Open Access

ARTICLE

Analysis and Defense of Attack Risks under High Penetration of Distributed Energy

Boda Zhang^1,*, Fuhua Luo¹, Yunhao Yu¹, Chameiling Di¹, Ruibin Wen¹, Fei Chen²
1 Network Security Department of Power Dispatching Control Center of Power Grid Co., Ltd., China Southern Power Grid Co., Nanming District, Guiyang, 550002, China
2 Key Laboratory of Control of Power Transmission and Conversion, Ministry of Education (Shanghai Jiao Tong University), Minhang District, Shanghai, 200240, China
* Corresponding Author: Boda Zhang. Email:

Energy Engineering https://doi.org/10.32604/ee.2025.069323

Received 20 June 2025; Accepted 15 September 2025; Published online 04 January 2026

Abstract

The increasing intelligence of power systems is transforming distribution networks into Cyber-Physical Distribution Systems (CPDS). While enabling advanced functionalities, the tight interdependence between cyber and physical layers introduces significant security challenges and amplifies operational risks. To address these critical issues, this paper proposes a comprehensive risk assessment framework that explicitly incorporates the physical dependence of information systems. A Bayesian attack graph is employed to quantitatively evaluate the likelihood of successful cyber attacks. By analyzing the critical scenario of fault current path misjudgment, we define novel system-level and node-level risk coupling indices to precisely measure the cascading impacts across cyber and physical domains. Furthermore, an attack-responsive power recovery optimization model is established, integrating DistFlow-based physical constraints and sophisticated modeling of information-dependent interference. To enhance resilience against varying attack scenarios, a defense resource allocation model is constructed, where the complex Mixed-Integer Nonlinear Programming (MINLP) problem is efficiently linearized into a Mixed-Integer Linear Programming (MILP) formulation. Finally, to mitigate the impact of targeted attacks, the optimal deployment of terminal defense resources is determined using a Stackelberg game-theoretic approach, aiming to minimize overall system risk. The robustness and effectiveness of the proposed integrated framework are rigorously validated through extensive simulations under diverse attack intensities and defense resource constraints.

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Keywords

CPDS; cyber-physical interdependence; Bayesian attack graph; Stackelberg game; risk assessment framework; power recovery; resource allocation

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