Electric Vehicle Charging Sessions Generator Based on Clustered Driver Behaviors^†

Round 1

Reviewer 1 Report

I congratulate the author on this work, Here are some notes on the paper with the aim of improving it. I wish you success.

1. An abstract is usually a paragraph that gives the full picture of your research in terms of problem definition, literature review, methodology, findings, and conclusion. Readers use the abstract to quickly learn the topic of your research. A well-written summary is critical to attracting readers so that they can open up and read about your work.

2. Abbreviations list does not contain all Abbreviations, for example DSO, TSO, APS, IEA.

3. The author should make sure that volume, pagination, and DIO number are mentioned in references bibliography. 

Author Response

We thank you for your contributions, comments and suggestions. It really helped us to enhance the quality of our paper. You can find our answers to your comments attached to this note.

Author Response File: Author Response.pdf

Reviewer 2 Report

This manuscript was highly organized with fluent English writing. Furthermore, this paper proposed a methodology that generates EV charging sessions for different types of EV drivers in facing the increasing penetration rate of electric vehicles (EV) requires the installation of new charge points, which can induce various problems. I think this manuscript can be accepted with minor revisions in WEVJ, here are the suggestions that need further address:

1. Minor grammar mistakes should be double-checked.

2. For the statement "Two main reasons can explain such evolution which are an increase of 317 the number of users and the evolution from PHEVs to BEVs.“ Do you have any proof or reference to support this?

3. For the statement:"The drawback of the methodology proposed in this paper is that it includes low energy demand charging sessions (from 2018 until 319 2020) in the statistical distributions" It takes courage to admit the shortcomings of your own manuscript. Do you have any makeup for this shortcoming of your methodology?

Author Response

We thank you for your contributions, comments and suggestions. It really helped us to enhance the quality of our paper. You can find our answers to your comments attached to this note.

Author Response File: Author Response.pdf

Reviewer 3 Report

This manuscript presents an electric vehicle charging sessions generator. The historical data have been analyzed to group different types of electric vehicle drivers together. For each group, specific sets of statistical parameters are extracted, which are then used by the generator. Here are some suggestions for improving the quality of the paper.

1. If possible, it is recommended to add some experiments to explain the performance difference between the proposed algorithm and the existing algorithm.

2. There are still many formal problems in the manuscript. For example, the definition of EV appears three times in line 1, line 17, and line 358 of the manuscript, and the summary of abbreviations is not complete in the Abbreviations part of the manuscript.

3. There are still format problems in the references in the manuscript, so please revise them according to the format requirements of the paper.

Author Response

We thank you for your contributions, comments and suggestions. It really helped us to enhance the quality of our paper. You can find our answers to your comments attached to this note.

Author Response File: Author Response.pdf

Reviewer 4 Report

Electric Vehicle Charging Sessions Generator Based on Clustered

Driver Behaviors

 

In this study, author has reported a case study for a hospital which plans to expand its EV fleet. The analysis includes electric vehicle (EV) charging sessions generator as it enables the creation of charging sessions data based on historical data of a specific charging location. Further historical data has been analyzed to group different type of EVs drivers.  The results indicate the strong impact on power and energy demand when adding new EV drivers to the population. The analyzed scenario highlights the need for grid reinforcement or smart charging technologies to avoid overloading and peak demands due to increase of charging session of specific EV driver types.

Author has reported a well-developed case study and manuscript details of simulation and methodology used and include in depth explanation for all the results rather than just explaining the plots. This manuscript can be considered to be publication with minor revisions.

1.     Author needs to check the word “actor” in line 32. Does it is intentional to use word “actor” or it will be “factor”?

2.     Author needs to provide the references for previous work in line 41. Also. It will be better to briefly describe previous works.

3.     It will better present a flow chart containing details about removing parking time and energy charged over and demand and respective methods.

4.     Author needs to go through complete manuscript and check the spelling, grammar and provide appropriate references at required palaces.

 

 

Author Response

We thank you for your contributions, comments and suggestions. It really helped us to enhance the quality of our paper. You can find our answers to your comments attached to this note.

Author Response File: Author Response.pdf

Reviewer 5 Report

This paper proposes a methodology that generates EV charging sessions for different types of EV drivers, which have been extracted from historical data via data mining techniques. However, there are some problems.

1. Introduction does not clearly present the latest developments in current research and the significance of this article.

2. Fig. 2 shows the data for the case when 'Number of charging sessions' = 0. 'Number of users' has three sets of data corresponding to 'Visitors', what does this mean and what does the conclusion of this case that frequent drivers represents 95.7% of the total number of charging sessions mean?

3. Fig. 5 results show a good match between the generated and the real charging sessions in this article, but the article does not mention the analysis of errors between them, for the part that does not match mentions that it is influenced by visitors, but the previous article mentions the data analysis of visitors and users, why this influencing factor is not well moderated.

4. Why is there no comparison between the generated data predicted from the training set for 2021-2022 and the validation data in Fig. 6?

Author Response

We thank you for your contributions, comments and suggestions. It really helped us to enhance the quality of our paper. You can find our answers to your comments attached to this note.

Author Response File: Author Response.pdf

Round 2

Reviewer 5 Report

The revised version is much better than the initial version and I have no more questions.