Data-Driven Probabilistic Anomaly Detection for Electricity Market under Cyber Attacks

Sun, Mucun; Ren, Lingyu; Chiang, Nai-yuan

doi:10.23919/acc50511.2021.9482640

Data-Driven Probabilistic Anomaly Detection for Electricity Market under Cyber Attacks

Conference · Tue May 25 04:00:00 EDT 2021 · 2021 American Control Conference (ACC)

DOI:https://doi.org/10.23919/acc50511.2021.9482640· OSTI ID:2217218

Sun, Mucun ^[1]; Ren, Lingyu; Chiang, Nai-yuan

Raytheon Technologies Research Center

Information and communication technologies have been widely used in smart grid for efficient operation. However, these technologies are vulnerable to malicious cyber attacks, which may lead to severe reliability and economic issues. Recently, a variety of data-driven anomaly detection approaches have been explored to detect potential cyber attacks in smart grids. In this paper, we researched on the electricity market data aiming to identify anomalies from the locational marginal prices (LMPs) and provide a new indicator for potential cyber attacks in power grids. Specifically, a novel data-driven probabilistic anomaly detection framework is proposed for electricity market, which consists of three major components: long short-term memory (LSTM) based deterministic electricity price forecasting, probabilistic electricity price forecasting and anomaly detection. This framework is tested on a model-based electricity market simulator under two types of cyber attacks, i.e., load redistribution attack (LRA) and price responsive attack (PRA). Numerical results on the simulated LMPs show that the proposed framework is capable of detecting data anomalies over these attacks.

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Research Organization:: Raytheon Technologies Research Center

Sponsoring Organization:: USDOE Office of Cybersecurity, Energy Security, and Emergency Response (CESER)

DOE Contract Number:: OE0000899

OSTI ID:: 2217218

Conference Information:: Journal Name: 2021 American Control Conference (ACC)

Country of Publication:: United States

Language:: English

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29 ENERGY PLANNING, POLICY, AND ECONOMY
cyber security
anomaly detection
machine learning
probabilistic forecasting
locational marginal price (LMP).

Data-Driven Probabilistic Anomaly Detection for Electricity Market under Cyber Attacks

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References (15)

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