Research on Reactive Power Optimization Based on Hybrid Osprey Optimization Algorithm
Round 1
Reviewer 1 Report
This work has great impact, since it presents an improved OSPREY optimization algorithm to solve reactive power optimization problems. The results that they present are with contribution in the area since the developed algorithm presents fast response times to make it converge to the optimal point, likewise it has great robustness and efficiency in solving practical problems. However, the document still has some observations and it is necessary to address them.
1.-It is necessary to add more references to further enrich this work, I leave some works that must be taken into account on optimization and learning algorithms.
“Morales, J. Y. R., López, G. L., Martínez, V. M. A., Vázquez, F. D. J. S., Mendoza, J. A. B., & García, M. M. (2020). Parametric study and control of a pressure swing adsorption process to separate the water-ethanol mixture under disturbances. Separation and Purification Technology, 236, 116214.”
“Sorcia-Vázquez, F. D. J., Garcia-Beltran, C. D., Valencia-Palomo, G., Brizuela-Mendoza, J. A., & Rumbo-Morales, J. Y. (2020). Decentralized robust tube-based model predictive control: Application to a four-tank-system. Revista Mexicana de Ingeniería Química, 19(3), 1135-1151.”
“Rentería-Vargas, E. M., Aguilar, C. J. Z., Morales, J. Y. R., De-La-Torre, M., Cervantes, J. A., Huerta, J. R. L., ... & Sánchez, R. O. (2022, September). Identification by Recurrent Neural Networks applied to a Pressure Swing Adsorption Process for Ethanol Purification. In 2022 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) (pp. 128-134). IEEE.”
2.- Improve the quality of figure 4.
3.- Place an annex section to add tables 1 and 2.
4.- The axes and bullets of figure 4 cannot be observed, improved or separated figures.
5.-There are two figures 6, one must be figure 7, describe them.
6.- What specific improvements should the authors consider regarding the methodology?, What further controls should be considered?
7.- Are the conclusions consistent with the evidence and arguments presented and do they address the main question posed?
This work has great impact, since it presents an improved OSPREY optimization algorithm to solve reactive power optimization problems. The results that they present are with contribution in the area since the developed algorithm presents fast response times to make it converge to the optimal point, likewise it has great robustness and efficiency in solving practical problems. However, the document still has some observations and it is necessary to address them.
1.-It is necessary to add more references to further enrich this work, I leave some works that must be taken into account on optimization and learning algorithms.
“Morales, J. Y. R., López, G. L., Martínez, V. M. A., Vázquez, F. D. J. S., Mendoza, J. A. B., & García, M. M. (2020). Parametric study and control of a pressure swing adsorption process to separate the water-ethanol mixture under disturbances. Separation and Purification Technology, 236, 116214.”
“Sorcia-Vázquez, F. D. J., Garcia-Beltran, C. D., Valencia-Palomo, G., Brizuela-Mendoza, J. A., & Rumbo-Morales, J. Y. (2020). Decentralized robust tube-based model predictive control: Application to a four-tank-system. Revista Mexicana de Ingeniería Química, 19(3), 1135-1151.”
“Rentería-Vargas, E. M., Aguilar, C. J. Z., Morales, J. Y. R., De-La-Torre, M., Cervantes, J. A., Huerta, J. R. L., ... & Sánchez, R. O. (2022, September). Identification by Recurrent Neural Networks applied to a Pressure Swing Adsorption Process for Ethanol Purification. In 2022 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) (pp. 128-134). IEEE.”
2.- Improve the quality of figure 4.
3.- Place an annex section to add tables 1 and 2.
4.- The axes and bullets of figure 4 cannot be observed, improved or separated figures.
5.-There are two figures 6, one must be figure 7, describe them.
6.- What specific improvements should the authors consider regarding the methodology?, What further controls should be considered?
7.- Are the conclusions consistent with the evidence and arguments presented and do they address the main question posed?
Author Response
Dear Reviewer,
Thank you for your letter and for the reviewers’ comments concerning our article energies-2612124 which we submitted to the Energies. Those comments are all valuable and very helpful for revising and improving our paper, as well as the essential guiding significance to our research. We have studied the comments carefully and have made corrections which we hope to meet with approval.
In the revised manuscript, we have used red text to indicate the modifications made based on your suggestions. The comments made by the reviewing experts and the corresponding responses are summarized below:
Response to Reviewer 1 Comments
1.It is necessary to add more references to further enrich this work, I leave some works that must be taken into account on optimization and learning algorithms
Response 1: After carefully reviewing the references you suggested, I have benefited greatly. These references can greatly enrich our work and are very helpful for optimization and algorithm learning. Therefore, we have cited your suggested references in this article, namely references [17], [18], and [23]. In the text, we display it in red font.
- - Improve the quality of figure 4
Response 2: Thank you for your suggestion. We have updated the image in the form of vector images and zoomed in to ensure clarity and readability.
3.Place an annex section to add tables 1 and 2
Response 3: Based on your suggestion, after adding Tables 1 and 2 to the appendix, the format of the document appears more reasonable.
4.The axes and bullets of Figure 4 cannot be observed, improved or separated figures
Response 4: Based on your suggestion, we have taken measures to display Figure 4 using vector graphics to ensure clearer axes and images.
- Here are two figures 6, one must be figure 7, describe them
Response 5: We are very sorry, we have corrected it and provided a detailed description of the image.
6.What specific improvements should the authors consider reviewing the methodology?, What further controls should be considered?
Response 6: Thank you for your question. Firstly, we have adopted three improvement strategies to improve the Osprey optimization algorithm, namely the population initialization strategy using Sobol sequence, the introduction of Weibull distribution step size factor in the Osprey search process, and the disturbance strategy based on firefly algorithm. Based on your suggestion, we have added an experiment on the effectiveness analysis of a single improvement strategy in the article to demonstrate the effectiveness of each specific improvement strategy. This experiment is presented in red font in section 4.5 of the article.
Secondly, in the future, we will consider applying the proposed improved Osprey optimization algorithm to more complex node systems, such as IEEE300 node systems. And consider adopting other advanced disturbance strategies, such as triangular walk disturbance strategies.
7.Are the conclusions consistent with the evidence and arguments presented and do they address the main question posed?
Response 7: Thank you for your comment. We have carefully reviewed and made further improvements to the conclusion section, which is displayed in red font in the text. Thank you!
We tried our best to improve the manuscript. These changes will not influence the content and framework of the paper. We appreciate for Editors/Reviewers’ warm work earnestly and hope that the correction will meet with approval. Once again, thank you very much for your comments and suggestions.
Thank you again for your review work and look forward to hearing your feedback.
Best regards,
Zhang yi,Liu pengtao
Author Response File: 
Author Response.docx
Reviewer 2 Report
The authors presented an improved Osprey Optimization Algorithm (IOOA) to solve the problems of reactive power optimization. First, the osprey population is initialized based on the Sobol se-quence to increase the initial population's diversity. The second, the probability factor based on Weibull distribution is introduced in the osprey position updating process to balance the explo-ration and development ability of the algorithm. At last, the disturbance based on the firefly op-timization algorithm is introduced to adjust the position of the osprey to enhance its ability to jump out of the local optimal. We compared multiple algorithms on a suite of CEC2017 test functions and performed Wilcoxon statistical tests to verify the validity of the proposed IOOA method. The experimental results show that the proposed IOOA has a faster convergence speed, a more robust ability to jump out of local optimal and higher robustness. We also apply the IOOA to the reactive power optimization problem of the IEEE33 distribution network and verify its fea-sibility and effectiveness in solving practical problems.
This work is original, novel and important to the field.
The paper could be published after major revision according to the attached pdf file.
Comments for author File: 
Comments.pdf
Minor editing is required.
Author Response
Dear Editors and Reviewers:
Thank you for your letter and for the reviewers’ comments concerning our article energies-2612124 which we submitted to the Energies. Those comments are all valuable and very helpful for revising and improving our paper, as well as the essential guiding significance to our research. We have studied the comments carefully and have made corrections which we hope to meet with approval.
In the revised manuscript, we have used yellow text to indicate the modifications made based on your suggestions. The comments made by the reviewing experts and the corresponding responses are summarized below:
Response to Reviewer 2 Comments
- The authors need to address the questions of practicality of this technology
Response 1: This article adopts an improved Osprey optimization algorithm to solve the practical problem of reactive power optimization in distribution networks. Previously, we applied it to an IEEE33 node system with distributed power sources for example analysis. Now, we have added a more complex IEEE69 system experiment (adding section 5.3 in the article) to verify the effectiveness of the proposed improved algorithm, The results indicate that our proposed algorithm also has significant advantages. Based on your suggestion, we will apply the algorithm to the study of reactive power optimization in a specific distribution system in a certain region in the future, to further enrich our work. Thank you for your suggestion!
- Why did the authors use proposed approach?
Response 2: The Osprey Optimization Algorithm is an optimization algorithm proposed in 2023 based on the behavior of Osprey, which simulates the hunting behavior of Osprey for optimization. Firstly, it has no other adjustable parameters, only adjustable parameters such as the population size and maximum iteration number that all swarm intelligence algorithms have. Furthermore, it has the characteristics of strong optimization ability and fast convergence speed. So, we choose it as the basic algorithm to apply to the reactive power optimization problem. Considering the complexity of reactive power optimization problems, we have improved the Osprey optimization algorithm to improve its convergence speed and ability to jump out of local optima. And in our CEC2017 numerical experiment, we analyzed the performance of the proposed algorithm, and the results showed that its performance in most test functions was stronger due to the comparison of other advanced optimization algorithms. Furthermore, we validated the effectiveness of IOOA on two reactive power optimization examples (IEEE33, IEEE69), and the results showed that it has stronger ability to solve practical problems in reactive power optimization.
- The introduction should be supported by recent publications of machine learning and metaEuropean technologies
Response 3: Firstly, thank you for providing so many excellent references; Furthermore, after carefully reviewing the references in the optimization field you provided, we have benefited greatly. Therefore, we have cited references [13], [14], [16], [19], [20], [21], and [22] in the article, which are displayed in yellow font. These references greatly enrich the literature review section of this article and provide more research basis for the article; Finally, thank you again!
We tried our best to improve the manuscript. These changes will not influence the content and framework of the paper. We appreciate for Editors/Reviewers’ warm work earnestly and hope that the correction will meet with approval. Once again, thank you very much for your comments and suggestions.
Thank you again for your review work and look forward to hearing your feedback.
Best regards,
Zhang yi,Liu pengtao
Author Response File: Author Response.docx
Reviewer 3 Report
The manuscript introduced an improved Osprey Optimization Algorithm to solve the problems of the slow convergence and local optimality. By and large, the idea behind this paper is interesting and certainly relevant for the scientific community. However, the manuscript should be improved in order to be considered for the publication. In my opinion, the literature review section has been poorly written and it does not describe properly the current state-of-the art. It hat regard, my suggestion to the authors is to significantly extend/update the literature review section. Furthermore, the superiority of the introduced optimization algorithm is shown on IEEE33 node system with two DG units and two groups of capacitor. My suggestion to the authors is to extend this section with simulation results that includes more complex networks with more flexible units. In other words, it will be interesting to see the algorithms performance in case when there are more degrees of freedom.
My suggestion to the editor is a major revision.
Extensive editing of English language required
Author Response
Dear Editors and Reviewers:
Thank you for your letter and for the reviewers’ comments concerning our article energies-2612124 which we submitted to the Energies. Those comments are all valuable and very helpful for revising and improving our paper, as well as the essential guiding significance to our research. We have studied the comments carefully and have made corrections which we hope to meet with approval.
In the revised manuscript, we have used blue text to indicate the modifications made based on your suggestions. The comments made by the reviewing experts and the corresponding responses are summarized below:
Response to Reviewer 3 Comments
- Literature review
Response 1: Our previous background and literature review sections were indeed lacking. After listening to your suggestions, we have further expanded this section to support the work of this article. After adopting your suggestions, the review section has been enriched and made more reasonable. In the text, it is displayed in blue font.
- Regarding the expansion of the experimental section
Response 2: Previously, we demonstrated the superiority of the introduced optimization algorithm on an IEEE33 node system with two DG units and two sets of capacitors. You suggest expanding the experimental section with simulation results, including more complex networks with more flexible units. We have carefully listened to your suggestions and added a more complex IEEE69 node system, as well as three DG units and three sets of capacitors. The locations of the distributed power supply are nodes 2, 5, and 56, and the locations of the capacitors are 16, 58, and 63. This example has more flexible units. The comparison with other optimization algorithms shows that under the same conditions, our proposed IOOA converges to a smaller active power loss value and obtains voltage fluctuations, further demonstrating the effectiveness and superiority of our proposed IOOA. It is precisely because of your suggestion that the experimental section has been greatly expanded and further validated the effectiveness of the proposed IOOA. The IEEE69 node system experiment is arranged in section 5.3 of the text and displayed in blue font.
We tried our best to improve the manuscript. These changes will not influence the content and framework of the paper. We appreciate for Editors/Reviewers’ warm work earnestly and hope that the correction will meet with approval. Once again, thank you very much for your comments and suggestions.
Thank you again for your review work and look forward to hearing your feedback.
Best regards,
Zhang yi,Liu pengtao
Author Response File: Author Response.docx
Round 2
Reviewer 1 Report
The authors attended the observations, the article can be accepted
The authors attended the observations, the article can be accepted
Reviewer 2 Report
Accept in the present form.
it is ok
Reviewer 3 Report
The authors have properly addressed the suggestions/comments. In my opinion, the manuscript can be accepted in present form.
Minor editing of English language required
