Deep Reinforcement Learning for Distribution System Cyber Attack Defense with DERs

Selim, Alaa; Zhao, Junbo; Ding, Fei; Miao, Fei; Park, Sung-Yeul

doi:10.1109/isgt51731.2023.10066375

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Deep Reinforcement Learning for Distribution System Cyber Attack Defense with DERs

Selim, Alaa·Zhao, Junbo·Ding, Fei·Miao, Fei·Park, Sung-Yeul

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Abstract

The use of smart inverter capabilities of distributed energy resources (DERs) enhances the grid reliability but in the meanwhile exhibits more vulnerabilities to cyber-attacks. This paper proposes a deep reinforcement learning (DRL)-based defense approach. The defense problem is reformulated as a Markov decision making process to control DERs and minimizing load shedding to address the voltage violations caused by cyber-attacks. The original soft actor-critic (SAC) method for continuous actions has been extended to handle discrete and continuous actions for controlling DERs' setpoints and loadshedding scenarios. Numerical comparison results with other control approaches, such as Volt-VAR and Volt-Watt on the modified IEEE 33-node, show that the proposed method can achieve better voltage regulation and have less power losses in the presence of cyber-attacks.

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active distribution systems cyber attack deep reinforcement learning POWER TRANSMISSION AND DISTRIBUTION renewable generation

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Selim, Alaa, Zhao, Junbo, Ding, Fei, Miao, Fei, Park, Sung-Yeul. 2023-03-22. Deep Reinforcement Learning for Distribution System Cyber Attack Defense with DERs. https://doi.org/10.1109/isgt51731.2023.10066375

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