A Multi-Temporal Regulation Strategy for EV Aggregators Enabling Bi-Directional Energy Interactions in Ancillary Service Markets for Sustainable Grid Operation

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The manuscript proposes a structured multi-stage scheduling framework for EVs aggregators to participate in ancillary service markets. The work aims to maximize operational revenue through coordinated real-time and day-ahead scheduling. While the model is mathematically good and that the work has good potentials, the paper requires key improvements in recent literature inclusion, visualization quality, model interpretability, and writing tone to elevate its scholarly and practical value. Below are my feedback for the authors: 

1. The paper draws on relatively older references to justify its novelty. However, the field of EV aggregator-based regulation strategies has seen substantial growth in 2024 and 2025. Key studies in this "hot research topics" are notably missing. I'd recommend the authors to revise the literature and focus on the newly published works, something +2024, and make possible comparisons. Below are a list of recommended works (not imposed by any mean):

• 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.
• Shern, S. J., Sarker, M. T., Haram, M. H. S. M., Ramasamy, G., Thiagarajah, S. P., & Al Farid, F. (2024). Artificial Intelligence Optimization for User Prediction and Efficient Energy Distribution in Electric Vehicle Smart Charging Systems. Energies, 17(22), 5772.
• Liu, Z. F., Liu, Y. Y., Jia, H. J., Jin, X. L., Liu, T. H., & Wu, Y. Z. (2025). Bi-level energy co-optimization of regional integrated energy system with electric vehicle to generalized-energy conversion framework and flexible hydrogen-blended gas strategy. Applied Energy, 390, 125868.

2. The text structure is dense and conceptually rich but lacks an illustrative figure summarizing the workflow and contributions. I'd highly recommend to add a schematic illustration in the introduction to help the readers visualize their workflow. 

3. Improve visual quality and clarity of some of your figures. For example, in figs 3 and 4, improve line quality and axis labeling. Response Mode diagrams could be redrawn in vector format with consistent labeling of states (charging, idle, discharging). Also in figs 15-17, I recommend replacing plain line charts with dual-axis formats (e.g., EV count vs. revenue), and annotate breakpoints where revenue saturates.

4. Throughout the manuscript, first-person phrases (e.g., “we propose,” “our model,” “we analyze”) appear frequently, and this is highly undesirable in academic writings. To maintain a formal and scholarly tone, revise to impersonal structures such as "This study proposes…”. 

5. While the core optimization model (Equations 21–27) is well-structured, it suffers from incomplete explanation of certain key assumptions. For example: 

• Equation (14)’s degradation model: The parameters appear empirical. Are they fitted from experimental data, and if so, what battery type and temperature conditions were assumed?
• Equations (9) and (10) model response willingness via Monte Carlo methods, but the choice of distribution for willingness thresholds θi is not stated. Clarify whether these are normal, uniform, or beta distributions.
• The link between subsidy level and participation rate in Equations (18)–(19) assumes monotonic behavior—justify this assumption, as user behavior can show plateau or threshold effects.

6. Terms like EVA, CBDR, DBDR, and ancillary service market are used extensively. Include a glossary at the beginning or end of the work for more clarity to your readers.

 

 

Author Response

Comment 1:The paper draws on relatively older references to justify its novelty. However, the field of EV aggregator-based regulation strategies has seen substantial growth in 2024 and 2025. Key studies in this "hot research topics" are notably missing. I'd recommend the authors to revise the literature and focus on the newly published works, something +2024, and make possible comparisons. Below are a list of recommended works (not imposed by any mean):

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.

Shern, S. J., Sarker, M. T., Haram, M. H. S. M., Ramasamy, G., Thiagarajah, S. P., & Al Farid, F. (2024). Artificial Intelligence Optimization for User Prediction and Efficient Energy Distribution in Electric Vehicle Smart Charging Systems. Energies, 17(22), 5772.

Liu, Z. F., Liu, Y. Y., Jia, H. J., Jin, X. L., Liu, T. H., & Wu, Y. Z. (2025). Bi-level energy co-optimization of regional integrated energy system with electric vehicle to generalized-energy conversion framework and flexible hydrogen-blended gas strategy. Applied Energy, 390, 125868.

Response 1:

First of all, the authors sincerely appreciate and fully agree with your valuable comments. As you rightly pointed out, the topic addressed in this paper has received considerable attention in recent years, and it is indeed necessary to supplement the manuscript with a review and comparison of high-quality literature published since 2024. In response to your suggestion, we have incorporated a review and citation of the three references you mentioned in the revised manuscript (see Lines 55-67, [4]-[6], highlighted), and additionally, we have summarized representative recent studies in this field and presented them in Table 1. Comparison Table of Methods in the revised manuscript.

Once again, we are grateful for your thoughtful comment.

 

Comment 2:The text structure is dense and conceptually rich but lacks an illustrative figure summarizing the workflow and contributions. I'd highly recommend to add a schematic illustration in the introduction to help the readers visualize their workflow.

Response 2:

Thanks for your insightful and constructive comments. In response to your valuable suggestion, we have added a carefully designed figure that concisely illustrates the workflow and key contributions of the study. This visual aid aims to provide readers with a clear and intuitive understanding of the overall methodology and main findings, thereby improving the clarity and conceptual structure of the manuscript. Please refer to Figure 2 in the revised manuscript, titled Overall Structure Diagram of the Paper. We believe that this addition to the introduction section significantly enhances the presentation and readability of our work.

 

Comment 3:Improve visual quality and clarity of some of your figures. For example, in figs 3 and 4, improve line quality and axis labeling. Response Mode diagrams could be redrawn in vector format with consistent labeling of states (charging, idle, discharging). Also in figs 15-17, I recommend replacing plain line charts with dual-axis formats (e.g., EV count vs. revenue), and annotate breakpoints where revenue saturates.

Response 3:

Thank you for pointing out this very important issue. In response, we have improved the quality of Figure 4 in the revised version by refining the line styles, increasing line thickness, and enhancing color saturation to improve overall visibility and clarity. Additionally, the schematic diagram illustrating the response modes has been redrawn as a high-resolution vector image in Figure 3 in the revised version , ensuring optimal sharpness and presentation in the revised manuscript. The labels for the different states (charging, idle, and discharging) have been standardized, with clear visual distinction between the idle state and the charging/discharging states. These enhancements are intended to significantly improve the readability and visual consistency of the figures.

As for problems in Figures 15-17, we sincerely appreciate your thoughtful suggestion to replace the standard line charts in Figures 15-17 with a dual-axis format (e.g., electric vehicle count versus revenue). After careful consideration, however, we believe that a dual-axis representation may not be appropriate for these particular figures. This is due to the highly dynamic and interdependent nature of the aggregator's revenue, which is not solely determined by the number of electric vehicles, but also by temporal variations, market demand fluctuations, and real-time strategic adjustments. As the number of electric vehicles evolves over time, the corresponding revenue is influenced by multiple interacting factors. Therefore, a dual-axis chart could oversimplify or obscure these complex relationships, potentially leading to misinterpretation of the underlying dynamics. In response to your suggestion, we have revised the figures( Figures 15-17 in the revised version) to include annotations indicating the maximum revenue range and the convergence interval in the revised manuscript. These additions serve to clearly highlight the saturation point of the revenue curve, offering readers a more intuitive understanding of revenue progression and the point at which it stabilizes. We believe these enhancements significantly improve the clarity and explanatory value of the figures.

 

Comment 4:Throughout the manuscript, first-person phrases (e.g., “we propose,” “our model,” “we analyze”) appear frequently, and this is highly undesirable in academic writings. To maintain a formal and scholarly tone, revise to impersonal structures such as "This study proposes…”.

Response 4:

Thank you for pointing out the shortcomings in our writing. We fully recognize the importance of maintaining a formal and objective tone in academic writing, as you have rightly emphasized. In response to your observation regarding the frequent use of first-person pronouns, we have revised the manuscript to adopt a more scholarly and impersonal style. For example, phrases such as “we propose” have been replaced with more formal alternatives like “this study proposes”, thereby enhancing the academic rigor, neutrality, and stylistic consistency of the paper.

 

Comment 5:While the core optimization model (Equations21–27) is well-structured, it suffers from incomplete explanation of certain key assumptions. For example: Equation (14)’s degradation model: The parameters appear empirical. Are they fitted from experimental data, and if so, what battery type and temperature conditions were assumed?Equations (9) and (10) model response willingness via Monte Carlo methods, but the choice of distribution for willingness thresholds θi is not stated. Clarify whether these are normal, uniform, or beta distributions.

Response 5:

The authors sincerely appreciate your insightful comments, which have greatly contributed to improving the clarity and rigor of the manuscript. In response to your suggestions, we have made the following revisions:

• The degradation model parameters presented in Equation (14) are derived from empirically fitted results based on experimental studies of Nickel Cobalt Manganese (NCM) lithium-ion batteries operating under standard ambient temperature conditions (25°C). These parameters reflect typical degradation behaviors observed in electric vehicle battery systems and are widely accepted for modeling battery dynamics in demand response applications. This clarification has now been explicitly added to Section 3.5 of the revised manuscript (Lines 406-413,highlighted).
• Regarding the Monte Carlo-based modeling approach in Equations (9) and (10), the psychological response threshold θ_iis assumed to follow a Beta distribution over the normalized interval [0, 1], with shape parameters α = 2 and β = 5.This distribution was chosen for its flexibility in representing heterogeneous user behavior and varying levels of price sensitivity. We have elaborated on this modeling assumption in Section 3.3 and provided a rationale along with a graphical illustration in the appendix to enhance transparency and reader understanding (Lines 325-331 highlighted).

 

Comment 6:The link between subsidy level and participation rate in Equations (18)–(19) assumes monotonic behavior justify this assumption, as user behavior can show plateau or threshold effects.

Response 6:

The authors sincerely appreciate your insightful comments. We have addressed the concerns raised as follows:

The monotonic relationship assumed between the subsidy level and user participation rate in Equations (18)-(19) is intended as a tractable and analytically convenient approximation of user behavior in response to short-term economic incentives. While we fully acknowledge that real-world user responses may exhibit threshold effects, saturation phenomena, or nonlinear dynamics, we believe that the monotonic assumption is reasonable within the context of intra-day decision-making, which is the primary focus of this study. To clarify this point, we have added explanatory statements in both Section 3.5 (Lines 432-437) and the limitations section (Lines 807-839), explicitly acknowledging this simplification and outlining potential directions for future refinement.

 

Comment 7:Terms like EVA, CBDR, DBDR, and ancillary service market are used extensively. Include a glossary at the beginning or end of the work for more clarity to your readers.

Response 7:

Thank you for pointing out this very important issue. We acknowledge that terms such as EVA, CBDR, DBDR, and ancillary service market are used frequently throughout the manuscript, which may present challenges for some readers. To improve clarity and enhance the accessibility of the manuscript, we have included a glossary that provides concise and precise definitions of these specialized terms. This glossary has been added as Table 2 (Line 147 in the revised version).

Reviewer 2 Report

Comments and Suggestions for Authors

Comments

 

This study explores a critical and current issue in the integration of electric vehicles (EVs) within smart grid systemsnamely, the optimization of electric vehicle aggregators (EVAs) for participation in ancillary service markets. It introduces a multi-temporal control approach that combines both charging-based and discharging-based demand response (CBDR and DBDR). The proposed strategy integrates factors such as user participation willingness, energy constraints, and time-based scheduling. Simulation results are provided to demonstrate the model’s effectiveness in enhancing EVA performance.

I have some comments:

1. Literature review: Please add a comparative table for related methods.
2. It is essential to clarify novelty and scope compared to past work (Abstract, Contribution) .
3. You need to discuss EVA’s performance in large-scale deployments.
4. While the proposed framework demonstrates robustness, its scalability in real-world applications involving thousands of EVs remains uncertain. The current simulations are limited to a fleet of 100 vehicles. It is important to clarify whether the methodology can be extended to larger-scale scenarios, and if any computational limitations or performance challenges might emerge during such expansion.
5. For figures try to ameliorate readability and consistent legends.

 

Author Response

Comment 1:Literature review: Please add a comparative table for related methods.

Response 1:

The authors sincerely appreciate your insightful comments, which have been invaluable in improving the quality of our manuscript. In the revised version, we have carefully added a detailed and representative comparative table, ensuring that the selected content is both appropriately comparable and illustrative. This addition significantly enhances the coherence and readability of the literature review and strengthens the overall scholarly contribution of the paper. The comparative literature table is presented as Table 1 (Line 115 in the revised version).

 

 

Comment 2:It is essential to clarify novelty and scope compared to past work (Abstract, Contribution) .

Response 2:

The authors sincerely thank the reviewer for the valuable suggestions regarding the abstract and introduction sections. In response to your feedback, we have systematically refined both sections to better emphasize the core innovations of this study and its advancements over prior work. The specific modifications to the abstract can be found in lines 9-23 of the revised manuscript.

In the abstract, we have succinctly presented the research background, highlighting the critical role of demand-side resources in ancillary service markets amid increasing load volatility and uncertainty. We introduced the proposed multi-stage operational strategy framework, which is based on electric vehicle power and energy response constraints and incorporates user response heterogeneity as well as multi-period peak regulation requirements. The optimization model and simulation results demonstrate substantial improvements in aggregator revenue and regulation capacity achieved through different demand response strategies. In particular, the combined CBDR and DBDR approach is shown to significantly enhance both economic benefits and grid flexibility.

In the introduction, we further clarified the study’s key innovations, including the development of a multi-stage operational strategy, the modeling of nonlinear user response characteristics, and the comprehensive consideration of multi-period peak regulation needs. We emphasized how the proposed joint-response strategy considerably improves regulation capacity, revenue stability, and overall system performance compared to conventional single-response approaches. These revisions aim to help readers more clearly understand the research background, significance, and contributions, thereby enhancing the manuscript’s logical coherence and persuasive strength. The specific modifications to the introduction are located in lines 42-67 and lines 129-146 of the revised version.

Once again, we deeply appreciate your thorough and constructive comments. Your guidance has been instrumental in improving the quality and clarity of our paper.

 

Comment 3:You need to discuss EVA’s performance in large-scale deployments.

Response 3:

Thank you for your valuable suggestion. We agree that it is necessary to discuss the implications of large-scale EVA deployment scenarios. In response, the authors have added a relevant discussion in the revised manuscript (Lines 817-838), outlining potential directions for further investigation in future work. Nevertheless, this study remains focused on developing a decoupled, behaviorally driven scheduling framework tailored to medium-scale aggregation contexts. The proposed method provides a sustainable foundation for subsequent model extensions and validations targeting ultra-large EVA networks. Once again, we sincerely appreciate your insightful comments.

Comment 4:While the proposed framework demonstrates robustness, its scalability in real-world applications involving thousands of EVs remains uncertain. The current simulations are limited to a fleet of 100 vehicles. It is important to clarify whether the methodology can be extended to larger-scale scenarios, and if any computational limitations or performance challenges might emerge during such expansion.

Response 4:

The authors sincerely appreciate the reviewer’s thoughtful and insightful comments. We fully acknowledge the importance of evaluating the scalability of the proposed framework, particularly in scenarios involving large-scale electric vehicle (EV) deployments.

In the current study, simulations were limited to a fleet of 100 EVs due to computational constraints and the need to ensure clarity and interpretability of results within a controlled environment. Nevertheless, the proposed model is designed with a modular architecture that is inherently well-suited for parallel computing. Preliminary profiling indicates that while computational complexity increases with the number of EVs, the core optimization framework-formulated as a mixed-integer linear program (MILP)-remains tractable for larger-scale applications when supported by appropriate solver configurations (e.g., warm starts, cutting-plane strategies) and sufficient computational resources. This clarification has been added in lines 587-598 and lines 817-838 of the revised manuscript.

 

Comment 5:.For figures try to ameliorate readability and consistent legends.

Response 5:

Thank you for pointing out this very important issue. In response, we have improved the quality of Figure 4 in the revised version by refining the line styles, increasing line thickness, and enhancing color saturation to improve overall visibility and clarity. Additionally, the schematic diagram illustrating the response modes has been redrawn as a high-resolution vector image in Figure 3 in the revised version , ensuring optimal sharpness and presentation in the revised manuscript. The labels for the different states (charging, idle, and discharging) have been standardized, with clear visual distinction between the idle state and the charging/discharging states. These enhancements are intended to significantly improve the readability and visual consistency of the figures.

As for problems in Figures 15-17, we sincerely appreciate your thoughtful suggestion to replace the standard line charts in Figures 15-17 with a dual-axis format (e.g., electric vehicle count versus revenue). After careful consideration, however, we believe that a dual-axis representation may not be appropriate for these particular figures. This is due to the highly dynamic and interdependent nature of the aggregator's revenue, which is not solely determined by the number of electric vehicles, but also by temporal variations, market demand fluctuations, and real-time strategic adjustments. As the number of electric vehicles evolves over time, the corresponding revenue is influenced by multiple interacting factors. Therefore, a dual-axis chart could oversimplify or obscure these complex relationships, potentially leading to misinterpretation of the underlying dynamics. In response to your suggestion, we have revised the figures( Figures 15-17 in the revised version) to include annotations indicating the maximum revenue range and the convergence interval in the revised manuscript. These additions serve to clearly highlight the saturation point of the revenue curve, offering readers a more intuitive understanding of revenue progression and the point at which it stabilizes. We believe these enhancements significantly improve the clarity and explanatory value of the figures.

Reviewer 3 Report

Comments and Suggestions for Authors

This is an interesting paper. However, there are some issues in this paper as follows:

1. The abstract section could have more numerical results.
2. How were the households selected? Is this a representative sample? Are they participants in a pilot programme?
3. What was the duration of the data collection? Were other influencing factors (e.g. holidays, weather anomalies) controlled?
4. Please mention your limitations in your study.
5. Please add a comparison table or bullet list in the introduction showing what this paper does that others do not. You can read and cite this paper: https://doi.org/10.3390/electronics14132514. This paper is related to your study.
6. Figures 7–9 lack detailed discussion. What do these usage trends tell us? How do they differ across seasons or income level.
7. More quantitative metrics (e.g., % peak demand reduction, cost savings per kWh, change in load factor) would strengthen the arguments.
8. Consider including error bars, confidence intervals, or statistical significance measures where appropriate.

Author Response

Comment 1:The abstract section could have more numerical results.

Response 1:

The authors sincerely appreciate the reviewer’s valuable suggestion. Specific numerical results and key performance indicators have been incorporated into the abstract to more effectively highlight the effectiveness of the proposed framework. These revisions are specifically reflected in lines 19-21 of the revised manuscript.

 

Comment 2:How were the households selected? Is this a representative sample? Are they participants in a pilot programme?

Response 2:

The authors sincerely appreciate the reviewer’s valuable suggestion. The electric vehicle user data utilized in this study were generated through Monte Carlo simulations, rather than obtained from specific pilot projects or real-world residential trials. A virtual cohort of 100 EV users was constructed to represent typical private vehicle owners in urban environments. Key behavioral parameters such as arrival and departure times, energy demand, and willingness to participate were defined based on well-founded assumptions and a carefully designed modeling framework.

Although the sample is synthetic, it was meticulously designed to reflect realistic variability and heterogeneity in user behavior, including both weekday and weekend usage patterns. This modeling approach ensures that the simulated population captures representative dynamics of EV users under standard operating conditions. In future research, we plan to incorporate empirical data from field deployments or regional pilot programs to further validate and refine the behavioral models. The detailed explanation can be found in lines 587-598 of the revised manuscript.

 

 

Comment 3:What was the duration of the data collection? Were other influencing factors (e.g. holidays, weather anomalies) controlled?

Response 3:

The authors sincerely appreciate the reviewer’s valuable suggestion. The simulation conducted in this study spans a 24-hour period, representing a prototypical daily operational scenario for electric vehicle aggregators. All user behavior dataincluding vehicle arrival and departure times, initial state-of-charge (SOC) distributions, and charging/discharging decisions-were synthetically generated using Monte Carlo sampling. This probabilistic approach enables the incorporation of user heterogeneity and behavioral uncertainty while maintaining computational efficiency.

The 24-hour timeframe was chosen as it aligns with the standard cycle commonly adopted in demand response and ancillary service evaluations. To specifically assess the effectiveness of the proposed CBDR and DBDR strategies, external disturbances such as public holidays, atypical weather conditions, and unforeseen grid events were intentionally excluded from the simulation. This controlled setting ensures that the outcomes are primarily driven by the regulation strategies and user response models, rather than by exogenous variability. Future research will aim to extend the framework to seasonal or multi-day simulations that incorporate a broader range of uncertainties.

 

Comment 4: Please mention your limitations in your study.

Response 4:

Thank you for pointing out this very important issue. This study has several acknowledged limitations. First, the simulations are based on synthetic data and assumed models, lacking validation against real-world operational data. Second, the model assumes complete knowledge of electricity prices and vehicle availability, thereby overlooking the uncertainties commonly encountered in practical applications. Additionally, the treatment of battery degradation costs is simplified and does not account for variations due to environmental conditions or usage patterns. Lastly, although the proposed strategies demonstrate strong performance in a 100-EV testbed, their scalability and real-time effectiveness in large-scale deployments remain to be further explored. Future work will aim to address these limitations to enhance the model’s robustness and practical applicability. The specific revisions are reflected in lines 807-839 of the revised manuscript.

 

Comment 5:Please add a comparison table or bullet list in the introduction showing what this paper does that others do not. You can read and cite this paper: https://doi.org/10.3390/electronics14132514. This paper is related to your study.

Response 5:

Thanks for your valuable suggestion. In the revised version, we have carefully added a detailed and representative comparative table, ensuring that the selected content is both appropriately comparable and illustrative. This addition significantly enhances the coherence and readability of the literature review and strengthens the overall scholarly contribution of the paper. The comparative literature table is presented as Table 1 (Line 115 in the revised version). Moreover, this paper has been cited in the revised manuscript.

 

Comment 6: Figures 8–9 lack detailed discussion. What do these usage trends tell us? How do they differ across seasons or income level.

Response 6:

The authors’ team sincerely appreciate the reviewer’s insightful and constructive comments. In response to your valuable suggestion, we have enriched the discussion related to Figures 8and 9 in the revised manuscript as follows:

Figures 8(a) and (b)reveal significant behavioral differences between different electric vehicle clusters. Cluster 1 exhibits a concentrated and regular nighttime charging pattern (22:00–06:00) with high synchronicity and predictability. This cluster likely corresponds to private vehicle owners with fixed commuting schedules and stable overnight charging access. Their behavior aligns with characteristics typical of middle to high-income groups, demonstrating strong temporal consistency and coordinated response capabilities. In contrast, Cluster 2 shows more dispersed charging and discharging boundaries, reflecting greater behavioral diversity and uncertainty. This pattern may represent users with more flexible or varied travel routines, highlighting the natural heterogeneity in scheduling and usage preferences among EV users.The supplementary discussion section for Figures 8 (a) and (b) is located on lines 291-305 in the revised manuscript.

Figure 9 illustrates the 24-hour energy load fluctuations under the CBDR strategy. Charging behavior demonstrates higher response elasticity during mid-to-high price periods (15:00–19:00), indicating that price-sensitive users tend to adjust their charging to reduce costs. Conversely, during less price-sensitive periods (such as early morning), users prioritize convenience, resulting in more stable charging patterns. Although seasonal effects were not explicitly modeled in this study, it is anticipated that seasonal variations—such as temperature-induced range loss and heating or cooling loads—would influence SOC boundaries and energy demand patterns. Future work will incorporate seasonal behavior and segmented demographic modeling to enhance the precision and equity of scheduling strategies.The supplementary discussion section of Figure 9 is located on lines 627-653 in the revised manuscript.

 

Comment 7 More quantitative metrics (e.g., % peak demand reduction, cost savings per kWh, change in load factor) would strengthen the arguments.

Response 7:

Thank you for pointing out this very important issue. The authors have augmented the results analysis section by integrating additional quantitative indicators, including percentage reductions in peak demand, cost savings per kilowatt-hour, and variations in load factor. These enhanced metrics offer a more nuanced and compelling illustration of the efficacy of the proposed strategies. Consequently, they substantively reinforce the manuscript’s core arguments and provide readers with a deeper, more comprehensive understanding of the operational advantages realized across different scenarios. The corresponding revisions have been incorporated in Section 5.3,lines 762-774 in the revised manuscript.

 

Comment 8:Consider including error bars, confidence intervals, or statistical significance measures where appropriate.

Response 8:

The authors’ team sincerely appreciate the reviewer’s suggestion regarding the inclusion of error bars, confidence intervals, or statistical significance measures. However, this study relies on deterministic optimization modeling and Monte Carlo-based simulations rather than repeated sampling of real-world datasets or comparative group testing. Therefore, the primary outcomes—such as peak regulation margin and EVA revenue—are derived from controlled scenario analyses rather than statistical inference over multiple trials. As such, standard statistical significance testing is not directly applicable. Nonetheless, we have clarified this modeling approach in the manuscript and added notes explaining the deterministic nature of the results to avoid confusion.The specific clarifications are reflected in lines 587-598 of the article.

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The authors have responded to every single feedback raised in a comprehensive way. I liked the addition of figure 2, which makes it easy to navigate through thoughts in this work. I'm happy to recommend this work to acceptance. Congrats.

Reviewer 2 Report

Comments and Suggestions for Authors

Accept in present form.

Reviewer 3 Report

Comments and Suggestions for Authors

The authors readdressed all issues.