Economic Viability of Vehicle-to-Grid (V2G) Reassessed: A Degradation Cost Integrated Life-Cycle Analysis

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This manuscript re-evaluates the economic viability of Vehicle-to-Grid (V2G) technology by incorporating both cycling degradation and calendar aging costs into a comprehensive life-cycle framework. The topic is highly relevant to the ongoing energy transition, and the methodology demonstrates practical value. However, the following key issues must be addressed before acceptance:

1. Although the authors adopt a degradation model based on the Arrhenius equation, the source and fitting basis of key parameters remain unclear. It is recommended that the authors provide more detailed references or validation to support these values. Additionally, while the paper briefly mentions the influence of temperature, depth of discharge (DoD), and charging rates on degradation, these factors are not quantitatively analyzed in the subsequent sensitivity analysis. The authors should address this in the discussion or limitations section.
2. The distinction between this study and existing literature is insufficient. For example, Khezri et al. (2024) have already integrated both cycling and calendar aging in V2G modeling. The authors should clearly articulate what makes this study novel compared to previous works—e.g., in terms of modeling improvements, vehicle diversity, or economic insight.
3. The current analysis is entirely based on individual EV owner perspectives, while in practice, V2G is often implemented through aggregators. The lack of discussion around aggregator operation costs, coordination mechanisms, or platform fees is a notable omission. It is recommended to briefly address this aspect in the discussion section.
4. Although the study evaluates V2G net revenue over a 10-year simulation period, key modeling assumptions such as initial battery conditions, dispatch strategies (e.g., whether based on maximum price differential), and potential idle periods are not clearly specified. The authors should provide a comprehensive list of these assumptions to enhance model transparency and reproducibility.
5. The literature review could benefit from the inclusion of more technically focused V2G papers, such as "A Fuzzy Logic Approach to Power System Security with Non-Ideal Electric Vehicle Battery Models in Vehicle-to-Grid Systems" and "Data Integrity Attack Resilience for Electric Vehicle Charging Management Centers in Distributed Optimal Power Flow with Non-Ideal Li-ion Battery Models".

 

Author Response

Comments 1.1: Although the authors adopt a degradation model based on the Arrhenius equation, the source and fitting basis of key parameters remain unclear. It is recommended that the authors provide more detailed references or validation to support these values.

Reply 1.1: Thank you for your comments. We have supplemented two references as the formula sources for cycle life degradation and calendar life degradation, with the parameter fitting based on data provided in the Tesla 2023 Impact Report. The following paragraph is added：

This study developed a semi-empirical lifespan model based on references to characterize both calendar aging and cycle aging of batteries. According to Tesla's 2023 Impact Report, Tesla Model 3 and Model Y vehicles exhibited only 15% average capacity degradation after 14 years or 322,000 kilometers of driving . Building upon Tesla's latest battery data, this paper recalculated and refitted the original model equations.

Comments 1.2: While the paper briefly mentions the influence of temperature, depth of discharge (DoD), and charging rates on degradation, these factors are not quantitatively analyzed in the subsequent sensitivity analysis. The authors should address this in the discussion or limitations section.

Reply 1.2: Thanks for pointing it out. We have included sensitivity analysis plots illustrating the effects of different charging rates on cycle life degradation and different temperatures on calendar life degradation, as detailed in Figures 5 and 6. The following paragraph is added：

When the electric vehicle battery temperature is maintained at 25°C (289.15 K) and subjected to 200 cycles, Figure 5 illustrates the impact of varying charge/discharge rates (C-rates) on cycle degradation. The results demonstrate that higher C-rates accelerate battery capacity degradation.

When evaluating a one-year usage period, Figure 6 demonstrates the effect of temperature variations on calendar aging. The results clearly show that calendar aging accelerates with increasing temperature.

Comments 2: The distinction between this study and existing literature is insufficient. For example, Khezri et al. (2024) have already integrated both cycling and calendar aging in V2G modeling. The authors should clearly articulate what makes this study novel compared to previous works—e.g., in terms of modeling improvements, vehicle diversity, or economic insight.

Reply 2: Thank you for your thoughtful and constructive comments. We have dedicated a separate section to summarize the limitations identified in the current literature. The key novelties of this study lie in applying the latest battery technologies for forecasting. We incorporated V2G data from diverse global regions for analysis, and accounted for the implicit charging costs associated with V2G operations. Also, we added these info into the introduction section.

Comments 3: The current analysis is entirely based on individual EV owner perspectives, while in practice, V2G is often implemented through aggregators. The lack of discussion around aggregator operation costs, coordination mechanisms, or platform fees is a notable omission. It is recommended to briefly address this aspect in the discussion section.

Reply 3: Thank you for this insightful feedback. We agree that aggregators play a critical role in real-world V2G implementation and appreciate your observation regarding the need to address operational costs, coordination mechanisms, and platform fees.

As noted in our study, successful V2G commercialization involves multiple stakeholders, including aggregators, EV owners, manufacturers, and grid utilities, creating a complex value chain. However, our current analysis intentionally focuses on EV owner perspectives and cross-regional variations (as detailed in Figures 12 to 15), which represent the foundational layer of V2G adoption dynamics.

We acknowledge that integrating aggregator economics—such as profit-sharing models, operational costs, and fee structures—was beyond the scope of this phase of research, as these elements pertain more directly to business model design and system-level optimization. Nevertheless, we recognize these factors are essential for scalable V2G deployment.

In line with your suggestion, we will add the Discussion section to briefly:

• Explicitly acknowledge aggregators as indispensable intermediaries in V2G ecosystems.
• Clarify that our study’s scope centers on EV owner incentives and geographical comparisons, thus excluding granular aggregator-level economics.
• Highlight that future work must address aggregator coordination mechanisms, coststructures, and revenue-sharing frameworks to enable commercialization.

Comments 4: Although the study evaluates V2G net revenue over a 10-year simulation period, key modeling assumptions such as initial battery conditions, dispatch strategies (e.g., whether based on maximum price differential), and potential idle periods are not clearly specified. The authors should provide a comprehensive list of these assumptions to enhance model transparency and reproducibility.

Reply 4: Thank you for bringing this to our attention. We have provided the relevant model assumptions and precises in Section 3.2. For example: battery parameters defining, caculating based on peak-valley prices, and EV users have adequate idel time for V2G.

3.2 Model Assumptions

This study is conducted under the following premises and assumptions:

1.Battery parameters: Using Tesla Model Y as a representative case, its battery capacity (kWh) and energy consumption per 100 km are adopted as baseline EV parameters.

2.Dispatch strategies: The calculation of V2G economic benefits is based on time-of-use (TOU) electricity pricing in the peak and valley region.

3.User availability: EV users are assumed to have sufficient idle time for V2G participation. Existing research indicates that over 71% of EV users travel less than 15 km daily [47], demonstrating adequate vehicle downtime for grid scheduling.

Comments 5: The literature review could benefit from the inclusion of more technically focused V2G papers, such as "A Fuzzy Logic Approach to Power System Security with Non-Ideal Electric Vehicle Battery Models in Vehicle-to-Grid Systems" and "Data Integrity Attack Resilience for Electric Vehicle Charging Management Centers in Distributed Optimal Power Flow with Non-Ideal Li-ion Battery Models".

Reply 5: Thank you for this valuable suggestion. We have strengthened the introduction by incorporating multiple additional references focused on technological breakthroughs in V2G, including both those highlighted in your comments and other relevant studies.

Reviewer 2 Report

Comments and Suggestions for Authors

The article presents a thorough and timely study on the economic feasibility of the Vehicle-to-Grid (V2G) technology, taking into account the full battery life cycle. This constitutes a significant contribution to the field of energy management in the context of electromobility. The authors comprehensively integrate battery degradation costs (both cyclic and calendar) into a dynamic valuation model, allowing for an accurate assessment of the actual profitability of V2G across five geographic regions.

It is recommended to explain and clarify next provisions:

The methodology used for estimating calendar degradation requires further clarification and should be supported by references validating the chosen model or approach. This would enhance the transparency and reproducibility of the results.

It is advisable to provide sources for the charging and discharging electricity prices, as these parameters have a critical impact on the outcomes of the economic modelling. Their inclusion would strengthen the scientific validity and credibility of the analysis.

The specific time frame of the study should be clearly indicated. Referring only to a general 10-year period without defining exact calendar years reduces the relevance and timeliness of the results and makes it more difficult to assess their applicability to current or future energy market conditions.

It would also be beneficial to briefly discuss potential practical barriers to the implementation of V2G - such as regulatory, technical, or behavioral challenges - to enhance the practical relevance of the study

Author Response

Comments 1: The methodology used for estimating calendar degradation requires further clarification and should be supported by references validating the chosen model or approach. This would enhance the transparency and reproducibility of the results.

Reply 1: Thank you for your valuable feedback. Calendar degradation is important for this study. We have supplemented additional references as the formula sources for cycle life degradation and calendar life degradation, with the parameter fitting based on data provided in the Tesla 2023 Impact Report. The following paragraph is added：

This study developed a semi-empirical lifespan model based on references to characterize both calendar aging and cycle aging of batteries. According to Tesla's 2023 Impact Report, Tesla Model 3 and Model Y vehicles exhibited only 15% average capacity degradation after 14 years or 322,000 kilometers of driving. Building upon Tesla's latest battery data, this paper recalculated and refitted the original model equations.

Comments 2: It is advisable to provide sources for the charging and discharging electricity prices, as these parameters have a critical impact on the outcomes of the economic modelling. Their inclusion would strengthen the scientific validity and credibility of the analysis.

Reply 2: Thank you for your feedback. We have checked the data and added the data source in paper. It contains TOU electricity pricing data for V2G across different countries and regions. The data source of TOU prices of different countries are listed below:

Comments 3: The specific time frame of the study should be clearly indicated. Referring only to a general 10-year period without defining exact calendar years reduces the relevance and timeliness of the results and makes it more difficult to assess their applicability to current or future energy market conditions.

Reply 3: We appreciate your valuable suggestions. In the introduction, we have defined the specific study period as 2025-2035 and included a forecast graph of electric vehicle adoption during this time frame. The following paragraph is added：

This study will focus on the critical period of 2025–2035 for V2G development. According to the latest projections by Ouyang Minggao, the global market share of energy vehicles (EVs) is expected to approach 40% in 2024, reach nearly 50% in 2025, and exceed 50% in 2026, marking the dominance of EVs in the automotive market. Electric vehicles (EVs) will experience explosive growth, with the cumulative number of vehicles participating in the market projected to surpass 200 million by 2035.

Comments 4: It would also be beneficial to briefly discuss potential practical barriers to the implementation of V2G - such as regulatory, technical, or behavioral challenges - to enhance the practical relevance of the study

Reply 4: Thank you for highlighting this point. The barriers to V2G implementation have been supplemented in the introduction section to enhance the practical relevance of the study. The following paragraph is added：

Technically, V2G still faces multiple challenges. For instance, frequent charge-discharge cycles accelerate battery degradation, and energy losses occur during power transmission. Additionally, incompatibility issues persist between grid systems and end-user infrastructure. Currently, most mainstream charging solutions only support unidirectional charging architectures, lacking bidirectional energy exchange capabilities.

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Authors have addressed all my comments. Good job!