TY - GEN
T1 - Model-based assessment for balancing privacy requirements and operational capabilities in the smart grid
AU - Knirsch, F.
AU - Engel, D.
AU - Frincu, M.
AU - Prasanna, V.
N1 - Conference code: 113061
Cited By :14
Export Date: 14 December 2023
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PY - 2015
Y1 - 2015
N2 - The smart grid changes the way energy is produced and distributed. In addition both, energy and information is exchanged bidirectionally among participating parties. Therefore heterogeneous systems have to cooperate effectively in order to achieve a common high-level use case, such as smart metering for billing or demand response for load curtailment. Furthermore, a substantial amount of personal data is often needed for achieving that goal. Capturing and processing personal data in the smart grid increases customer concerns about privacy and in addition, certain statutory and operational requirements regarding privacy aware data processing and storage have to be met. An increase of privacy constraints, however, often limits the operational capabilities of the system. In this paper, we present an approach that automates the process of finding an optimal balance between privacy requirements and operational requirements in a smart grid use case and application scenario. This is achieved by formally describing use cases in an abstract model and by finding an algorithm that determines the optimum balance by forward mapping privacy and operational impacts. For this optimal balancing algorithm both, a numeric approximation and-if feasible-an analytic assessment are presented and investigated. The system is evaluated by applying the tool to a real-world use case from the University of Southern California (USC) microgrid. © 2015 IEEE.
AB - The smart grid changes the way energy is produced and distributed. In addition both, energy and information is exchanged bidirectionally among participating parties. Therefore heterogeneous systems have to cooperate effectively in order to achieve a common high-level use case, such as smart metering for billing or demand response for load curtailment. Furthermore, a substantial amount of personal data is often needed for achieving that goal. Capturing and processing personal data in the smart grid increases customer concerns about privacy and in addition, certain statutory and operational requirements regarding privacy aware data processing and storage have to be met. An increase of privacy constraints, however, often limits the operational capabilities of the system. In this paper, we present an approach that automates the process of finding an optimal balance between privacy requirements and operational requirements in a smart grid use case and application scenario. This is achieved by formally describing use cases in an abstract model and by finding an algorithm that determines the optimum balance by forward mapping privacy and operational impacts. For this optimal balancing algorithm both, a numeric approximation and-if feasible-an analytic assessment are presented and investigated. The system is evaluated by applying the tool to a real-world use case from the University of Southern California (USC) microgrid. © 2015 IEEE.
KW - Approximation algorithms
KW - Data privacy
KW - Digital storage
KW - Electric power transmission networks
KW - Microgrids
KW - Balancing algorithms
KW - Energy and information
KW - Heterogeneous systems
KW - High level use-cases
KW - Operational capabilities
KW - Operational requirements
KW - Privacy requirements
KW - University of Southern California
KW - Smart power grids
U2 - 10.1109/ISGT.2015.7131805
DO - 10.1109/ISGT.2015.7131805
M3 - Conference contribution
BT - 2015 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2015 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2015
Y2 - 18 February 2015 through 20 February 2015
ER -
