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Robust Distribution State Estimation for Reliable Locational Marginal Pricing under Cyber-Attacks

Journal Article · · IEEE Transactions on Power Systems
 [1];  [2];  [3]
  1. University of Alaska, Fairbanks, AK (United States)
  2. University of Idaho, Moscow, ID (United States)
  3. Idaho National Laboratory (INL), Idaho Falls, ID (United States)
Here this paper examines the impact of false data injection (FDI) cyber-attacks on distribution system state estimation (DSSE) and the resulting distribution locational marginal price (DLMP) in power markets. Two robust high-breakdown regression estimators, namely S- and MM- estimators, are implemented to provide resistance against FDI attacks targeting measurements and grid topology, creating leverage points. The introduced estimators are compared to the weighted least squares (WLS) with a bad data detection and rejection module (BDD) and the robust Huber M-estimator. The proposed estimators are shown to be effective and compare favorably to both existing Huber M- and the WLS with BDD in the presence of topology FDI attacks. Both the S- and MM-estimators provide good performance in the case of clean and corrupted measurements. Their performance is comparable in this case to the Huber M- and the WLS, followed by a BDD module. The simulation considered a modified distribution IEEE 13 and 34-bus systems where the impact of FDI attack scenarios is shown on the state and the DLMP pricing in the presence of distributed Generation.
Research Organization:
Idaho National Laboratory (INL), Idaho Falls, ID (United States)
Sponsoring Organization:
USDOE
Grant/Contract Number:
AC07-05ID14517
OSTI ID:
2205209
Report Number(s):
INL/JOU--23-71060-Revision-0
Journal Information:
IEEE Transactions on Power Systems, Journal Name: IEEE Transactions on Power Systems Journal Issue: - Vol. -; ISSN 0885-8950
Publisher:
IEEECopyright Statement
Country of Publication:
United States
Language:
English

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