliances in order to cope with the
current interoperability issues. Nevertheless, common electrical devices affect
energy consumption significantly and therefore deserve consideration within
energy management applications. This paper discusses the integration of smart
and legacy devices into a generic system architecture and, subsequently,
elaborates the requirements and components which are necessary to realize such
an architecture including an application of load detection for the
identification of running loads and their integration into existing HEM
systems. We assess the feasibility of such an approach with a case study based
on a measurement campaign on real households. We show how the information of
detected appliances can be extracted in order to create device profiles
allowing for their integration and management within a HEMS
A Lightweight Privacy-Preserved Spatial and Temporal Aggregation of Energy Data
Smart grid provides fine-grained real time energy consumption, and it is able to improve the efficiency of energy management. It enables the collection of energy consumption data from consumer and hence has raised serious privacy concerns. Energy consumption data, a form of personal information that reveals behavioral patterns can be used to identify electrical appliances being used by the user through the electricity load signature, thus making it possible to further reveal the residency pattern of a consumer’s household or appliances usage habit. This paper proposes to enhance the privacy of energy con- sumption data by enabling the utility to retrieve the aggregated spatial and temporal consumption without revealing individual energy consumption. We use a lightweight cryptographic mech- anism to mask the energy consumption data by adding random noises to each energy reading and use Paillier’s additive homo- morphic encryption to protect the noises. When summing up the masked energy consumption data for both Spatial and Temporal aggregation, the noises cancel out each other, hence resulting in either the total sum of energy consumed in a neighbourhood at a particular time, or the total sum of energy consumed by a household in a day. No third party is able to derive the energy consumption pattern of a household in real time. A proof-of- concept was implemented to demonstrate the feasibility of the system, and the results show that the system can be efficiently deployed on a low-cost computing platform
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