Robust Distribution State Estimation for Reliable Locational Marginal Pricing under Cyber-Attacks
Journal Article
·
· IEEE Transactions on Power Systems
- University of Alaska, Fairbanks, AK (United States)
- University of Idaho, Moscow, ID (United States)
- 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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