A Morphing-Based Future Scenario Generation Method for Stochastic Power System Analysis

Round 1

Reviewer 1 Report

In my opinion, the presented research results have not been comprehensively discussed and should be improved before publishing.

For example, Figure 2 shows a relatively large amount of data regarding the temperature (15 series of data) in one graph window but the discussion contains only the average annual variations (in lines 307 - 309). Figure 2 would be much more readable if the authors used trend lines for plots or statistical discussion in the text of the results (maximum and minimum values, mean, median, ranges, etc.). Statistical data of the results of the particular scenario (SSP1, SSP2, SSP3 or SSP4) for 2050 could be then compared with the data of 2023.

The same comment applies to Figures 3 and 5. I regret to write that in this form both figures are valueless because there is nothing interesting to be seen. Especially, in terms of temperature and wind speed, the differences between scenarios are completely invisible. This effect is even worse after printing the figures. In lines 312 - 313, the authors mentioned the consideration of 600 scenarios but no discussion in the text about the results of the scenarios. Instead, the descriptions were placed below the figures. For Fig. 3a: "temperature stabilize around 1.8°C" - it is not seen in the Figure. I would consider changing the way of the presentation of the results or at least providing a statistical description of the results.

In lines 323  - 324 authors wrote that Figure 4 "illustrates the relationship between DBI and clustering parameters". Shouldn't be the axis description "DBI" instead of "Score"? Moreover, it is written that "the DBI achieves its maximum value at 8" but it is not seen in Figure 4. Could you explain it in more detail?

In Table 1 energy production was provided. It would be useful to provide also the capacity of the PV system and wind turbine (in kW) which were considered in this scenario.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

The paper titled " Morphing Based Future Scenario Generation Method for Stochastic Power System Analysis" presents an innovative approach to address the challenges posed by the integration of multiple wind and solar photovoltaic farms into the power system amidst the uncertainties of future climate change. The proposed method utilizes a combination of morphing, copula theory, and cluster analysis to generate future scenarios, taking into account the interdependence of power generation among renewable farms.

While the paper provides a detailed explanation of the proposed methodology, some readers may find certain aspects of the method difficult to grasp due to its complexity. Simplifying the language or providing additional illustrations could enhance understanding for a wider audience.

In order to enhance the clarity and scientific significance of the visual representations, it is imperative to undertake data processing for visualization, particularly for Figures 2 and 3. Although Figure 5 exhibits a higher degree of clarity, it would still benefit from data processing to optimize visual coherence. The current presentation of lines and markers within the figures is deemed excessively cluttered, thus diminishing their efficacy in effectively conveying scientific insights. Therefore, a more refined approach to data visualization is warranted to better highlight the substantive findings depicted within the figures.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

This work presents an interesting research. The article seems to be free of important technical errors. The paper needs a minor revision as there are some things to be checked. Some advices and suggestions are given below. Please, take into account as suggested in this document.

1.    Measures are better to express all in a same unit like SI unit, in order to avoid confusion.

2.    Several abbreviations appeared along the paper. For that reason, it is highly recommended to include a list with them in the beginning of the paper.

3.    It is not so easy to find references given in the paper. If references are arranged in order of appearance, numeric format is recommended. Otherwise, alphabetic order can make easier their reading.

4.    It is better to have a brief explanation about Morphing method. If this methodology is based on other references, please mention it; if not, describe it in more detail.

5.    Case study: The current 3. Results. To give a more detailed information about the present capacity of wind and solar PV farms is highly recommended.

Minor editing of English language required

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

The Authors have addressed my comments in details. The manuscript has been corrected according to them. 

Some minor comments were listed below.  

I would consider using the Global horizontal irradiance (GHI, in W/m²) instead of Global horizontal radiation. The first term seems to be more appropriate. The decision is up to the Authors.  

Line 346: I would recommend using 3.5 instead of the highly precision value of 3.529 kW/m2

Provide a space between the value and its unit (e.g. 50 MW, 60 MW etc.)

The numerical values of the most important results should be provided in the conclusion section

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

The authors of the paper titled "Morphing Based Future Scenario Generation Method for Stochastic Power System Analysis" have commendably addressed the comments raised by the reviewers, which is acknowledged with gratitude. Nevertheless, two principal concerns persist.

While the paper offers a comprehensive elucidation of the proposed methodology, some readers may encounter challenges in discerning the primary distinctions in methods responsible for various variations in a weather element in visualized results. For example, the first panel in Figure 2, illustrating Dry Bulb Temperature (DBT) throughout one year, encompasses three types of DBT (DBT1, DBT2, and DBT3). However, the precise differences between these variants remain unclear to me. Similarly, elucidation regarding the distinctions among the scenarios SSP1, SSP2, SSP3, and SSP5 is warranted. The placement of such information should be strategically positioned so readers are easy to find.

It is acknowledged that the authors have enhanced the readability of the figures compared to the previous draft. Nonetheless, interpreting Figure 3 and Figure 5 still poses difficulties. A recommendation is made to consider modifying these figures to achieve a higher degree of clarity. For instance, in Figure 3, each panel comprises 600 curves corresponding to 600 clusters of random scenarios. It is suggested to plot either the average or median of each weather element throughout a year and incorporate error bars at each hour to denote the deviations induced by random scenarios. Such modifications would facilitate a more accessible interpretation of the data presented.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 3

Reviewer 2 Report

Enhancing the clarity of Figure 4 would greatly benefit readers' comprehension. Additional explanations elucidating the figure's significance, including the decreasing trend of curves along the horizontal axis, and clarifying the interpretation of gray boxes, are recommended. Providing such details will assist readers in understanding the process of identifying the optimal parameter for K-means clustering, thereby enhancing the overall understanding of the methodology.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf