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DOE OSTI · 2205209

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

Miles, Andrew G.·Chakhchoukh, Yacine·Alam, S. M. Shafiul
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Abstract

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.

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24 POWER TRANSMISSION AND DISTRIBUTIONcyber-securitydistribution locational marginal pricefalse data injectionleverage pointsrobust MM-estimatorrobust S-estimatorrobust distribution state estimationtopology attacks

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Miles, Andrew G., Chakhchoukh, Yacine, Alam, S. M. Shafiul. 2023-10-17. Robust Distribution State Estimation for Reliable Locational Marginal Pricing under Cyber-Attacks. https://doi.org/10.1109/tpwrs.2023.3325219

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