TY - GEN
T1 - A game theory model for electricity theft detection and privacy-aware control in AMI systems
AU - Cardenas, Alvaro A.
AU - Amin, Saurabh
AU - Schwartz, Galina
AU - Dong, Roy
AU - Sastry, Shankar
N1 - Copyright:
Copyright 2013 Elsevier B.V., All rights reserved.
PY - 2012
Y1 - 2012
N2 - We introduce a model for the operational costs of an electric distribution utility. The model focuses on two of the new services that are enabled by the Advanced Metering Infrastructure (AMI): (1) the fine-grained anomaly detection that is possible thanks to the frequent smart meter sampling rates (e.g., 15 minute sampling intervals of some smart meter deployments versus monthly-readings from old meters), and (2) the ability to shape the load thanks to advanced demand-response mechanisms that leverage AMI networks, such as direct-load control. We then study two security problems in this context. (1) In the first part of the paper we formulate the problem of electricity theft detection (one of the use-cases of anomaly detection) as a game between the electric utility and the electricity thief. The goal of the electricity thief is to steal a predefined amount of electricity while minimizing the likelihood of being detected, while the electric utility wants to maximize the probability of detection and the degree of operational cost it will incur for managing this anomaly detection mechanism. (2) In the second part of the paper we formulate the problem of privacy-preserving demand response as a control theory problem, and show how to select the maximum sampling interval for smart meters in order to protect the privacy of consumers while maintaining the desired load shaping properties of demand-response programs.
AB - We introduce a model for the operational costs of an electric distribution utility. The model focuses on two of the new services that are enabled by the Advanced Metering Infrastructure (AMI): (1) the fine-grained anomaly detection that is possible thanks to the frequent smart meter sampling rates (e.g., 15 minute sampling intervals of some smart meter deployments versus monthly-readings from old meters), and (2) the ability to shape the load thanks to advanced demand-response mechanisms that leverage AMI networks, such as direct-load control. We then study two security problems in this context. (1) In the first part of the paper we formulate the problem of electricity theft detection (one of the use-cases of anomaly detection) as a game between the electric utility and the electricity thief. The goal of the electricity thief is to steal a predefined amount of electricity while minimizing the likelihood of being detected, while the electric utility wants to maximize the probability of detection and the degree of operational cost it will incur for managing this anomaly detection mechanism. (2) In the second part of the paper we formulate the problem of privacy-preserving demand response as a control theory problem, and show how to select the maximum sampling interval for smart meters in order to protect the privacy of consumers while maintaining the desired load shaping properties of demand-response programs.
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U2 - 10.1109/Allerton.2012.6483444
DO - 10.1109/Allerton.2012.6483444
M3 - Conference contribution
AN - SCOPUS:84875731397
SN - 9781467345385
T3 - 2012 50th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2012
SP - 1830
EP - 1837
BT - 2012 50th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2012
T2 - 2012 50th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2012
Y2 - 1 October 2012 through 5 October 2012
ER -