Ensuring Sustainable Grid Stability through Effective EV Charging Management: A Time and Energy-Based Approach

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The study proposes a method for managing EV charging demand by prioritizing EVs based on time to deadline and energy demand to prevent transformer overloading during peak load intervals. The author has done this by allocating energy to EVs according to their rankings while adhering to transformer capacity limits, with an aim to meet the energy demands of a larger number of electric vehicles.

While the paper is of interest and is well-organized, I'd like the author to correspond to several point to improve the manuscript, as follows: 

1. For such an important topic, implementing dynamic optimization algorithms that can adjust in real-time to changing grid conditions and user behaviors would definitely improve the method's ability to handle uncertainties and fluctuations in demand effectively. I'd like the author to elaborate on this point and discuss if it could be implemented in his proposed method. 

If not possible to fit in his model, then I highly recommend to discuss it as future way to expand his study. 

2. Complimenting point (1), the study is in a highly discussed topic and further discussion/expansion in the introduction would enrich this manuscript, especially with recent 2024 publications, highlighting the differences between them and your manuscript. Here is a list of newly published work in this arena that I recommend the author to incorporate: 

- Aljohani, T., Mohamed, M. A., & Mohammed, O. (2024). Tri-level hierarchical coordinated control of large-scale EVs charging based on multi-layer optimization framework. Electric Power Systems Research, 226, 109923.

- Esmaili, A., Oshanreh, M. M., Naderian, S., MacKenzie, D., & Chen, C. (2024). Assessing the spatial distributions of public electric vehicle charging stations with emphasis on equity considerations in King County, Washington. Sustainable Cities and Society, 107, 105409.

... etc. A total of 5~10 additions would be appreciated. 

3. The unserved index, presented in eq (10) is indeed a valuable metric for evaluating the performance of the proposed method. However, the author should consider further elaboration on how this index is calculated and interpreted, including the implications of high or low unserved percentages on the effectiveness of the proposed model.

4. The model assumes prioritization of EV charging based on time to deadline and energy demand to prevent transformer overloading. While the prioritization strategy seems logical, the paper should explicitly state the underlying assumptions of the model. It is essential to assess the validity of these assumptions and consider potential scenarios where they may not hold true. 

5. While the paper mentions the use of optimization algorithms for managing EV load, it would be beneficial to provide more details on the specific algorithms employed.

6. Expansion of the results section is highly recommended. Explaining the implications of the findings, discussing any unexpected outcomes, and relating the results back to the research objectives can strengthen the paper's impact and relevance.

7. The paper could provide more information on the dataset used for simulations and the methodology followed for performance evaluation. 

8. A list of glossary would be a good addition to the paper. 

 

 

Comments on the Quality of English Language

Moderate English checking is needed

Author Response

The study proposes a method for managing EV charging demand by prioritizing EVs based on time to deadline and energy demand to prevent transformer overloading during peak load intervals. The author has done this by allocating energy to EVs according to their rankings while adhering to transformer capacity limits, with an aim to meet the energy demands of a larger number of electric vehicles.

While the paper is of interest and is well-organized, I'd like the author to correspond to several point to improve the manuscript, as follows:

The author is thankful to the reviewer for reading our paper with interest and providing us with useful comments to improve it further. All the concerns raised by the reviewer are addressed in the revised manuscript. All major changes are highlighted in the revised manuscript. The point-to-point response to each comment is provided as follows.

1. For such an important topic, implementing dynamic optimization algorithms that can adjust in real-time to changing grid conditions and user behaviors would definitely improve the method's ability to handle uncertainties and fluctuations in demand effectively. I'd like the author to elaborate on this point and discuss if it could be implemented in his proposed method.

If not possible to fit in his model, then I highly recommend to discuss it as future way to expand his study.

Thank you for this comment. The authors agree that implementing dynamic optimization algorithms that can adjust in real-time to changing grid conditions and user behaviors would definitely improve the method's ability to handle uncertainties and fluctuations in demand effectively. As suggested by the reviewer, the following information is added at the end of the conclusions section.

Implementing dynamic optimization algorithms that can adjust in real-time to changing grid conditions and user behaviors would improve the method's ability to handle uncertainties and fluctuations in demand effectively. This approach aligns with the need to assess the validity of assumptions underlying the model and consider scenarios where these assumptions may not hold true, as discussed previously.

2. Complimenting point (1), the study is in a highly discussed topic and further discussion/expansion in the introduction would enrich this manuscript, especially with recent 2024 publications, highlighting the differences between them and your manuscript. Here is a list of newly published work in this arena that I recommend the author to incorporate:

- Aljohani, T., Mohamed, M. A., & Mohammed, O. (2024). Tri-level hierarchical coordinated control of large-scale EVs charging based on multi-layer optimization framework. Electric Power Systems Research, 226, 109923.

- Esmaili, A., Oshanreh, M. M., Naderian, S., MacKenzie, D., & Chen, C. (2024). Assessing the spatial distributions of public electric vehicle charging stations with emphasis on equity considerations in King County, Washington. Sustainable Cities and Society, 107, 105409.

... etc. A total of 5~10 additions would be appreciated.

The literature review part is improved and six new and recent articles, including the two suggested by the reviewer are added to the introduction section. All newly added information is highlighted.

3. The unserved index, presented in eq (10) is indeed a valuable metric for evaluating the performance of the proposed method. However, the author should consider further elaboration on how this index is calculated and interpreted, including the implications of high or low unserved percentages on the effectiveness of the proposed model.

Following the reviewer’s suggestion the following information is added to Section 3.3, last paragraph.

The unserved index proposed in this study offers a valuable quantitative measure of the effectiveness of EV load management methods. Calculated as the percentage of EVs not served during charging intervals, it directly reflects how well a method meets EV charging demand. A lower unserved index indicates higher efficiency in serving EVs, minimizing unmet demand and potential waiting times. Conversely, a higher index suggests inefficiencies in allocation or capacity, highlighting areas for improvement in charging infrastructure or management strategies. Policymakers and researchers can utilize this index to compare and optimize different methods, ensuring more reliable and responsive EV charging systems that better meet the needs of users and contribute to sustainable transportation goals.

4. The model assumes prioritization of EV charging based on time to deadline and energy demand to prevent transformer overloading. While the prioritization strategy seems logical, the paper should explicitly state the underlying assumptions of the model. It is essential to assess the validity of these assumptions and consider potential scenarios where they may not hold true.

To clarify the underline assumptions and validity of these assumptions, the following information is added Section 3, second paragraph.

The proposed model in this study adopts a prioritization strategy for EV charging based on remaining time to deadline and energy demand to prevent transformer over-loading. It assumes a static transformer capacity throughout the day, accurate and predictable daily load estimations for buildings and EVs. In addition, it considers prioritization focused solely on time and energy demand, fixed charging rates within de-fined limits, and exclusive management of EV charging without flexibility in building load handling. Potential scenarios where these assumptions may not hold true include fluctuating transformer capacities due to environmental factors or maintenance, un-predictable variations in load profiles, the need for more nuanced prioritization criteria beyond time and energy, evolving charging infrastructure capabilities such as vehicle-to-grid technologies, and dynamic adjustments in charging strategies based on real-time grid conditions.

5. While the paper mentions the use of optimization algorithms for managing EV load, it would be beneficial to provide more details on the specific algorithms employed.

The algorithm is developed by the author for this study and is not borrowed from another study. To clarify this and provide the overall processing to the proposed algorithm, the following information added to Section 3.2, last paragraph.

The process can be summarized as follows. First, EVs are prioritized based on their time to deadline and energy demand. For each time interval, the model computes normalized energy and time factors for EVs, ranks them using weighted criteria, and allocates energy starting from the highest-ranked EVs. Energy allocation continues until the transformer's capacity nears overload, updating the capacity after each allocation. EVs ranked lower in priority for a given interval are considered in subsequent intervals. This approach aims to optimize grid resource utilization while ensuring critical energy needs are met efficiently during high-demand periods.

6. Expansion of the results section is highly recommended. Explaining the implications of the findings, discussing any unexpected outcomes, and relating the results back to the research objectives can strengthen the paper's impact and relevance.

By following the reviewer’s comment a new section “4.5. Weekly Analysis” is added to the revised manuscript.     In this section, the performance of the proposed method is evaluated for seven consecutive days (a week). The purpose of this analysis is to determine the capability of the proposed method in managing the load of the network under different day types such as weekdays and holidays. In this section, three new figures are also added.

7. The paper could provide more information on the dataset used for simulations and the methodology followed for performance evaluation.

The following information provides the information on the dataset used for simulations, in Section 4, first paragraph.

To evaluate the performance of the proposed method in allocating power to EVs during peak load intervals, daily energy consumption profiles from a dataset representing an actual distribution system are utilized. The dataset used in this study pertains to a bus of an actual distribution system owned by a municipal utility situated in the Mid-west region of the United States. This system constitutes a fully observable network equipped with smart meters installed across all customer sites [16]. Specifically, a bus from feeder 3 is selected, encompassing 135 secondary distribution transformers and fed by a 69-kV substation. This section of the distribution grid predominantly serves residential areas and accommodates both single and three-phase loads.

8. A list of glossary would be a good addition to the paper.

All the abbreviations are listed at the end of the paper, in accordance with the MDPI’s paper formatting guidelines.

 

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

This paper proposes a method to prevent overloading of the transformer substation due to increased demand from electric vehicles during peak hours. The proposed approach is based on assessing the magnitude of overload and prioritizing the allocation of charging power to vehicles with higher rankings. The ranking value is calculated according to the time to deadline and the energy demand of each vehicle.

Although the subject matter is of strong interest, this paper has strong weaknesses that preclude its publication in this journal:

Novelty is unclear and weak, the proposed method is rather simple and widely used in the literature.

The reference scenario has not been well defined. Only one day is referred to considering a one-hour resolution. A more in-depth study that considers a larger time window to give more weight to the results obtained would be needed. The load estimation algorithm is unclear.

 In general, the exposition of content and results needs improvement.

Author Response

This paper proposes a method to prevent overloading of the transformer substation due to increased demand from electric vehicles during peak hours. The proposed approach is based on assessing the magnitude of overload and prioritizing the allocation of charging power to vehicles with higher rankings. The ranking value is calculated according to the time to deadline and the energy demand of each vehicle.

Although the subject matter is of strong interest, this paper has strong weaknesses that preclude its publication in this journal:

The author is thankful to the reviewer for reading our paper with interest and providing us with useful comments to improve it further. All the concerns raised by the reviewer are addressed in the revised manuscript. All major changes are highlighted in the revised manuscript. The point-to-point response to each comment is provided as follows.

Novelty is unclear and weak, the proposed method is rather simple and widely used in the literature.

The author believe that this ambiguity is caused due to improper explanation of the contributions and proposed method in the original manuscript. The following major changes are made in the revised manuscript to clarify this ambiguity.

• All major contributions are itemized at the end of the introduction section.
• More information is added to almost all the section from abstract to conclusions to clarify the contributions.

The reference scenario has not been well defined. Only one day is referred to considering a one-hour resolution. A more in-depth study that considers a larger time window to give more weight to the results obtained would be needed. The load estimation algorithm is unclear.

By following the reviewer’s comment a new section “4.5. Weekly Analysis” is added to the revised manuscript.     In this section, the performance of the proposed method is evaluated for seven consecutive days (a week). The purpose of this analysis is to determine the capability of the proposed method in managing the load of the network under different day types such as weekdays and holidays. In this section, three new figures are also added.

A summary of the load estimation process is added to Section 2.2 of the revised manuscript. All  major changes are highlighted.

In general, the exposition of content and results needs improvement.

By following the reviewer’s comment, the following major changes are made in the revised manuscript to clarify this ambiguity.

• Abstract is improved by adding key findings
• Introduced is improved by adding more relevant literature (major contributions are itemized).
• All key assumptions used in this study are mentioned.
• More information is added to load estimation section, proposed algorithm, and simulation environment section.
• Additional simulation results are added to analyze the performance for longer duration.
• Conclusions section is improved by adding more results and future research directions.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Dear Authors,

 

After reviewing the manuscript titled “Ensuring Sustainable Grid Stability Through Effective EV Charging Management: A Time and Energy Based Approach” No. sustainability-3081069, it was found that it could be accepted after modifications to the manuscript, such as:

Please add some results to the abstract.

The literature review needs expansion and updating such as:

Related Work and Motivation for Electric Vehicle Solar/Wind Charging Stations: A Review, World Electr. Veh. J. 2024, 15, 215. https://doi.org/10.3390/wevj15050215

Please expand the conclusion and add some important findings.

Please rearrange the manuscript so that the figures and table appear after they are referred to in the text.

Please modify the table numbering in the text (a number 1, not a letter I, see lines 279 - 282 - 284 - 294).

There is an undefined abbreviation (PEVs) on line 125, please add a table of all abbreviations and symbols.

Best Wishes

 

 

Comments on the Quality of English Language

Minor editing of English language required

Author Response

After reviewing the manuscript titled “Ensuring Sustainable Grid Stability Through Effective EV Charging Management: A Time and Energy Based Approach” No. sustainability-3081069, it was found that it could be accepted after modifications to the manuscript, such as:

The author is thankful to the reviewer for reading our paper with interest and providing us with useful comments to improve it further. All the concerns raised by the reviewer are addressed in the revised manuscript. All major changes are highlighted in the revised manuscript. The point-to-point response to each comment is provided as follows.

Please add some results to the abstract.

By following the reviewer’s comment, key results are added to the abstract.

The literature review needs expansion and updating such as:

Related Work and Motivation for Electric Vehicle Solar/Wind Charging Stations: A Review, World Electr. Veh. J. 2024, 15, 215. https://doi.org/10.3390/wevj15050215

The literature review part is improved and six new and recent articles, including the article suggested by the reviewer are added to the introduction section. All newly added information is highlighted.

Please expand the conclusion and add some important findings.

By following the reviewer’s comment, conclusions section is improved by adding more results and future research directions.

Please rearrange the manuscript so that the figures and table appear after they are referred to in the text.

By following the reviewer’s comment, all figures and tables are rearranged and now they appear after they are referred to in the text. .

Please modify the table numbering in the text (a number 1, not a letter I, see lines 279 - 282 - 284 - 294).

The highlighted mistake is corrected in the revised manuscript and is highlighted for the reference.

There is an undefined abbreviation (PEVs) on line 125, please add a table of all abbreviations and symbols.

The highlighted mistake is corrected in the revised manuscript and is highlighted for the reference. PEV is changed to EV for uniformity throughout the paper.

Best Wishes

Thank you again for reviewing our paper and providing us with useful comments.

 

 

 

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The author has addressed my concerns in a good way. I recommend accept. Congrats.

Author Response

Many thanks for your acceptance

Reviewer 2 Report

Comments and Suggestions for Authors

The authors modified the paper by introducing a more in-depth study through a weekly analysis of load profiles. The novelty and purpose of the paper have also been better clarified.

1)

However, the study and results are highly dependent on the type of load analyzed. On this front, the revised manuscript is still unclear. In particular, starting from the NHTS arrival, departure and parking duration data, how was the power profile of each vehicle and consequently the aggregate power calculated? No information is present regarding:

- the type/model of vehicle and its maximum charging power.

- The type and power of the charging points.

- The number of vehicles considered.

In practice, what scenario is being considered? In the graphs we see a peak of about 100 kW, does this mean having a peak of about 30 vehicles charging simultaneously with a power of 3 kW each? These considerations have not been presented/analyzed and are essential to assess the feasibility of the scenario and proposed profiles.

I would suggest that the authors consider the following reference, which adopts a similar method from NHTS data: https://doi.org/10.3390/en16042076

2)

The proposed method allows for load leveling, thereby decreasing substation congestion. This implies that the charging demand of a portion of vehicles no will be satisfied. Is it possible to quantify the difference in terms of "demand satisfaction" between the proposed method and the standard case? Are there any suggestions or additions to be considered in the algorithm to decrease this possible issue? Please clarify this point.

3)

The figures still require improvement. The following are some suggestions:

- Please increase the size of the figures from 3 to 9.

- Since these are hourly profiles, you can use lines with markers to identify the hourly value (as done in figure 3).

- Insert grid for both x- and y-axis in all graphs.

 

- it is suggested that the x-axis be expressed in hours for figures 10-11-12 as well.

Author Response

The authors modified the paper by introducing a more in-depth study through a weekly analysis of load profiles. The novelty and purpose of the paper have also been better clarified.

The author is thankful to the reviewer for reading our paper for the second time with interest and providing us with useful comments to improve it further. All the concerns raised by the reviewer are addressed in the revised manuscript. All major changes are highlighted in the revised manuscript. The point-to-point response to each comment is provided as follows.

1) However, the study and results are highly dependent on the type of load analyzed. On this front, the revised manuscript is still unclear. In particular, starting from the NHTS arrival, departure and parking duration data, how was the power profile of each vehicle and consequently the aggregate power calculated? No information is present regarding:

- the type/model of vehicle and its maximum charging power.

- The type and power of the charging points.

- The number of vehicles considered.

After estimating the daily mileage for vehicles, the study incorporates technical parameters specific to EVs. The dataset, referenced in [35], provides information on the mileage efficiency and usable battery size of all commercially available EVs. As of March 2024, the average energy efficiency for EVs is 195 Wh/km, and the average usable battery size is 68.9 kWh. An overview of different EV models is presented in Fig. 2. This data serves as the foundation for computing the daily energy consumption of EVs. Figure 3 illustrates the daily energy consumption of EVs, showing that most EVs consume under 25 kWh of energy per day. This observation aligns well with the average vehicle mileage of under 100 km per day and an average energy efficiency of 195 Wh/km.

 

To clarify this to the readers, the same information is added to Section 2.2 with two new figures.

Type 1 and type 2 chargers are considered in this study, as these are the most widely used chargers in residential sector. An EV fleet of 50 EVs is considered for the simulation. To clarify this the following information is added to Section 4.1

An EV fleet of 50 EVs is considered [38], and the distribution of EVs at the charging station during different hours of the day is depicted in Fig. 5. Level 1 and level 2 chargers are considered which are most commonly used in the residential sector.

 

In practice, what scenario is being considered? In the graphs we see a peak of about 100 kW, does this mean having a peak of about 30 vehicles charging simultaneously with a power of 3 kW each? These considerations have not been presented/analyzed and are essential to assess the feasibility of the scenario and proposed profiles.

I would suggest that the authors consider the following reference, which adopts a similar method from NHTS data: https://doi.org/10.3390/en16042076

An EV fleet size of 50 EVs is considered for the simulation. In terms of charging, the area under the curve (total energy consumed) gives a clearer picture instead of the peak power. For example, according to the area of EV load curve in Fig. 6, each EV is charging about 12kWh of energy per day, which is reasonable given the mileage efficiencies and daily travelling mileages, as discussed in Fig. 2 and 3.

 

The suggested reference is included in section 2.2.

2) The proposed method allows for load leveling, thereby decreasing substation congestion. This implies that the charging demand of a portion of vehicles no will be satisfied. Is it possible to quantify the difference in terms of "demand satisfaction" between the proposed method and the standard case? Are there any suggestions or additions to be considered in the algorithm to decrease this possible issue? Please clarify this point.

This aspect is quantified in the study by proposing an index named as Unserved Index in Section 3.3. The unserved index proposed in this study offers a valuable quantitative measure of the effectiveness of EV load management methods. Calculated as the percentage of EVs not served during charging intervals, it directly reflects how well a method meets EV charging demand. A lower unserved index indicates higher efficiency in serving EVs, minimizing unmet demand and potential waiting times. Conversely, a higher index suggests inefficiencies in allocation or capacity, highlighting areas for improvement in charging infrastructure or management strategies. Policymakers and researchers can utilize this index to compare and optimize different methods, ensuring more reliable and responsive EV charging systems that better meet the needs of users and contribute to sustainable transportation goals.

The performance of the proposed method is evaluated against the earliest deadline first method in terms of served and unserved EVs. The unserved index values consistently favor the proposed method over the earliest deadline method, showcasing lower values. During load adjustment intervals, this difference ranges from 14% to 33%, signifying the superiority of the proposed method in efficiently managing EV demand. This analysis is presented in Section 4.4.

 

3) The figures still require improvement. The following are some suggestions:

- Please increase the size of the figures from 3 to 9.

- Since these are hourly profiles, you can use lines with markers to identify the hourly value (as done in figure 3).

- Insert grid for both x- and y-axis in all graphs.

 - it is suggested that the x-axis be expressed in hours for figures 10-11-12 as well.

 

All figures are improved in the revised manuscript by making the flowing changes.

1. Both x and y grids are added to all figures
2. Size of the figures is enhanced for better visualization
3. In Figs. 12-14, x-axis is expressed in hours for consistency

 

Author Response File: Author Response.pdf

Round 3

Reviewer 2 Report

Comments and Suggestions for Authors

Please review the paper further because mistakes are present, for example, the caption in Figure 3 is missing.

Author Response

Comment 1. Please review the paper further because mistakes are present, for example, the caption in Figure 3 is missing.

Author: Thank you for your comment. The paper has been fully reviewed and updated.