Zero-Trust Zero-Communication Defence against Hybrid Cyberattacks in Distributed Energy Resources Using Mean Field Reinforcement Leaning

Zejian Zhou, Dongliang Duan () and Hao Xu
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Zejian Zhou: Electrical Engineering and Computer Science Department, University of Wyoming, Laramie, WY 82071, USA
Dongliang Duan: Electrical Engineering and Computer Science Department, University of Wyoming, Laramie, WY 82071, USA
Hao Xu: Electrical and Biomedical Engineering Department, University of Nevada, Reno, NV 89557, USA

Energies, 2024, vol. 17, issue 20, 1-21

Abstract: As the evolution of smart grids accelerates, distributed energy resources (DERs) emerge as key elements in the transformation of global energy systems. However, the integration of these technologies introduces significant cybersecurity vulnerabilities, notably false data injection (FDI) and a direct load-altering attack (DLAA). Traditional load-altering attacks require a huge attack load and, thus, are not practical to implement. In contrast, in modern DER environments where households become “prosumers” with high-power energy generation, the implications of such attacks are substantially amplified. This paper considers a hybrid cyberattack that includes both FDI and a DLAA, and presents a hierarchical, optimal load adjustment framework that addresses these security concerns. A centralized optimizer first calculates the ideal load-shedding strategies for each substation, which are then securely broadcast to households. To address the complexities at the individual household level, we introduce a novel reinforcement learning algorithm termed Mean Field Deep Deterministic Policy Gradients (MF-DDPG). This algorithm employs mean-field game theory to enable decentrally coordinated decision-making among each household, making it particularly effective in zero-trust scenarios. Through this multifaceted approach, we offer a robust countermeasure against load-altering attacks, thereby enhancing the resilience and stability of advanced smart grids.

Keywords: load demand attack; mean field games; reinforcement learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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