3,499 research outputs found
Smart Meter Privacy: A Utility-Privacy Framework
End-user privacy in smart meter measurements is a well-known challenge in the
smart grid. The solutions offered thus far have been tied to specific
technologies such as batteries or assumptions on data usage. Existing solutions
have also not quantified the loss of benefit (utility) that results from any
such privacy-preserving approach. Using tools from information theory, a new
framework is presented that abstracts both the privacy and the utility
requirements of smart meter data. This leads to a novel privacy-utility
tradeoff problem with minimal assumptions that is tractable. Specifically for a
stationary Gaussian Markov model of the electricity load, it is shown that the
optimal utility-and-privacy preserving solution requires filtering out
frequency components that are low in power, and this approach appears to
encompass most of the proposed privacy approaches.Comment: Accepted for publication and presentation at the IEEE SmartGridComm.
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Differentially Private State Estimation in Distribution Networks with Smart Meters
State estimation is routinely being performed in high-voltage power
transmission grids in order to assist in operation and to detect faulty
equipment. In low- and medium-voltage power distribution grids, on the other
hand, few real-time measurements are traditionally available, and operation is
often conducted based on predicted and historical data. Today, in many parts of
the world, smart meters have been deployed at many customers, and their
measurements could in principle be shared with the operators in real time to
enable improved state estimation. However, customers may feel reluctance in
doing so due to privacy concerns. We therefore propose state estimation schemes
for a distribution grid model, which ensure differential privacy to the
customers. In particular, the state estimation schemes optimize different
performance criteria, and a trade-off between a lower bound on the estimation
performance versus the customers' differential privacy is derived. The proposed
framework is general enough to be applicable also to other distribution
networks, such as water and gas networks
Privacy-Protecting Energy Management Unit through Model-Distribution Predictive Control
The roll-out of smart meters in electricity networks introduces risks for
consumer privacy due to increased measurement frequency and granularity.
Through various Non-Intrusive Load Monitoring techniques, consumer behavior may
be inferred from their metering data. In this paper, we propose an energy
management method that reduces energy cost and protects privacy through the
minimization of information leakage. The method is based on a Model Predictive
Controller that utilizes energy storage and local generation, and that predicts
the effects of its actions on the statistics of the actual energy consumption
of a consumer and that seen by the grid. Computationally, the method requires
solving a Mixed-Integer Quadratic Program of manageable size whenever new meter
readings are available. We simulate the controller on generated residential
load profiles with different privacy costs in a two-tier time-of-use energy
pricing environment. Results show that information leakage is effectively
reduced at the expense of increased energy cost. The results also show that
with the proposed controller the consumer load profile seen by the grid
resembles a mixture between that obtained with Non-Intrusive Load Leveling and
Lazy Stepping.Comment: Accepted for publication in IEEE Transactions on Smart Grid 2017,
special issue on Distributed Control and Efficient Optimization Methods for
Smart Gri
Differentially Private Convex Optimization with Piecewise Affine Objectives
Differential privacy is a recently proposed notion of privacy that provides
strong privacy guarantees without any assumptions on the adversary. The paper
studies the problem of computing a differentially private solution to convex
optimization problems whose objective function is piecewise affine. Such
problem is motivated by applications in which the affine functions that define
the objective function contain sensitive user information. We propose several
privacy preserving mechanisms and provide analysis on the trade-offs between
optimality and the level of privacy for these mechanisms. Numerical experiments
are also presented to evaluate their performance in practice
A privacy preserving approach to energy theft detection in smart grids
A major challenge for utilities is energy theft, wherein malicious actors steal energy for financial gain. One such form of theft in the smart grid is the fraudulent amplification of energy generation measurements from DERs, such as photo-voltaics. It is important to detect this form of malicious activity, but in a way that ensures the privacy of customers. Not considering privacy aspects could result in a backlash from customers and a heavily curtailed deployment of services, for example. In this short paper, we present a novel privacy-preserving approach to the detection of manipulated DER generation measurements
Profiling Users in the Smart Grid
The implementation of the smart grid brings with it many new components that are fundamentally different to traditional power grid infrastructures. The most important addition brought by the smart grid is the application of the Advanced Metering Infrastructure (AMI). As part of the AMI, the smart meter device provides real time energy usage about the consumer to all of the smart grids stakeholders. Detailed statistics about a consumer’s energy usage can be accessed by the end user, utility companies and other parties. The problem, however, is in how to analyse, present and make best use of the data. This paper focuses on the data collected from the smart grid and how it can be used to detect abnormal user behaviour for energy monitoring applications. The proposed system employs a data classification technique to identify irregular energy usage in patterns generated by smart meters. The results show that it is possible to detect abnormal behaviour with an overall accuracy of 99.45% with 0.100 for sensitivity, 0.989 for specificity and an error of 0.006 using the LDC classifier
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