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Optimal Power Flow (OPF) is a crucial part of the Energy Management System (EMS) as it determines individual generator outputs that minimize generation cost while satisfying transmission, generation, and system level operating constraints. OPF relies on a core EMS routine, namely state estimation, which computes system states, principally bus voltages/phase angles at the buses. However, state estimation is vulnerable to false data injection attacks in which an adversary can alter certain measurements to corrupt the estimator's solution without being detected. It is also shown that a stealthy attack on state estimation can increase the OPF cost. However, the impact of stealthy attacks on the economic and secure operation of the system cannot be comprehensively analyzed due to the very large size of the attack space. In this paper, we present a hybrid framework that combines formal analytics with Simulink-based system modeling to investigate the feasibility of stealthy attacks and their influence on OPF in a time-efficient manner. The proposed approach is illustrated on synthetic case studies demonstrating the impact of stealthy attacks in different attack scenarios. We also evaluate the impact analysis time by running experiments on standard IEEE test cases and the results show significant scalability of the framework.
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Sporadic data integrity for secure state estimation
We consider the problem of network-based attacks, such as Man-in-the-Middle attacks, on standard state estimators. To ensure graceful control degradation in the presence of attacks, existing results impose very strict integrity requirements on the number of noncompromised sensors. We study the effects of sporadic data integrity enforcement, such as message authentication, on control performance under stealthy attacks. We show that even with sporadic data integrity guarantees, the attacker cannot introduce an unbounded state estimation error while remaining stealthy. We present a design-time framework to derive safe integrity enforcement policies, and illustrate its use; we show that with even 20% of authenticated messages we can ensure satisfiable state estimation errors under attacks.
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- PAR ID:
- 10056949
- Date Published:
- Journal Name:
- 2017 IEEE 56th Annual Conference on Decision and Control (CDC)
- Page Range / eLocation ID:
- 163 to 169
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/CDC.2017.8263660
