Review Reports

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This manuscript studies on the co-optimization of device dispatch and network reconfiguration for active distribution networks (ADNs). The work is highly relevant to the field of sustainable energy systems, addressing the critical intersection of technical efficiency (loss reduction) and environmental impact (carbon emission reduction) within a unified, rigorous optimization framework. The use of a DistFlow-based MISOCP model solved with a commercial solver provides verifiable optimality gaps, which is a significant strength compared to heuristic approaches. The results on the IEEE 33-bus system demonstrate substantial improvements. The manuscript is generally clear but would benefit from revisions before it can be considered for publication.

1. The model for the PHEV aggregator is simplistic. Constraining it to a single-period, symmetric active power injection ignores the temporal coupling of energy constraints (e.g., state-of-charge dynamics). The assumption of zero reactive power exchange should be better justified, as modern V2G chargers can provide reactive power support. Please discuss how this simplification affects the realism and generalizability of the results.
2. The results are compelling when compared against the authors' own cases (1, 2, and 3). However, to firmly establish the contribution, the performance of the proposed method should be compared against a benchmark algorithm from the literature (e.g., a established heuristic or another MISOCP formulation with a different objective) on the same test system. This would better contextualize the claimed advancements.
3. The manuscript focuses solely on the optimal steady-state configuration without considering the practical costs and limitations of reconfiguration. There is no discussion of switching operations, their associated costs, or potential wear-and-tear on switches. Acknowledging this limitation and suggesting its incorporation for future multi-period studies would strengthen the discussion section.
4. The literature review and the method should be improved and needs to add more the differences. The authors should add more recently published related papers about low-carbon development as complementary references, such as: doi.org/10.1080/09640568.2021.1945552.
5. Key parameters like electricity price (c_e = 620 RMB/MWh), carbon price (c_c = 95.96 RMB/tCO2), and emission factors are stated without a clear justification or reference to a specific regional context or time frame. Please provide references or a rationale for selecting these values, as they directly influence the weighting in the objective function and the resulting dispatch.
6. The statement that CPLEX "yields a global optimum for the convex-relaxed mixed-integer model" (e.g., in the Abstract and Section 2.4) is technically correct but could be misleading for readers not deeply familiar with convex relaxations. It should be explicitly clarified that global optimality is guaranteed for the relaxed problem, and the quality of this solution for the original non-convex problem is contingent on the tightness of the relaxation.
7. The limitations section (4.5) is well-identified but could be more critical. The single-period, deterministic analysis is a significant simplification. The authors should elaborate on how the absence of uncertainty (in renewable generation, load, PHEV availability) might affect the robustness of the proposed dispatch and reconfiguration strategy in a real-world setting.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

This paper presents a study on the joint optimization of the topology and operating modes of active distribution networks (ADNs) with a high share of distributed generation. The authors develop a model based on the DistFlow equations, reduced to a second-order mixed-integer programming (MISOCP) problem. The goal is to minimize both network active power losses and CO₂ emissions, taking into account electricity prices and a carbon tax. The model coordinates the operation of photovoltaic plants, wind turbines, microturbines, aggregated plug-in hybrid electric vehicles with V2G functionality, as well as reactive power (static compensators and capacitor banks). A 33-node IEEE test network demonstrates the effectiveness of the proposed approach: device coordination alone reduced losses by 81.18% and emissions by 85.62% relative to the baseline scenario. Additional use of network topology reconfiguration enabled a reduction in losses by 89.90% and emissions by 89.19%. The results are confirmed by the small optimality gap (~10⁻⁷) after SOC relaxation, which indicates the high accuracy of the proposed convex approximation.

 

However, it would be necessary to clarify a number of comments that are available to the article:

1. The "Results" section does not specify whether the PV/WT generation and load data are real (measured) or synthetic (simulated). To ensure reproducibility and assess practical significance, it is necessary to clearly indicate the source of the data: real measurements, typical daily profiles, or generated scenarios.
2. The article presents a comparison of only three scenarios (baseline, with devices, and with devices and reconfiguration) on a single test network. There is no comparison of the performance of the proposed MISOCP approach with alternative methods from Table 1 (e.g., heuristic or artificial intelligence methods) for key metrics: solution time, solution quality on large networks, and robustness to initial conditions.
3. The weighting coefficients w1 and w2 in the objective function (9) are calculated post hoc, based on the baseline scenario (Case 1). This is methodologically questionable, as it makes the objective function dependent on a specific "bad" initial state of the system. No sensitivity analysis of the results to the choice of these weights or to the use of alternative scalarization methods (e.g., the ε-constraint) has been performed.
4. Table 3 and Figures 7-12 show the optimal settings for devices in Cases 2 and 3. However, there is no analysis of why the algorithm produced these specific values. For example, why is the PV at node 6 producing at its maximum (0.8 MW), while at node 31 it only produces 0.4753 MW? Is this related to local voltage balance or current limitation in the branches? Such an analysis would deepen the understanding of the model's operation.
5. Studies that provide verified data on losses and power factors for specific technological mechanisms and the grid as a whole could be considered. These data (node ​​loads, loss factors, cos φ) are critical input parameters for constructing a reliable optimization model (https://doi.org/10.1016/j.energy.2024.133293, https://doi.org/10.18799/24131830/2021/10/3394). An approach based on experimental data and statistics can serve as a tool for verifying the results of the MISOCP model on real industrial facilities. Comparing the loss reductions predicted by the model with those actually measured after implementing the solutions would increase the evidentiary value of the study. Therefore, supplementing the article with a methodology for calculating and auditing losses will give it greater practical depth, linking high-level mathematical optimization with applied energy efficiency problems at real industrial enterprises and will allow for a more informed selection of goals and constraints for the optimization model.
6. The model is static. To assess the actual annual or daily effect, integration into a multi-period model that accounts for the variability of renewable energy sources and load profiles is necessary. The authors note this, but do not even provide a conceptual description of such an expanded model.
7. The emissions calculation (7) considers only the grid and microturbines, assuming zero emissions for PV, WT, and PHEV. This is valid for the operational phase, but for a full life cycle carbon assessment (LCA), this approach is simplified.
8. Figure 14 shows that voltages in Case 3 have improved. However, there is no quantitative comparison of voltage quality metrics, such as the voltage deviation index.
9. Modeling PHEVs as a simple aggregated source/load with zero reactive power is a strong simplification. It does not take into account the fleet's state-of-charge (SOC) constraints or connection stochasticity, which are critical for multi-period optimization. In the current static formulation, this is not a drawback, but requires explicit specification in the constraints.
10. The percentage reductions in losses and emissions relative to the base case (Case 1) are presented. However, for a specialist, it would also be more informative to see the absolute values of the reduction in operating costs C(x) from equation (8) in monetary terms for each scenario.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Dear Authors,

your paper is good; its research section (methodology, results, and discussions) is, in my opinion, good. All the formulas are good, and the figures are legible.

As your reviewer, I have a comment on section 1. The introduction is too long. Please separate a significant portion of the text and Table 1 from it and create a new section: Background of research. 

In the Introduction section, no more than one page, add (highlight the research objective) and research questions in the form of RQ1...? RQ2.... etc. (the number of questions is up to you). In the new Background... section, add a broader review of the literature. You can also propose a research hypothesis based on the literature.

Strongly emphasize the research gap.

Best wishes

Reviewer

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The information in the 29th reference is incorrect and needs to be corrected to:

Du, M., Antunes, J., Wanke, P., & Chen, Z. (2022). Ecological efficiency assessment under the construction of low-carbon city: a perspective of green technology innovation. Journal of Environmental Planning and Management, 65(9), 1727-1752.

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Author Response

Comments 1: The information in the 29th reference is incorrect and needs to be corrected to: Du, M., Antunes, J., Wanke, P., & Chen, Z. (2022). Ecological efficiency assessment under the construction of low-carbon city: a perspective of green technology innovation. Journal of Environmental Planning and Management, 65(9), 1727–1752.

Response 1: Thank you for pointing this out. We agree with this comment. Therefore, we have corrected the bibliographic information of Reference [29] to the accurate citation provided by the reviewer. This revision can be found in the revised manuscript on Page 39, Lines 1251–1252, where the reference entry is updated in the reference list: “Du, M., Antunes, J., Wanke, P., & Chen, Z. (2022). Ecological efficiency assessment under the construction of low-carbon city: a perspective of green technology innovation. Journal of Environmental Planning and Management, 65(9), 1727–1752.”

Reviewer 2 Report

Comments and Suggestions for Authors

This paper presents a comprehensive study on the joint optimization of topology and dispatch in active distribution networks, accounting for losses and carbon emissions. The authors propose a model based on MISOCP using DistFlow, which is a modern and methodologically sound approach. After a thorough revision, taking into account comments, the paper has become significantly more comprehensive, transparent, and substantiated.

1. Source of generation and load data. The authors explicitly stated that data from a standard IEEE 33-bus test feeder, not in-kind measurements, is used. The text has been updated to clearly state that all studies are conducted for a static snapshot, without considering time series or stochastic scenarios. This improves reproducibility but limits the practical interpretation of the results under real-world variability.
2. Comparison with alternative methods. In response to this comment, the authors added a section comparing the proposed MISOCP method with metaheuristics (GA, DE, GWO, ABC, TS, TLBO) and the Branch Exchange heuristic. The results show that MISOCP provides the best objective function value with competitive solution times. This strengthens the method's position as an efficient and accurate tool.
3. Methodology for Determining Weighting Coefficients. The authors acknowledged a methodological vulnerability and added a new section 4.5 with a sensitivity analysis and Pareto frontier construction. This demonstrates the robustness of the results to weight changes and provides an alternative scalarization method through ε-constraints, significantly enhancing the scientific rigor of the work.
4. Analysis of Optimal Device Settings. A detailed explanation has been added to the text explaining why different generation nodes operate at different power levels. The authors attribute this to voltage constraints, reverse flows, and network topology, which deepens the understanding of the model's operation and improves its interpretability.
5. Data Verification and Loss Audit Methodology. A new subsection 3.5 has been added, describing the methodology for calibration, audit, and verification of losses based on real measurements. Suggested references are also included. This is an important addition that links the theoretical model to practical applications and increases the credibility of the results.
6. Static Nature of the Model. The authors expanded the section on limitations by adding a conceptual description of the multi-period extension of the model. Although an implementation is not provided, this description provides direction for future research and demonstrates an awareness of the limitations of the current work.
7. Accounting for Emissions Only during the Operational Phase. The authors explained that the model used only accounts for direct emissions during the operational phase, which is acceptable for operational optimization. They also note that a full life cycle assessment (LCA) is beyond the scope of the study, which is an appropriate limitation.
8. Quantitative Assessment of Voltage Quality. In response to this comment, voltage deviation indices (VDIabs, VDIsq, MaxAbsDev, etc.) have been introduced, and a table with a quantitative comparison has been added. This significantly improves the voltage quality analysis and makes it more objective.
9. Simplified PHEV model. The authors explicitly stated that within the static model, PHEVs are aggregated as active power with zero reactive component, and SOC dynamics are not taken into account. This simplification is adequate for this study, but it indicates the possibility of expansion in future work.
10. Absolute values of cost reductions. Absolute values of operating costs C(x) in monetary terms have been added to the text for all three scenarios, improving the economic interpretation of the results and making them more illustrative for specialists.

Recommendations for further improvement

1. It is recommended that future work implement a multi-period extension of the model taking into account the variability of renewable energy sources and load, as well as the SOC dynamics of PHEVs.
2. It would be useful to add an analysis of resilience to generation and demand uncertainties, for example, through stochastic or robust programming.
3. Applying the model to real networks with in-kind data would increase the practical value of the study.
4. Including reactive power and PHEV charging dynamics would make the model more realistic.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Dear Authors, Now your paper is better.

It should be published.

Best wishes

Reviewer

Author Response

We greatly appreciate the reviewer’s encouraging comments and support for publication and best wishes to you!

Round 3

Reviewer 1 Report

Comments and Suggestions for Authors

Accept in present form.