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Peer-Review Record

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

Energies 2025, 18(12), 3145; https://doi.org/10.3390/en18123145
by Bo Li 1,2,*, Xin Jin 3,4, Tingjie Ba 5, Tingzhe Pan 3,4, En Wang 1,2 and Zhiming Gu 1,2
Reviewer 1: Anonymous
Reviewer 2:
Reviewer 3: Anonymous
Energies 2025, 18(12), 3145; https://doi.org/10.3390/en18123145
Submission received: 13 February 2025 / Revised: 20 May 2025 / Accepted: 26 May 2025 / Published: 16 June 2025
(This article belongs to the Special Issue Big Data Analysis and Application in Power System)

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The study presented in this paper is very meaningful. There are some issues that require revision by the authors.

1. The legend to Figure 1 is somewhat problematic, and the colors of YES / NO are not distinguished.

2. The number of references remains to be increased.

3. In section 3, some illustrations can be added to improve readability.

4. In Section 4, the wiring diagram of the PV-integrated smart microgrid should be given out.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

This article tackles cyber security problem for PV grids. In the proposed method, proactive measures use digital twins digital twin-based deception, along with reinforcement learning-driven cyber defense, and blockchain authentication to purposedly mislead cyber attackers to prevent from breaching real power grids. However, deployment of the virtual means increases computational overhead, it is necessary to find a balance between security, performance, and scalability.

 

In the article various mathematical metrics to make sure numerical optimization can come up with satisfactory results. Equation 25 needs to be checked as it appears only a constant.

 

In Figure 1, legend should remain to be corrected. In Figure 2, the x-axis unit is missing.

 

In final results, several figures are shown for better cyber security performance. However, indices of the figures such as cyber complexity factor, PV grid response factor, cyber defense strategy factor are not well defined yet.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

The paper proposes a multi-layer optimization method for enhancing the PV-integrated smart  grid security against cyberattacks. Digital twin-based deception, reinforcement learning-driven defense strategies and blockchain authentication are used to protect against false data injection and denial-of-service disruptions. Considering the current situation, the topic is highly relevant and significant.

  • The manuscript is well organized, and the references are appropriate in the context of the current state of research in the field. 
  • A few of the numbered expressions lack an equal sign, making them non-equations. I suggest that they be referred to as "expressions" or "functions."
  • Some doubts about the blockchain verification success rate values ​​extracted from figure 7.
  • The text provides clarification on how to interpret the figures, which are intuitive and clear.
  • The work fullfils the stated objectives: Energy management, operational stability, and cybersecurity in PV-integrated smart grids are all improved by the suggested framework, according to the case study's results.

The analysis of attack diversion efficiency, cyberattack detection rates, energy dispatch efficiency, resilience score, power loss reduction, response time, and blockchain authentication demonstrates that the framework, despite its complexity and comprehensiveness, does not impair operational performance.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

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