Journal Article

Deceptive Cyber-Resilience in PV Grids: Digital Twin-Assisted Optimization Against Cyber-Physical Attacks

Pan, Tingzhe, Li, Bo, Gu, Zhiming, Jin, Xin, Ba, Tingjie, Wang, En

2025

Overview

The increasing integration of photovoltaic (PV) systems into smart grids introduces new cybersecurity vulnerabilities, particularly against cyber-physical attacks that can manipulate grid operations and disrupt renewable energy generation. This paper proposes a multi-layered cyber-resilient PV optimization framework, leveraging digital twin-based deception, reinforcement learning-driven cyber defense, and blockchain authentication to enhance grid security and operational efficiency. A deceptive cyber-defense mechanism is developed using digital twin technology to mislead adversaries, dynamically generating synthetic PV operational data to divert attack focus away from real assets. A deep reinforcement learning (DRL)-based defense model optimizes adaptive attack mitigation strategies, ensuring real-time response to evolving cyber threats. Blockchain authentication is incorporated to prevent unauthorized data manipulation and secure system integrity. The proposed framework is modeled as a multi-objective optimization problem, balancing attack diversion efficiency, system resilience, computational overhead, and energy dispatch efficiency. A non-dominated sorting genetic algorithm (NSGA-III) is employed to achieve Pareto-optimal solutions, ensuring high system resilience while minimizing computational burdens. Extensive case studies on a realistic PV-integrated smart grid test system demonstrate that the framework achieves an attack diversion efficiency of up to 94.2%, improves cyberattack detection rates to 98.5%, and maintains an energy dispatch efficiency above 96.2%, even under coordinated cyber threats. Furthermore, computational overhead is analyzed to ensure that security interventions do not impose excessive delays on grid operation. The results validate that digital twin-based deception, reinforcement learning, and blockchain authentication can significantly enhance cyber-resilience in PV-integrated smart grids. This research provides a scalable and adaptive cybersecurity framework that can be applied to future renewable energy systems, ensuring grid security, operational stability, and sustainable energy management under adversarial conditions.

• Publisher: MDPI AG
• Subject: Adaptation / Algorithms / Alternative energy sources / attack diversion and deception strategies / Blockchain / blockchain authentication in smart grids / cyber-resilient photovoltaic systems / Cybersecurity / Cyberterrorism / Data security / Deception / Deep learning / digital twin-based cybersecurity / Digital twins / Efficiency / Infrastructure / Machine learning / Methods / multi-objective optimization for grid security / Optimization / Photovoltaic power generation / Prevention / reinforcement learning for cyber defense / Renewable resources / Safety and security measures / Technology application