Load Profile and Load Flow Analysis for a Grid System with Electric Vehicles Using a Hybrid Optimization Algorithm
Round 1
Reviewer 1 Report
The manuscript at hand offers an intriguing exploration of electric vehicles' (EVs) integration into the power grid. As the authors rightly point out, the growing popularity of EVs necessitates strategic adaptations in our power transmission infrastructure. Their proposed solution employs a hybrid genetic algorithm particle swarm optimization (HGAIPSO) to determine the optimal placement and sizing of EVs in a radial transmission network, with the aim of mitigating power loss and voltage instability. The authors should be commended for their innovative approach to this pressing issue. The study acknowledges the complexities of integrating EVs into the grid and suggests an intelligent, AI-driven solution that doesn't simply rely on upgrades to the existing grid. Their use of the HGAIPSO is particularly noteworthy, and the beneficial impacts of this method on reducing power loss and stabilizing voltage profiles are compelling.
While the study presents some impressive findings, there are a few areas that could benefit from further clarification or expansion:
-> Algorithm Explanation: While the HGAIPSO is central to this research, the manuscript could benefit from a more detailed explanation of the algorithm. How does it function and why is it particularly suited to this application?
-> Implementation Practicalities: How feasible is the proposed method when it comes to real-world implementation? Discussion of potential challenges and solutions would be beneficial.
-> Scalability: The study relies on the IEEE-30 bus test system. However, it would be interesting to explore how this method would perform in larger or more complex systems.
-> Comparison to Other Methods: Comparing the HGAIPSO method to other existing or proposed methods for grid optimization could add depth to the study and provide a clearer context for the significance of the results.
Despite these suggestions, the study is undoubtedly a valuable contribution to the field. It offers an intelligent, practical solution to the challenges presented by the rise of EVs, demonstrating how AI can facilitate the transition to more sustainable transportation options. The results are promising, and further research and development in this area will undoubtedly be exciting to observe.
Author Response
REVIEWER ONE COMMENTS
The manuscript at hand offers an intriguing exploration of electric vehicles' (EVs) integration into the power grid. As the authors rightly point out, the growing popularity of EVs necessitates strategic adaptations in our power transmission infrastructure. Their proposed solution employs a hybrid genetic algorithm particle swarm optimization (HGAIPSO) to determine the optimal placement and sizing of EVs in a radial transmission network, with the aim of mitigating power loss and voltage instability. The authors should be commended for their innovative approach to this pressing issue. The study acknowledges the complexities of integrating EVs into the grid and suggests an intelligent, AI-driven solution that doesn't simply rely on upgrades to the existing grid. Their use of the HGAIPSO is particularly noteworthy, and the beneficial impacts of this method on reducing power loss and stabilizing voltage profiles are compelling.
While the study presents some impressive findings, there are a few areas that could benefit from further clarification or expansion:
-> Algorithm Explanation: While the HGAIPSO is central to this research, the manuscript could benefit from a more detailed explanation of the algorithm. How does it function and why is it particularly suited to this application?
Author’s comment: More information has been added about the algorithm
-> Implementation Practicalities: How feasible is the proposed method when it comes to real-world implementation? Discussion of potential challenges and solutions would be beneficial.
Author’s comment: More information has been added on the discussion section
-> Scalability: The study relies on the IEEE-30 bus test system. However, it would be interesting to explore how this method would perform in larger or more complex systems.
Author’s comment: Thank you for the comment, this will be considered in the future studies
-> Comparison to Other Methods: Comparing the HGAIPSO method to other existing or proposed methods for grid optimization could add depth to the study and provide a clearer context for the significance of the results.
Author’s comment: The comparisons has been done one results and discussion section
Despite these suggestions, the study is undoubtedly a valuable contribution to the field. It offers an intelligent, practical solution to the challenges presented by the rise of EVs, demonstrating how AI can facilitate the transition to more sustainable transportation options. The results are promising, and further research and development in this area will undoubtedly be exciting to observe.
Reviewer 2 Report
My comments are presented below. Please consider them for the next round of review.
1- The paper includes many problems in terms of structure and language, for instance, (by in []) in line 156. Hence, you should check it accurately.
2- The abstract is lengthy, please define the main problem briefly first and then explain the goal and some numerical results.
3- The quality of figures and tables quality is not acceptable.
4- I think most of the descriptions related to the other algorithms can be removed (use reference just) and only explain the structure of the hybrid algorithm.
1- The paper includes many problems in terms of structure and language, for instance, (by in []) in line 156. Hence, you should check it accurately.
Author Response
REVIEW TWO COMMENTS
My comments are presented below. Please consider them for the next round of review.
1- The paper includes many problems in terms of structure and language, for instance, (by in []) in line 156. Hence, you should check it accurately.
Author’s comment: The error has been corrected
2- The abstract is lengthy, please define the main problem briefly first and then explain the goal and some numerical results.
Author’s comment: The abstract has been summarised accordingly
3- The quality of figures and tables quality is not acceptable.
Author’s comment: The quality of figures has been improved
4- I think most of the descriptions related to the other algorithms can be removed (use reference just) and only explain the structure of the hybrid algorithm.
Author’s comment: Some information has been removed making the description more specific
Comments on the Quality of English Language
1- The paper includes many problems in terms of structure and language, for instance, (by in []) in line 156. Hence, you should check it accurately.
Author’s comment: Errors has been corrected.
Reviewer 3 Report
Overall, the paper titled "Load Profile and Load Flow Analysis for a Grid System with Electric Vehicles Using a Hybrid Optimization Algorithm" addresses an important topic related to the integration of electric vehicles (EVs) into the grid system. However, there are several areas where the paper can be improved:
1. The paper assumes that readers are already familiar with the challenges and issues associated with EV integration into the grid. It would be beneficial to provide a more comprehensive introduction that outlines the current state of EV adoption, the impact on the grid, and the existing approaches to address these challenges.
2. The paper mentions the use of a hybrid genetic algorithm particle swarm optimization (HGAIPSO) for determining the optimal EV location and size. However, the paper does not provide enough details about this algorithm, such as the specific techniques employed, convergence properties, or the rationale for selecting this particular approach. It is important to provide a thorough explanation of the algorithm to establish its credibility and potential advantages over other methods.
3. What’s the scope of other metaheuristic algorithms? You may add brief comparison referring to ‘Application of metaheuristic optimization based support vector machine for milling cutter health monitoring,’ ‘A white-box SVM framework and its swarm-based optimization for supervision of toothed milling cutter through characterization of spindle vibrations’
4. The paper briefly mentions the objective of finding optimal EV locations and sizes in a radial transmission network. However, it would be beneficial to clearly state the research objectives and research questions to guide the study. This will help readers understand the purpose of the research and evaluate the effectiveness of the proposed method in achieving those objectives.
5. While the paper mentions a comparative study and provides some quantitative results regarding power loss reduction and voltage improvement, it lacks a detailed analysis and interpretation of the findings. The paper should include a thorough discussion of the results, including any limitations or assumptions made, and provide insights into the implications and significance of the observed improvements.
6. It is important to acknowledge and discuss the limitations of the proposed approach. This could include considerations such as scalability to larger grid systems, sensitivity to input parameters, computational efficiency, and generalizability to different network topologies. Additionally, the paper should address the applicability of the proposed method in real-world scenarios and discuss potential challenges in implementing the suggested solution.
7. The paper briefly mentions economic models and methods to engage and reward users. However, it lacks a detailed discussion on the social and economic aspects of implementing the proposed solution. Considering the potential impact on electricity pricing, consumer behavior, and infrastructure costs, a more comprehensive analysis of the socio-economic implications would strengthen the paper.
8. Hyperparameters of designed network must be included in a tabular form. You may refer to paper ‘Augmentation of decision tree model through hyper-parameters tuning for monitoring of cutting tool faults based on vibration signatures’
9. Was the algorithm trained using standard hyperparameters or were they altered?
10. Comment on computational time and complexity in training of algorithm.
By addressing these points, the paper can provide a more thorough and comprehensive analysis of the proposed method and its potential contributions to the integration of EVs into the grid system.
Moderate editing of English language required
Author Response
REVIEWER THREE COMMENTS
Overall, the paper titled "Load Profile and Load Flow Analysis for a Grid System with Electric Vehicles Using a Hybrid Optimization Algorithm" addresses an important topic related to the integration of electric vehicles (EVs) into the grid system. However, there are several areas where the paper can be improved:
- The paper assumes that readers are already familiar with the challenges and issues associated with EV integration into the grid. It would be beneficial to provide a more comprehensive introduction that outlines the current state of EV adoption, the impact on the grid, and the existing approaches to address these challenges.
Author’s comment: Introduction is provided
- The paper mentions the use of a hybrid genetic algorithm particle swarm optimization (HGAIPSO) for determining the optimal EV location and size. However, the paper does not provide enough details about this algorithm, such as the specific techniques employed, convergence properties, or the rationale for selecting this particular approach. It is important to provide a thorough explanation of the algorithm to establish its credibility and potential advantages over other methods.
Author’s comment: More detailed explanation of the algorithm has been provided
- What’s the scope of other metaheuristic algorithms? You may add brief comparison referring to ‘Application of metaheuristic optimization based support vector machine for milling cutter health monitoring,’ ‘A white-box SVM framework and its swarm-based optimization for supervision of toothed milling cutter through characterization of spindle vibrations’
Author’s comment: Thank you for the recommendation the results and the discussion has been updated.
- The paper briefly mentions the objective of finding optimal EV locations and sizes in a radial transmission network. However, it would be beneficial to clearly state the research objectives and research questions to guide the study. This will help readers understand the purpose of the research and evaluate the effectiveness of the proposed method in achieving those objectives.
Author’s comment: The research question has been added
- While the paper mentions a comparative study and provides some quantitative results regarding power loss reduction and voltage improvement, it lacks a detailed analysis and interpretation of the findings. The paper should include a thorough discussion of the results, including any limitations or assumptions made, and provide insights into the implications and significance of the observed improvements.
Author’s comment: Results and discussion has been added and revised
- It is important to acknowledge and discuss the limitations of the proposed approach. This could include considerations such as scalability to larger grid systems, sensitivity to input parameters, computational efficiency, and generalizability to different network topologies. Additionally, the paper should address the applicability of the proposed method in real-world scenarios and discuss potential challenges in implementing the suggested solution.
Author’s comment: Thank you for the comment, but the research is ongoing and this can be considered for the future studies
- The paper briefly mentions economic models and methods to engage and reward users. However, it lacks a detailed discussion on the social and economic aspects of implementing the proposed solution. Considering the potential impact on electricity pricing, consumer behavior, and infrastructure costs, a more comprehensive analysis of the socio-economic implications would strengthen the paper.
- Hyperparameters of designed network must be included in a tabular form. You may refer to paper ‘Augmentation of decision tree model through hyper-parameters tuning for monitoring of cutting tool faults based on vibration signatures’
Author’s comment: Thank you for the suggestion tables has been added
- Was the algorithm trained using standard hyperparameters or were they altered?
- Comment on computational time and complexity in training of algorithm.
By addressing these points, the paper can provide a more thorough and comprehensive analysis of the proposed method and its potential contributions to the integration of EVs into the grid system.
Comments on the Quality of English Language
Moderate editing of English language required
Author’s comment: Errors has been corrected
Reviewer 4 Report
This manuscript is a study of power quality of an electrical grid with V2G functionalities. The manuscript considered only the benefits of the V2G functionalities to the power quality issues, which have been widely explored.
1. The technology reviews account for 50% of the manuscript, which has been widely reviewed and summarized. Instead, the authors should focus on the effects of EV integration on the power system operations.
2. The 3 scenarios used in this study are not practical. All the connected EVs supply active powers to the grid. There should be some EVs that do not participate in the grid supporting scheme.
3. What is “active electricity” in line 548?
N/A.
Author Response
REVIEWER FOUR COMMENTS
This manuscript is a study of power quality of an electrical grid with V2G functionalities. The manuscript considered only the benefits of the V2G functionalities to the power quality issues, which have been widely explored.
- The technology reviews account for 50% of the manuscript, which has been widely reviewed and summarized. Instead, the authors should focus on the effects of EV integration on the power system operations.
Author’s comment: Take you for the comments, summary has been made
- The 3 scenarios used in this study are not practical. All the connected EVs supply active powers to the grid. There should be some EVs that do not participate in the grid supporting scheme.
Author’s comment: Thank you for the comment, but the research is ongoing and this can be considered for the future studies
- What is “active electricity” in line 548?
Author’s comment: Error has been corrected
Comments on the Quality of English Language
N/A.
Round 2
Reviewer 1 Report
Paper can be accepted now.
Author Response
Thank you very much for your comment much appreciated.
Reviewer 2 Report
1-The quality of Figures 1-9 should be improved. 2-The paper includes many problems in language. Please modify the paper.
1-The paper includes many problems in language. Please modify the paper.
Author Response
Thank you very for your comment, the quality of figures has been improved and some are removed as the literature review was too long as the the reviewer comment.
The English errors has been corrected.
Reviewer 3 Report
Accept
Nil
Author Response
Thank you very much for your comment much appreciated.
Reviewer 4 Report
The main problem of the manuscript is the hypothesis and methodology, which have not been revised.
The minor issue is the structure, which contains 10 pages of the literature review. This overshadows the main study.
Author Response
Thank you very much for your comment much appreciated.
The chapters has been revised.
The literature review has been revised, some pages were removed.
Round 3
Reviewer 2 Report
Acceptable
Acceptable
Author Response
Thank you very much for your comment much appreciated.
Reviewer 4 Report
The structure of the manuscript has been improved.
Author Response
Thank you very much for your comment much appreciated.
