Peer Review History

Original SubmissionMarch 5, 2025
Decision Letter - Chandan Kumar Shiva, Editor

PONE-D-25-11087Optimization the Stochastic Optimal Reactive Power Dispatch with renewable energy resources using a Modified Dandelion AlgorithmPLOS ONE

Dear Dr. Ebeed,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Based on the reviewers' feedback, a major revision is required before further consideration. The Key concerns include clarity in technical language, need for more details on Modified Dandelion Optimizer algorithm, and a refined contribution statement. The scenario selection process and sensitivity analysis need elaboration, along with a detailed discussion on computational complexity, convergence behaviour, and statistical analysis. Figures should be improved. Please address these issues and submit a revised manuscript with a detailed response.

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We look forward to receiving your revised manuscript.

Kind regards,

Chandan Kumar Shiva

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: 1- The paper contains multiple language issues that make the text hard to follow (e.g., “increases the complicities,” “application the proposed algorithm”). Careful proofreading is needed to improve readability and clarity.

2- Some terms and ideas are not clearly defined, like "stochastic fluctuations" and "expected voltage stability." A clearer definition of technical terms would help readers from broader backgrounds.

3- While the enhancements to the Dandelion Optimizer are interesting, the paper would benefit from a more in-depth discussion of why these specific techniques (QOBL, WFM, FDB) were chosen and how they address limitations of the original Dandelion Optimizer.

4- The contribution of the paper compared to other hybrid optimization approaches needs to be more clearly stated. How does this work advance the state of the art in SORPD beyond existing techniques?

5- The paper mentions 15 scenarios for representing load and RER uncertainties but does not clarify how these scenarios were selected or reduced. More insight into the scenario generation and reduction process would add credibility to the stochastic modeling.

6- A sensitivity analysis on the number of scenarios would strengthen the argument for the robustness of the proposed approach.

7- The paper should discuss the computational complexity of the MDO algorithm and how it scales with larger systems.

Convergence behavior and runtime performance compared to the other optimization techniques should be clearly presented, ideally with convergence plots.

8- While the results seem promising, the paper would benefit from a more detailed discussion of why the MDO algorithm outperforms others. What specific characteristics of the enhanced MDO contribute to its superior performance?

9- Statistical analysis of the results (mean, variance, confidence intervals) would provide a more rigorous comparison between optimization techniques.

10- figure 9 is repeated

11- improve figure 1, 9, 10

Reviewer #2: 1- More details are needed about problem formulation.

2- Title of 3.2 “ Modified Dandelion optimizer “

Title of 4.2 “ Modified DANDELION OPTIMIZER “

are same.

3- Numbers of titles and subtitles should be corrected.

4- At figure 12 , give a reason, why BWO algorithm is the highest value of SEPL (MW) than other algorithms.

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Reviewer #1: Yes:  Khairy Sayed

Reviewer #2: Yes:  Loai Saad Eldeen NASRAT

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Revision 1

The response to reviewer comments has been uploded

Attachments
Attachment
Submitted filename: Response to Reviewers.docx
Decision Letter - Chandan Kumar Shiva, Editor

PONE-D-25-11087R1Optimization the Stochastic Optimal Reactive Power Dispatch with renewable energy resources using a Modified Dandelion AlgorithmPLOS ONE

Dear Dr. Ebeed,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================<h1>Editor's Decision: Minor Revision</h1>

Based on the reviewers' comments, this manuscript requires minor revisions before publication. Both reviewers identified issues with formatting, language clarity, and technical content. Key improvements needed include proofreading for language errors, clearer definitions of technical terms, justification for the proposed algorithm modifications, more rigorous comparison with existing techniques, detailed analysis of computational complexity, and correction of formatting inconsistencies and figure issues. Please address the duplicate Figure 9, improve Figures 1, 9, and 10, and explain the BWO algorithm's higher SEPL value.

==============================

Please submit your revised manuscript by Jul 05 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

  • A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
  • A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
  • An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols .

We look forward to receiving your revised manuscript.

Kind regards,

Chandan Kumar Shiva

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/ . PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org . Please note that Supporting Information files do not need this step.

Revision 2

Comment 1: “Both reviewers identified issues with formatting, language clarity, and technical content.”

Authors’ Response: The authors appreciate the editor and reviewers’ comments. A thorough proofreading was carried out. Linguistic and grammatical errors were corrected. Additional explanations have been added to clarify ambiguous terms. The formatting of the manuscript has been revised.

Comment 2: “justification for the proposed algorithm modifications “

Authors’ Response: The authors appreciate the editor and reviewers’ comments. The limitations of the original Dandelion Optimizer (DO) are that it prone to local optima and suffers from stagnation in case of solving the nonlinear objective functions like ORPD. In this regard, three modifications have been integrated for improving the searching ability of the DO using FDB, QOBL, and WFM.

Comment 3: “More rigorous comparison with existing techniques “

Authors’ Response: The authors appreciate the editor and reviewers’ comments. A comparsion between the proposed algorithm and the other techniques has included in Table 8 in the revised paper.

Comment 4: “detailed analysis of computational complexity “

Authors’ Response: The authors appreciate the editor and reviewers’ comments.” It should be mentioned that the proposed MDO has a high level of computational complexity, because the DO is based on three stages, including rising, landing, and descending. Hence, the computational complexity of the DO is O(M×T×pop×Dim×fit), where M, T, pop, Dim, and fit are the current optimal solution, the maximum iteration number, the number of populations, the problem dimension, and the objective function [87]. The MDO is based on three modifications, including the WFM, QOBL, and FDB, Consequently, the computational complexity of the MDO is O(M×T×pop×Dim×fit +3(M×T×pop×Dim×fit)). Hence, the computational complexity of the MDO is O(4(M×T×pop×Dim×fit)).” This explanation is added on the manuscript between lines 508 and 515.

Comment 5: “Figure 9 is repeated.”

Authors’ Response: The authors appreciate the editor and reviewers’ comments. Figure 9 has been updated to avoid repetition.

Comment 6: “improve figure 1, 9, 10.”

Authors’ Response: The authors appreciate the editor and reviewers’ comments. The quality of the mentioned figures has been improved on the manuscript.

Comment 7: “Please explain the BWO algorithm's higher SEPL value. “

Authors’ Response: The authors sincerely appreciate the editor's and reviewers’ insightful comments.

“The BWO algorithm distributes its candidate solutions widely in the search space to avoid premature convergence and accurately explore potential optimal solutions. While this intensive exploration allows local minima to be avoided, it can also lead to less consistent or higher values of the objective function in some runs. It is important to note that Figure 12 shows the distribution of the objective function, which includes both the objective function and the system constraints.

Attachments
Attachment
Submitted filename: Response to Editor and Reviewers.docx
Decision Letter - Chandan Kumar Shiva, Editor

Optimization the stochastic optimal reactive power dispatch with renewable energy resources using a modified dandelion algorithm

PONE-D-25-11087R2

Dear Dr. Ebeed,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Chandan Kumar Shiva

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Formally Accepted
Acceptance Letter - Chandan Kumar Shiva, Editor

PONE-D-25-11087R2

PLOS ONE

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Academic Editor

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