Smart Reserve Planning Using Machine Learning Methods in Power Systems with Renewable Energy Sources

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

1.      It is recommended to clearly list the contribution points of this article. Improve the image quality in the article and correct the redundant arrows, squares and horizontal lines in Figure 4 of the article.

2.      System reserve is taken into account when calculating EPNS. In Figure 4, how to increase the reserve when EPNS > EPNSmax?

3.      The conclusion is that increasing the EPNSmax identified by power system operators can reduce the need for backup but increase the total cost. Whether there is an economical and reliable operating relationship between these three aspects of the power system.

4.      The three machine learning methods MLP, LATM, and CNN mentioned in this paper are all implemented based on traditional methods. How to improve the algorithm to reduce the error?

Comments on the Quality of English Language

The writing should be improved.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The study is devoted to the selection of the optimal volume of power reserve in power systems with renewable electricity sources. This problem is certainly relevant for modern energy systems with a high share of renewable electricity sources. In their work, the authors describe the problem in detail, review existing research, and describe in detail the proposed methodology with subsequent testing. Despite the high quality of the article, there are several areas that could be strengthened:

1. The authors should have explicitly described the scientific novelty of the research.

2. When reviewing existing studies, it would be useful to provide a summary table of their strengths and weaknesses, and to identify the need for developing a new method.

3. It would be worth removing the wording “popular machine learning methods” from the title, because classifying a method as popular is subjective.

4. It is necessary to justify the choice of machine learning methods (MLP, CNN, and LSTM).

5. It is not entirely clear from the text of the article how the features for the machine learning method were selected.

6. If possible, the authors of the work should increase the quality of the figures (convert from raster to vector format)

7. The conclusions must provide the numerical results of the study, as well as describe directions for future research.

Comments on the Quality of English Language

Minor editing of English language required

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

I have no further comments.

Comments on the Quality of English Language

No comment.