Network Reconfiguration Framework for CO₂ Emission Reduction and Line Loss Minimization in Distribution Networks Using Swarm Optimization Algorithms

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

In this study, a Network Reconfiguration Framework is developed for reducing CO2 emissions by uniformly distributing line current and reducing line loss in Distribution Networks through optimized switch operations during both system planning and operational stages. The application is performed on a real Distribution Network within Taipower. The comparisons are carried out considering several Swarm Optimization Algorithms. The paper is interesting. But some comments and suggestions for authors can be summarized in the attached file.

Comments for author File: Comments.pdf

Comments on the Quality of English Language

Moderate editing of English language required

Author Response

Dear Reviewer:

Thanks for your valuable comments on our paper, entitled "Network Reconfiguration Framework for CO₂ Emission Reduction and Line Loss Minimization in Distribution Networks Using Swarm Optimization Algorithms ". This paper has been revised according to your comments, as the attached file.

Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

The article by Lin et al. provides an approach to reducing CO2 emissions and minimizing line loss in active distribution networks (ADNs). Their method leverages swarm optimization algorithms (SOAs) for network reconfiguration (NR), demonstrating a practical application in the context of Taiwan's power distribution network, Taipower.

Questions and Suggestions:

How do the proposed algorithms perform in different network configurations, particularly in more complex or larger-scale networks than Taipower?

The paper could benefit from a more detailed comparison between the proposed swarm optimization approach and traditional NR methods. What are the specific advantages in terms of efficiency, cost, and reliability?

While focusing on CO2 emissions and line loss, does the framework consider the impact of NR on overall network reliability and safety, especially under fault conditions?

How scalable is the proposed framework in adapting to future changes in the network, such as the integration of renewable energy sources or changes in load patterns?

An economic analysis, including cost-benefit considerations and potential savings, would strengthen the case for practical implementation of the framework.

 

Further exploration in terms of scalability, comparison with traditional methods, and economic viability would enhance the robustness and applicability of the research.

Comments on the Quality of English Language

The article by Lin et al. provides an approach to reducing CO2 emissions and minimizing line loss in active distribution networks (ADNs). Their method leverages swarm optimization algorithms (SOAs) for network reconfiguration (NR), demonstrating a practical application in the context of Taiwan's power distribution network, Taipower.

Questions and Suggestions:

How do the proposed algorithms perform in different network configurations, particularly in more complex or larger-scale networks than Taipower?

The paper could benefit from a more detailed comparison between the proposed swarm optimization approach and traditional NR methods. What are the specific advantages in terms of efficiency, cost, and reliability?

While focusing on CO2 emissions and line loss, does the framework consider the impact of NR on overall network reliability and safety, especially under fault conditions?

How scalable is the proposed framework in adapting to future changes in the network, such as the integration of renewable energy sources or changes in load patterns?

An economic analysis, including cost-benefit considerations and potential savings, would strengthen the case for practical implementation of the framework.

 

Further exploration in terms of scalability, comparison with traditional methods, and economic viability would enhance the robustness and applicability of the research.

Author Response

Dear Reviewer:

Thanks for your valuable comments on our paper, entitled "Network Reconfiguration Framework for CO₂ Emission Reduction and Line Loss Minimization in Distribution Networks Using Swarm Optimization Algorithms ". This paper has been revised according to your comments, as the attached file.

Author Response File: Author Response.docx

Reviewer 3 Report

Comments and Suggestions for Authors

1) The purpose of the study, according to the authors, is to minimize losses in Taipower's electrical distribution network, improving the algorithm for energy discharges into the network and switches. The authors claim that they manage to reduce losses, and therefore improve the general productivity of the network, and that their method also reduces CO2 emissions. This last point is misleading: it is true, as the authors state, that in the current system analyzed the energy is not completely renewable and therefore there are CO2 emissions. By reducing losses, therefore, it is evident that these emissions also decrease. However, in a system with renewable generation this effect would disappear, and therefore this reduction in CO2 emissions is completely marginal in the study analyzed. The contribution of the study is simply to design a system to reduce losses in the electrical distribution system, and therefore the reference to emissions reduction should be qualified or eliminated completely.

 2) The results obtained are positive in the sense that a reduction in losses is effectively achieved. However, and according to the data displayed in Figure 16, these gains are small, something that should be made clear in the conclusions.

 3) Much of the article, specifically sections 2 and 3, are unnecessary and should be summarized by referring the reader to the appropriate references, since it is known and published Literature.

 4) The connection of this study to the general topic of sustainability is unclear. The article is, rather, a pure work of engineering optimization. The authors should explain much more clearly why they have selected this journal, or send it to another more appropriate with the content of the study.

 5) The English, finally, is reasonably understandable, though many expressions are strange, and I recommend a review by a native English speaker.

Comments on the Quality of English Language

The English is reasonably understandable, though many expressions are strange, and I recommend a review by a native English speaker.

Author Response

Dear Reviewer:

Thanks for your valuable comments on our paper, entitled "Network Reconfiguration Framework for CO₂ Emission Reduction and Line Loss Minimization in Distribution Networks Using Swarm Optimization Algorithms ". This paper has been revised according to your comments, as the attached file.

Author Response File: Author Response.docx

Reviewer 4 Report

Comments and Suggestions for Authors

The research paper titled "Network Reconfiguration Framework for CO2 Emission Reduction and Line Loss Minimization in Distribution Networks Using Swarm Optimization Algorithms" presents a comprehensive study on enhancing the efficiency of active distribution networks (ADNs) through network reconfiguration (NR) to reduce CO2 emissions and line losses. The framework developed in this study is applied to the ADN of Taipower, utilizing a combination of swarm optimization algorithms (SOA) and OpenDSS, an electric power distribution system simulator.

Advantages:

1. Innovative Approach: The integration of SOAs with ADNs for CO2 emission reduction is a novel approach. The use of swarm intelligence algorithms demonstrates a significant advancement in addressing complex optimization problems in large-scale ADNs.
2. Comprehensive Modeling: The paper effectively models the distribution feeder using OpenDSS, incorporating real data from the Distribution Mapping Management System and Supervisory Control and Data Acquisition system of Taipower. This realistic modeling approach enhances the applicability and accuracy of the framework.
3. Radial Topology Maintenance: The NR optimization algorithm is well-designed to maintain a radial topology, crucial for the coordination of protection relays and grid code compliance.
4. User-Friendly Interface: The developed Generic Active Distribution Network Reconfiguration Framework (GADNRF) is user-friendly and offers a wide range of functionalities for ADN operators, including multiple simulation modes and the ability to select different swarm algorithms.
5. Demonstrated Effectiveness: The numerical results showcase the framework's effectiveness in reducing CO2 emissions and line losses. The detailed analysis of the results from simulations of a single and multiple substations provides practical insights into the framework's performance.

Disadvantages:

1. Complexity in Algorithm Implementation: The NR problem addressed is inherently complex, and the implementation of the swarm intelligence algorithms might pose challenges in terms of computational efficiency and scalability, especially for large-scale networks.
2. Data Management Challenges: The framework requires extensive data, including long-term load data, photovoltaic and wind turbine generation data, and switch status data. Managing and processing this big data efficiently is a significant challenge.
3. Limited Simulations: The paper only presents simulations and analyses for two cases due to paper length limitations. A broader range of simulations across various scenarios would provide a more comprehensive evaluation of the framework's effectiveness.

Recommendations for Improvement:

1. Enhancing Computational Efficiency: Investigate methods to optimize the computational efficiency of the swarm optimization algorithms, especially for large-scale ADNs. Techniques such as parallel processing or employing more efficient data structures could be explored to handle the large data sets and complex calculations.

2. Expanding Simulation Scenarios: Conduct additional simulations across diverse scenarios, including different types of ADNs and varying environmental conditions. This would test the robustness and adaptability of the framework under varied real-world conditions.

3. Incorporating Real-Time Data Handling: Enhance the framework to handle real-time data more effectively. This could involve integrating advanced data analytics and machine learning techniques to predict and manage dynamic changes in the network.

4. Improving User Interface (UI) for Greater Accessibility: While the UI is user-friendly, additional features such as guided tutorials, interactive visualizations, and customizable dashboards could be added to make it more accessible to operators with varying levels of expertise.

5. Addressing Data Privacy and Security: Given the extensive use of data from various sources, the framework should incorporate robust data privacy and security measures to protect sensitive information and ensure compliance with regulatory standards.

6. Environmental Impact Assessment: Include a comprehensive assessment of the environmental impacts of the proposed NR strategies beyond CO2 emission reduction. This could involve studying impacts on local ecosystems, wildlife, and the broader implications of the implemented changes.

7. Scalability Assessment: Conduct a thorough scalability analysis to evaluate how the framework performs as the size of the network increases. This is crucial for its application in larger and more complex ADNs.

8. Feedback Loop for Continuous Improvement: Implement a feedback mechanism within the framework to continuously monitor, analyze, and improve its performance based on real-world operational data.

By addressing these recommendations, the framework's efficiency, applicability, and impact can be significantly enhanced, making it a more robust tool for optimizing ADNs for CO2 emission reduction and line loss minimization. Hence, I recommend the major revision of the presented research paper.

 

Comments on the Quality of English Language

Focusing on the quality of written English, structure, grammar, vocabulary, style, and consistency, the following observations and recommendations can be made:

1. Clarity and Comprehensibility:

• The paper utilizes technical and specialized vocabulary appropriate for its subject matter, which is suitable for its target audience of professionals in electrical engineering and related fields​​.
• Some sentences are lengthy and complex, potentially challenging for readers who are not experts in the field. Simplifying these sentences could enhance readability without sacrificing technical accuracy.

2. Grammar and Sentence Structure:

• The paper generally follows standard grammatical rules. However, there are instances where sentence structure could be improved for better clarity. For example, the use of semicolons and commas in some complex sentences seems inconsistent, which can occasionally make the text difficult to follow​​.
• Some sentences appear to be overly complex or run-on, which might hinder smooth reading. Breaking down these sentences into shorter, more concise statements could improve understanding​​.

3. Consistency and Style:

• The paper maintains a formal and academic tone throughout, which is appropriate for a research paper in this field.
• Consistency in terminology and phrasing is generally maintained, which is crucial for technical accuracy and coherence.

4. Vocabulary and Technical Language:

• The technical language used is appropriate for the subject matter, reflecting a high level of expertise in the field of electrical engineering and optimization algorithms​​.
• The use of specialized terms is consistent, aiding in the paper's credibility and precision.

5. Structure and Organization:

• The paper is well-organized, with a clear abstract, introduction, methodology, results, and conclusion sections. This structure is effective for presenting complex research in an accessible way​​.
• Headings and subheadings are used effectively to guide the reader through the various parts of the paper.

Areas for Improvement:

• Simplification of Complex Sentences: Simplifying complex sentences could enhance the overall readability without compromising the technical details.
• Consistency in Punctuation: Attention to consistent punctuation, especially in complex and lengthy sentences, would aid in readability and comprehension.

In conclusion, the paper is well-written from a linguistic standpoint, particularly in its use of technical language and overall structure. Minor improvements in sentence structure and punctuation could further enhance its clarity and accessibility.

 

 

Author Response

Dear Reviewer:

Thanks for your valuable comments on our paper, entitled "Network Reconfiguration Framework for CO₂ Emission Reduction and Line Loss Minimization in Distribution Networks Using Swarm Optimization Algorithms ". This paper has been revised according to your comments, as the attached file.

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

It can be accepted.

Comments on the Quality of English Language

It can be accepted.

Reviewer 2 Report

Comments and Suggestions for Authors

While the paper presents a framework using swarm optimization algorithms, it lacks a thorough analysis of the chosen algorithms' efficacy compared to other potential methods. This raises questions about the robustness and optimization efficiency of the selected algorithms. The paper primarily focuses on simulations with data from Taipower's ADNs. This limited scope may not accurately represent diverse real-world scenarios, potentially reducing the generalizability and applicability of the findings. The paper does not provide a comprehensive comparative analysis with other existing frameworks or methodologies. This is crucial for validating the effectiveness and efficiency of the proposed framework over existing solutions.

Comments on the Quality of English Language

While the paper presents a framework using swarm optimization algorithms, it lacks a thorough analysis of the chosen algorithms' efficacy compared to other potential methods. This raises questions about the robustness and optimization efficiency of the selected algorithms. The paper primarily focuses on simulations with data from Taipower's ADNs. This limited scope may not accurately represent diverse real-world scenarios, potentially reducing the generalizability and applicability of the findings. The paper does not provide a comprehensive comparative analysis with other existing frameworks or methodologies. This is crucial for validating the effectiveness and efficiency of the proposed framework over existing solutions.

Reviewer 3 Report

Comments and Suggestions for Authors

The corrections introduced according to my comments are sufficient.

Reviewer 4 Report

Comments and Suggestions for Authors

Thank you.