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

Bo Li (), Xin Jin, Tingjie Ba, Tingzhe Pan, En Wang and Zhiming Gu
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Bo Li: Electric Power Institute, Yunnan Power Grid Co., Ltd., Kunming 650217, China
Xin Jin: Institute of Measurement Technology, China Southern Power Grid Electric Power Research Institute Co., Ltd., Guangzhou 510663, China
Tingjie Ba: Yunnan Electric Power Grid Co., Ltd., Kunming 650011, China
Tingzhe Pan: Institute of Measurement Technology, China Southern Power Grid Electric Power Research Institute Co., Ltd., Guangzhou 510663, China
En Wang: Electric Power Institute, Yunnan Power Grid Co., Ltd., Kunming 650217, China
Zhiming Gu: Electric Power Institute, Yunnan Power Grid Co., Ltd., Kunming 650217, China

Energies, 2025, vol. 18, issue 12, 1-25

Abstract: 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.

Keywords: cyber-resilient photovoltaic systems; digital twin-based cybersecurity; attack diversion and deception strategies; reinforcement learning for cyber defense; blockchain authentication in smart grids; multi-objective optimization for grid security; computational overhead vs. security trade-off (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: 2025
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