Decentralized electric vehicle charging enables large-scale photovoltaic integration in tropical cities

Domain: neuroscience
Published: 2026-04-07
Editorial outcome: minor_revision

Abstract
The rapid growth of tropical cities and the rising challenges of climate change call for efficient, low-carbon energy systems. Solar photovoltaics could play a key role, but deployment in tropical climates is constrained by localized thunderstorms that cause rapid generation fluctuations and stress electricity grids. While electric vehicles could balance such fluctuations by acting as distributed energy storage, this potential has not been systematically explored. Here, using Singapore as a case study, we develop a decentralized, district-level vehicle charging strategy that aligns with urban mobility patterns inferred from mobile phone data. Contrary to conventional centralized charging strategies, our approach substantially reduces grid flows, enabling greater photovoltaic integration into the existing grid infrastructure. We further show that detailed urban mobility patterns are critical to the balancing performance of electric vehicle storage. Our results highlight the potential of coordinated photovoltaic and electric vehicle systems for large-scale solar energy deployment in tropical cities.

Introduction
Urban growth is increasingly concentrated in tropical regions where ≈ 50% of the world’s population is expected to live by 20501. This trend, particularly evident in ever-expanding megacities such as Lagos, São Paulo, or Jakarta, is associated with rapidly rising energy demands and a pressing need for low-carbon energy infrastructures that limit the impact on global warming2.

Solar photovoltaic (PV) is currently becoming one of the key renewable energy technologies to meet the global growth of electricity demand while reducing carbon emissions3–6. Indeed, PV installations have experienced rapid global expansion, driven by declining costs and supportive policies7. However, due to the intermittent nature of solar energy, the large-scale integration of PV installations poses several operational challenges to the electricity system8,9. A prominent example is the so-called ‘duck curve’, where the net electricity demand (total demand minus PV output) across a region dips when PV injections become high at noon (‘duck’s belly’), and then suddenly rises when the sun sets in the early evening while people still need high amounts of electricity (‘duck’s neck’). As a result, electricity system operators face an oversupply of generation during the valley hours and steep demand increases in the early evening that may require PV curtailments10 and large amounts of highly flexible generators like gas turbines11. While the duck curve problem has so far been observed typically in more dry climates like California in the USA12 and Shandong in China13, its challenges intensify in tropical climates where localized thunderstorms and rapidly changing cloud covers additionally introduce highly fluctuating PV injections that vary strongly across small geographic areas14,15 (Supplementary Note 1). Such periods of strong local imbalances between electricity supply and demand may jeopardize the security of the electricity supply if there are insufficient electricity grid capacities to balance out these fluctuations across larger regions16–18. Therefore, addressing the intermittent availability of solar energy is essential to enabling the deployment of large-scale PV energy in tropical cities.

Controlled charging of the growing number of electric vehicles (EVs)19–21 may offer a scalable and cost-efficient solution to these challenges22–25. For instance, at the city-wide aggregated power system level, previous work has shown that incentivizing vehicle charging during the daytime, when PV generation is high, flattens the duck curve, which reduces the need for PV curtailments or capital investments in flexible generators, stationary energy battery storage or other technologies that would otherwise be required to balance out the intermittent generation26–28. The balancing effects are further amplified if EVs can feed the energy stored in their batteries back to the electricity grid. This bidirectional energy exchange through vehicle charging and discharging is known as vehicle-to-grid (V2G) and effectively turns EVs into large amounts of decentralized, mobile energy storage devices29.

These well-studied dynamics at the aggregate power systems level23,26–28,30,31 suggest that controlled EV charging can also help balance out the more local and more short-term PV generation fluctuations present in tropical regions. This may not only flatten the net electricity demand curve at the system level but also reduce the need for costly grid expansions by minimizing the flows resulting from strong spatial and temporal imbalances in PV generation. However, despite the growing importance of tropical cities, this potential has not been explored systematically, possibly due to the lack of detailed individual mobility data needed to assess the spatial and temporal availability of EVs for charging and discharging.

Here, we address this gap by integrating fine-grained EV mobility patterns derived from mobile phone data and PV generation patterns derived from solar irradiance data into an electricity grid model. Using Singapore as a case study of a tropical city and applying different future EV and PV integration scenarios, our framework enables us to assess EV charging strategies for their ability to support large-scale PV integration by balancing highly intermittent PV generation in tropical climates. Our analysis reveals that the commonly assumed EV charging optimization at the system level flattens the city-wide duck curve and reduces the associated peak net demand, but – counterintuitively – induces even larger flows on the electricity grid. In contrast, controlled EV charging optimized to smooth fluctuations at the more local urban district level not only flattens the city-wide duck curve but also reduces grid flows, thereby lowering the need for infrastructure upgrades. In addition, we demonstrate the importance of considering detailed urban mobility patterns in evaluating the EV potential by comparing the effectiveness of controlled EV charging on weekdays and weekends.

Results
PV integration and EV charging scenarios
In Singapore, a tropical city-state with ≈ 5.9 million inhabitants, solar energy is considered the only viable option for large-scale renewable energy use due to limited resources otherwise32. To assume realistic future high-PV-high-EV scenarios, we follow the projections of Singapore’s ‘PV Roadmap’33 and ‘Green Plan’34, according to which the maximum installed PV capacity reaches 8.6 gigawatt-peak (GWp) and all cars (N ≈ 0.7 · 106) are assumed to be electric by the year 2050. PV panels are thereby installed on all usable surfaces, including roofs and façades of buildings, infrastructures, and suitable water bodies. For the same year, the city’s peak base electricity demand (i.e., without EVs and PV) is estimated to be 11.5 GW (see Methods). Note that the number of cars per resident is low (≈ 0.12), even when compared to other ‘car-lite’, high-density cities (e.g., ≈ 0.23 in New York City).

Based on these values for 2050, we consider five different scenarios to quantify the benefits of controlled EV charging for the integration of PV: i) “No PV, no EV” – neither PV nor EVs are integrated, ii) "PV only" – PV is integrated but without any EV adoption, iii) “PV + uncontrolled charging” – both PV and EVs are integrated and EVs simply charge until their batteries are full when they are parked, iv) "V2G only" – no PV is integrated but EVs are adopted and obey controlled bidirectional charging where both charging and discharging behaviors are optimized, v) “PV + V2G” – both PV and EVs are integrated and EVs obey controlled bidirectional charging.

We simulate the spatial and temporal variation of the PV generation by combining solar irradiance data with fine-grained building geometry information (Methods). In addition, the detailed mobility patterns of EVs are needed to determine the periods and locations when they are parked and, therefore, available for charging and discharging35. To that end, we simulate the EV mobility using mobile phone data and the TimeGeo model described in refs. 31,36 (Supplementary Notes 2 and 3). This framework generates detailed daily trajectories (i.e., time-stamped sequences of visits to different locations) for each individual of an entire population by enriching sparse location data from mobile phones with a probabilistic human mobility model. Having established these general mobility patterns, we then use transportation mode data to extract the trips that are made by EVs (Supplementary Note 3) and to determine charging and discharging patterns. A series of validation studies using independent ground-truth datasets, including census data and car park occupancy records, confirm that the mobile phone data are sufficiently representative across Singapore and that our EV mobility estimates are consistent with empirical observations (details in Supplementary Note 3). Finally, to study the combined impact of PV and EV integration on the power flows across the city-wide electricity grid, we make use of an existing electricity network model for Singapore37 (Supplementary Note 5). Possible future changes in the network topology are thereby not considered.

Impact of tropical thunderstorms on grid flows
We start assessing the impact of thunderstorms by examining the dynamic grid loadings with PV integration (“PV only” scenario). Figure 1a depicts the solar irradiance at 10:30 am, 12:00 pm, and 1:30 pm of a typical day with thunderstorms. Solar irradiance shows significant variations over short spatial and temporal scales due to thunderstorms. For instance, differences of up to 1000 W/m2 can be observed between the east and west part of the city (≈ 55 km distance), and local fluctuations of 800 W/m2 within the short period between 10:30 am and 12:00 pm. Lower irradiance during thunderstorms leads to rapid decreases in the PV generation and therefore to sharp local peaks in the net electricity demands (Supplementary Note 6). This results in dramatically increasing grid flows that balance out these demand differences. Indeed, as can be observed by comparing Fig. 1a with Fig. 1b, districts with lower solar irradiance tend to exhibit higher line loads, and vice versa. Figure 1c additionally illustrates the detailed load profile of an exemplary line during the same day, with periods affected by thunderstorms highlighted. During the two thunderstorm periods, the line load shows sharp peaks, followed by rapid drops as the thunderstorms pass.

As shown in Fig. 1d for all lines and an extended period of 2 months (using solar irradiance data from March 20th to May 20, 2024), these significant increases in the line loads due to thunderstorms persist after integrating EVs without a controlled charging scheme (“PV + uncontrolled charging”, see Methods). On several lines, maximum loads increase by more than 100% on days with thunderstorms (characterized by strong irradiance fluctuations) compared with cloud-free, high-irradiance days. This shows that even conservative security margins of 33-50% can be insufficient if line flow limits do not account for thunderstorms (a 100% load increase corresponds to a required security margin of 50%). Such increases in peak line loads therefore put high pressure on the transmission lines and, without energy storage, may require costly upgrades to the grid infrastructure.

Mitigating the impact of thunderstorms through EV charging
Having established the impact of thunderstorms, we now examine the potential of controlled EV charging for mitigating these additional burdens on the electricity grid (“PV + V2G” scenario). We start with a conventional ‘system-level’ V2G optimization approach which seeks to minimize the aggregated, city-wide peak net demand and is typically used to assess the benefits of V2G for the power generation level27,28 (Methods). Figure 2a shows the effect of this controlled EV charging scheme on the flow dynamics of an exemplary line (same as in Fig. 1c) over the course of a day. Interestingly, optimizing EV charging at the system level leads to a strong peak in the flow magnitude that is not only higher than in the “PV only” scenario but even exceeds the maximum flow observed in the “PV + uncontrolled charging” scenario. This observation may appear counterintuitive, as V2G, unlike uncontrolled charging, enables vehicles to feed power back to the grid, reducing net electricity demand and, consequently, line flows. However, the system-level "PV+V2G" strategy prioritizes minimizing peak net demand at the city-wide scale, which permits localized demand fluctuations. These local variations, in turn, lead to significant line flow variability.

To tackle this challenge, we apply a decentralized, ‘district-level’ V2G optimization scheme (Methods). It minimizes the peak net electricity demands of each urban district and thus can be expected to alleviate the observed thunderstorm-induced grid flows. Singapore comprises 55 urban districts (‘planning areas’) with areas ranging from ≈ 1 km2 in the dense downtown to ≈ 20 km2 in the surroundings. Indeed, district-level "PV+V2G" is able to reduce the peak load on the exemplary line compared to both the “PV only” scenario and the "PV + uncontrolled charging" scenario (Fig. 2b). This reflects its focus on flattening the demand curves at the more local level, which reduces the need for electricity exchanges across districts and thus naturally lowers the line flows. Note that these peaks are, however, still higher than in the scenarios without PV (“No PV, no EV” and “V2G only”).

Extending the analysis to all lines and to the 2-month period of solar irradiance data generalizes these observations (Fig. 2c-f). On sunny days without thunderstorms, both system-level (Fig. 2c) and district-level (Fig. 2d) charging optimization are able to reduce the maximum line loads compared to the “PV+uncontrolled charging” scenario, whereas the district-level optimization shows larger gains. This superiority of the district-level optimization on days without thunderstorms suggests that this approach is also beneficial for dry climates. On days with thunderstorms, however, system-level optimization increases the maximum loads on the majority of the lines (Fig. 2e): Counterintuitively, system-level charging optimization – a common approach when evaluating V2G effectiveness – exacerbates grid loading in tropical climates. District-level EV charging optimization, in contrast, is able to substantially reduce the loads compared to the combination of PV with uncontrolled EV charging (Fig. 2f), yielding reductions of 18.1 ± 11.8% (mean ± standard deviation). This confirms that decentralized EV charging optimization, balancing out demand fluctuations at the district level, effectively reduces the burden on the grid due to PV integration in tropical climates. More detailed statistics across all PV and EV integration scenarios are given in Supplementary Note 8. An extensive sensitivity study shows that district-level V2G optimization remains effective across a wide range of different operating conditions, including varying battery capacities, (dis)charging rates, EV adoption levels, and PV integration scenarios (Supplementary Note 9). Moreover, a detailed alternating-current (AC) power flow analysis shows that network voltage levels remain within acceptable limits (Supplementary Note 10). Generally, we find qualitatively similar, but less strong, effects when using uni- instead of bidirectional EV charging, i.e., not allowing EVs to feed energy back to the grid (Supplementary Note 11).

Is district-level “PV+V2G” also able to reduce the original duck-curve problem at the aggregate, city-wide level? Indeed, as shown in Fig. 3, the large-scale deployment of PV ("PV only") induces a strong dip in the city-wide net demand in the early afternoon, especially on sunny days (for days with thunderstorms see Supplementary Note 6). System-level “PV+V2G” almost completely flattens this duck curve (Fig. 3a). District-level “PV+V2G” is able to reduce the peak to a similar degree, while some dip in the demand curve persists (Fig. 3b). However, the demand increase in the late afternoon is substantially less steep compared to the “PV only” scenario and comparable to the usual base demand increase ("No PV, no EV" scenario) in the morning hours. Thus, district-level EV charging also significantly reduces the original duck-curve problem, albeit to a slightly lesser extent than the system-level approach. Note that controlled charging without PV (“V2G only”) flattens the curve (Fig. 3), but there is no appreciable reduction in the peak demand compared to the base scenario (“No PV, no EV”). Similarly, integrating PV alone (“PV only”) has no effect on the peak demand when only PV is integrated. In other words, only the combined deployment of PV and EVs can effectively lower peak demand and reduce dependence on non-PV generators.

Impact of mobility patterns on V2G potential
Since EVs can only charge or discharge while parked, their spatio-temporal energy storage potential is strongly determined by their detailed mobility patterns. This is particularly evident during thunderstorms, when solar irradiance drops and recovers rapidly, leading to sudden reductions and increases in PV generation. These generation losses can be compensated through V2G support from EVs, but only if a sufficient number of EV batteries are available – this availability depends on the number of parked EVs connected to the grid.

To explore the importance of this factor, we compare the effectiveness of controlled EV charging on weekdays with weekends. Figure 4a, b illustrates the average number of parked EVs simulated over a month, broken down into weekdays and weekends, in a typical peripheral residential district and a typical central commercial district, respectively. The residential area exhibits an inverted bell curve in EV availability, with most vehicles departing in the morning and returning in the evening, limiting the potential for daytime charging and energy storage. On the weekend, this curve is substantially flatter with many more cars parked during the daytime, which increases the potential of V2G to smooth out local drops in the PV outputs due to thunderstorms. This trend is primarily due to the absence of work obligations for many residents on weekends, reducing the need to commute. Conversely, the commercial district with its offices and shopping malls shows the opposite behavior with a high EV availability during the daytime and low availability during the night. This pattern aligns with business hours, as many vehicles remain parked at workplaces and commercial places, creating an opportunity to synchronize EV charging with solar PV generation effectively. Furthermore, and again being in contrast to the residential area, the commercial area has a lower number of cars parked during the weekend compared to the weekdays, which reduces the potential of V2G to smooth out local drops in the PV outputs due to thunderstorms. This can again be explained by the absence of work commutes during the weekends.

Based on this observation that EVs tend to be more evenly distributed across the city during weekends, we hypothesize that controlled EV charging reduces grid loadings more effectively on the weekends compared to weekdays. This expectation is confirmed in Fig. 4c and Supplementary Fig. 25, showing the results from the district-level “PV+V2G” strategy. The maximum line loads are appreciably reduced during weekends compared to weekdays, highlighting the importance of considering individual EV mobility patterns for evaluating their impact on the power grid performance. Note that the base electricity demands (i.e., the district-level demands without EVs) are assumed to be the same on weekdays and weekends (Methods), so that these differences are a direct causal result of the mobility patterns.

Methods
This study involved secondary analysis of previously collected, anonymized mobility data and did not constitute human subjects research. Institutional review board approval was therefore not required. Details of the mobility data and their use in the EV mobility modeling are provided in Supplementary Notes 2 and 3.

Base electricity demand modeling
The base electricity demand (not considering PV and EV integration) was modeled using the bottom-up approach in ref. 53. The electricity demand for each building was thereby simulated using the open-source software ‘City Energy Analyst’54. This software determines demand profiles based on building attributes (building use type, building height, floor area, etc.), weather input, and the surrounding environment. The simulated household energy consumption was calibrated against district-level monthly data55, and sectoral energy estimates were adjusted using annual consumption data for each sector from the same source. Subsequently, we summed the electricity demands of all buildings and then used half-hourly system demand data56 for 2022, averaged over all days, to calibrate the simulated energy demand profiles. Next, the future demand profile for the year 2050 was obtained by scaling the peak demand to 11.5 GW, as projected by the Solar Energy Research Institute of Singapore33, and assuming that the relative half-hourly variations remain unchanged (Supplementary Note 4). In other words, the same base electricity demand curve was used for all days in our simulation (e.g., we did not differentiate between weekdays and weekends) to better understand the impact of PV and EV mobility dynamics. Finally, to get the district-level half-hourly demand profile (for all sectors), we aggregated the calibrated demands for all buildings in a district.

Solar PV generation modeling
Due to the limited availability of real-world PV generation data at the level of the analyzed integration scenarios, a conventional simulation-based method was adopted. Specifically, solar PV generation was simulated for each urban district by combining spatiotemporal irradiance data with the generation capacity of available surface areas. Although indirect, such irradiance-based methods have been frequently used in prior studies and have shown reliable performance in urban-scale PV potential assessments33,57. Building-integrated PV (BIPV) constitutes the largest share of Singapore’s overall PV potential due to the high building density. The potential generation capacity of BIPV at the district level was taken from a prior study53, whereas a minimum irradiation threshold of 750 kWh m−2 year−1 was used to select available surfaces for PV deployment. Building geometries were assumed to remain unchanged for the year 2050. In addition to buildings, our study considers other potential PV installation areas, including water bodies for floating PV (usable surface ~0.28 × area of water bodies), and designated land areas for ground-mounted PV33. For both categories, system-level efficiencies were assumed to be the same as those adopted for rooftop PV53. Finally, to match Singapore’s projected PV deployment target33 of 8.6 GWp by 2050, the remaining generation was attributed to infrastructure-integrated PV (e.g., noise barriers, PV structures above roads). Aggregated across all districts, this yields a contribution to the installed PV capacity of 9.3% (0.8 GWp), consistent with the city-wide estimate of 12.6% in Singapore’s PV Roadmap33 (which provides only an aggregate figure). Then, the PV generation profile of each district was derived based on the mean district-level solar irradiance profile58, together with the estimated installed PV capacity. A simplified linear irradiance-output relationship59 was assumed to convert irradiance into generation. While we note that a non-linear model may offer higher accuracy at higher spatial resolutions (e.g., individual buildings), the linear approximation is appropriate for the current district-level analysis, where localized variations (e.g., local shading and performance ratios) can cancel out.

Uncontrolled EV charging
Charging demand was simulated for an entire month, after which the daily average was calculated to represent typical daily charging patterns60. To explore the maximum possible utilization of EVs as mobile storage units, all vehicles were assumed to be connected to charging stations whenever the parking duration exceeds a pre-determined threshold of 30 minutes61,62. A vehicle was then charged if its battery state-of-charge (SOC) was below a pre-defined threshold of 20% or if the current SOC was insufficient to cover the upcoming trips63,64. A backward calculation was used to determine the minimum SOC required to satisfy subsequent trips at each time step, starting from the last trip and working backward. This calculation accounts for the maximum allowable charging opportunity (i.e., when an EV is parked longer than 30 mins) and the SOC limits of each vehicle i (SOCmin i ≤ SOCi(t) ≤ SOCmax i, with SOCmin i = 0 and SOCmax i = 1). Through this backward iteration, the minimum SOC required for the next trip(s) at each step was determined. All EVs were assumed to be equipped with an average battery size of 71 kWh24,65–67. We also assumed a charging infrastructure of 7.2 kW, which is the standard for residential charging in urban areas67,68. The simulation is summarized in Supplementary Note 7. The robustness of our results against variations of these parameters is presented in Supplementary Note 9. All simulations were implemented in MATLAB R2023b (MathWorks).

System-level EV charging optimization
System-level (i.e., city-scale) EV charging optimization focuses on the benefits for the aggregate power generation needs by flattening the daily demand curve and thus reducing the required installed capacity of non-PV generators that constantly ramp up and down, allowing for steady-state operation with optimal efficiency. For the EV charging optimization, a day was discretized into Δt = 0.25 hour = 15 min intervals such that each day starts at t = 1 and ends at t = 96. The EV charging and discharging states were determined as follows. Consider a power system in which a total amount of N EVs schedule their charging profiles over T time slots, each with a duration of Δt. Let O(t) and S(t) denote the base (“No PV, no EV”) electricity demand and the solar PV generation at time t, respectively. The net non-EV demand is D(t) = O(t) − S(t). To flatten the aggregate demand profile seen by the grid, the quadratic deviation of the total load is minimized28:

min \sum_{t=1}^{T} \left( D(t) + \sum_{i=1}^{N} (p_{i,h}^{ch}(t) - p_{i,is}^{dis}(t)) \right)^2

s:t: 0 ≤ p_{i,h}^{ch}(t) ≤ p_{i}^{ch}, 8i,t

0 ≤ p_{i,is}^{dis}(t) ≤ p_{i}^{dis}, 8i,t

SOC_i(t+1) = SOC_i(t) + \frac{\eta_i^{ch} p_{i,h}^{ch}(t) Δt}{c_i} - \frac{p_{i,is}^{dis}(t) Δt}{\eta_i^{dis} c_i}, 8i,t

SOCmin i ≤ SOC_i(t) ≤ SOCmax i, 8i,t

SOC_i(t_{j}^{dep}) ≥ SOC_i^{req}(t_{j}^{dep}), 8i,j

where p_{i,h}^{ch/dis}(t), p_{i}^{ch/dis} and η_{i}^{ch/dis} denote the charging/discharging power of vehicle i at time step t, its maximum value and the corresponding efficiency. The battery capacity is denoted by c_i. The j-th departure event of vehicle i is given by t_{j}^{dep}, and SOC_i^{req}(t_{j}^{dep}) is the minimum state-of-charge required to complete the subsequent trip(s) starting at that departure time (pre-computed as in the uncontrolled charging case). We assumed a fixed charging and discharging power for all EVs, equal to the charging rate used for uncontrolled charging. The efficiencies were set as η_{i}^{ch} = 0.87 and η_{i}^{dis} = 0.90. The EV charging optimization problem was solved using Gurobi Optimizer (version 12.0.0, Gurobi Optimization, LLC).

District-level EV charging optimization
Conversely, this optimization emphasizes more local benefits by minimizing the maximum net demand within each urban district, ensuring a more balanced distribution of energy demand across urban areas:

min \sum_{k=1}^{K} z_k

s:t: z_k ≥ D_k(t) + \sum_{i \in I_k} (p_{i,h}^{ch}(t) - p_{i,is}^{dis}(t)), 8k,t

Eqs: (2) - (6)

where k represents the index of districts and z_k represents the peak demand of district k. I_k denotes the set of vehicle indices within district k and K is the total number of districts. D_k^{max} is the peak base demand (without PV and EV integration), which was used to normalize the electricity demand, given that its magnitude varies greatly across the districts.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Comments to Authors for considering the following revisions:\nThe article discusses the potential of integrating solar photovoltaics (PV) and electric vehicles (EVs) to address energy\nchallenges in tropical cities, using Singapore as a case study. Key points discussed included the following:\n1. Challenges of PV in Tropical Climates like in Singapore:\n• Localized thunderstorms cause rapid fluctuations in PV energy output, leading to grid overloads.\n• The \"duck curve\" problem, where net electricity demand dips at noon and rises sharply in the evening, is exacerbated in\ntropical climates.\n2. Role of EVs:\n• EVs can act as mobile energy storage units, balancing out local fluctuations in PV generation.\n• A decentralized, district-level EV charging strategy is proposed, which aligns with urban mobility patterns derived from\nmobile phone data.\n3. Scenarios and Simulations:\n• Various scenarios were simulated, including \"PV only,\" \"PV+uncontrolled charging,\" \"V2G only,\" and \"PV+V2G.\"\n• District-level optimization showed significant benefits in reducing grid stress and managing the duck curve.\n4. Findings:\n• District-level EV charging reduces grid flows and enables greater PV integration compared to conventional centralized\nstrategies.\n• Detailed urban mobility patterns are critical for the effectiveness of EVs in balancing fluctuating energy sources.\nOverall, the study presents a promising solution to the challenges of integrating renewable energy like PVs in tropical cities,\nbut its implementation will require careful consideration of data availability, infrastructure, and policy support.\nBefore accepting the manuscript, authors are recommended to consider the following aspects to revise and improve the\nimpact of their research work. Below are the proposed Revisions. It is important to align their data analysis in terms of the\nSingapore’s Green Plan targets to make it furthermore relevant to their case study.\n1. Explicit Reference to Green Plan Goals:\n• Suggested revision: Clearly state how each aspect of the research aligns with specific goals of the Singapore Green Plan\n2030 for Solar PV and EVs.\n• Example: Validating their analysis how it supports the Green Plan's goal to quadruple solar energy deployment by 2025\nand beyond by optimizing PV generation and EV charging.\nAlong these lines, authors are recommended to include Policy Recommendations that align with the Green Plan's initiatives.\nFor example, to support the Green Plan's target for all newly registered cars to be cleaner-energy models from 2030, policies\nsuch as subsidies for EV purchases and reduced charging fees during off-peak hours are recommended. For more guidance\non this aspect, authors are recommended to go through related research (papers are given below) on solar capacity in\nSingapore and energy security options while revising this article.\n• Jiong Rui Loh, Sreenivasulu Bellam (2024). Towards net zero: Evaluating energy security in Singapore using system\ndynamics modelling, Applied Energy, (358), 122537.\n• Wei Kit, K., Sreenivasulu Bellam. (2024). Evaluating the growth of Singapore’s solar electricity capacity towards Green\nPlan 2030 targets and beyond using system dynamics modelling approach. Applied Energy, Vol. 376A, 124091.\nAuthors are also recommended to cite these two key articles and any related cross references therein.\n2. Integration with Broader Sustainability Goals:\n• Suggested revision: Discuss how the research contributes to broader sustainability goals in terms of emission savings\nthrough PV and EV integration. For example. by reducing carbon emissions, this research contributes to the Green Plan's\nvision of integrating energy mix to save emissions.\n3. Data Privacy Concerns:\n• Challenge to overcome: The reliance on detailed mobility data from mobile phones raises concerns about data privacy and\nthe feasibility of obtaining such data in other regions. Ensuring the privacy and security of detailed urban mobility data from\nmobile phones. Public trust and willingness to share data may be affected, potentially limiting the availability of crucial\ninformation for optimizing EV charging strategies. How this could be overcome?\n4. Future Research Directions:\n• Revision: Outline future research directions that align with the Green Plan's long-term vision. For example, future research\nshould focus on optimizing EV charging at finer spatial scales and integrating additional renewable energy sources,\nsupporting the Green Plan's aim to build a Resilient Future. For economic factors, how this research and proposed solutions\ncan stimulate economic growth by fostering innovation in renewable energy technologies, aligning with the Green Plan's\nvision of a Green Economy in Singapore.\n5. Insights, limitations, conclusions/summary: A good paragraph(s) about these points from this research work could be\nincluded after the discussion section.\n6.Data Availability options to be included besides having supplementary notes provided.\nBy incorporating these revisions, the manuscript will not only demonstrate its alignment with the Singapore Green Plan 2030\nand Green Economy but also provide a comprehensive roadmap for achieving the plan's sustainability goals with a\nsuccessful integration of PVs and EVs.", "reviewer_2": "This paper assesses the potential of coordinated PV and EV systems planning for large-scale PV deployment in tropical\ncities, with a focus on Singapore. The paper is generally well structured and presented. There are a few comments for the\naurhours to consider as below:\n- For the PV generation, why the authors didn't use the practical data directly from the utility company? The indirect\nestimation from solar irradiance is often not so reliable.\n- It is needed to discuss potential limitations and biases associated with using mobile phone data (e.g., coverage, privacy,\nand representativeness).\n- The coordination of sub models are not clearly presented?\n- The case study is focused on Singapore, which is described as a “car-lite” city with a low number of cars per resident. The\nmanuscript should address how the findings might differ in cities with higher car ownership or different urban structures.\n- The authors use a static grid topology based on existing infrastructure. It does not seem to account for future grid\nenhancements or upgrades, which are likely in a 2050 scenario. This may overestimate the grid stress under high PV\npenetration scenarios.\n- The authors didn't do economic assessment, which is rather essential for practical implementation.", "reviewer_3": "- The study focuses heavily on theoretical optimization and grid load simulations but lacks any cost-benefit analysis. For\ninstance, no quantitative estimate of the financial cost of battery degradation from frequent bidirectional charging is provided,\nwhich could significantly offset the theoretical benefits.\n- The authors should clarify what is the economic trade-off between decentralized V2G control and upgrading the grid\ninfrastructure? Also, they should highlight whether the district-level V2G systems realistically implementable, considering the\nrequired communication and control infrastructure?\n- Quantitative impact of this assumption is not analyzed—how would results change with higher-capacity batteries, or faster\ncharging infrastructure? Also, assumptions on 100% availability and connection compliance (i.e., all cars being plugged in\nwhen parked >30 min) are highly optimistic and not validated.\n- How sensitive are the findings to changes in EV adoption rate, PV capacity factor, or frequency of thunderstorms?\n- The claim that “district-level V2G reduces peak load by up to X%” (Figure 2) needs to be statistically qualified.\n- The analysis focuses solely on line load magnitudes and peak demand, neglecting critical metrics like voltage stability,\nfrequency deviations, line losses, transformer loading. Without this, the real operational impact on the grid cannot be fully\nassessed.\n- While mobile phone data is used to simulate EV mobility, the accuracy and resolution of this input is not quantified. What is\nthe uncertainty in vehicle location or mode inference? Also, how is the transition from aggregate phone data to specific EV\nbehavior validated?\n- The 114% increase in line loads during thunderstorms (top of Page 6) is a strong result but must be contextualized against\nline ratings and safety margins.\n- There are some redundancies within the text, e.g., the duck curve is explained in both the intro and discussion. Also, some\nterms (e.g., \"baseline demand\") are not clearly defined early on.\n- The linear model assumes error-free input and doesn’t report confidence intervals for 2030/2050 forecasts.\n- There's no train/test split, k-fold CV, or comparison with other models (e.g., exponential smoothing, time series models).\n- Power flow visualizations are purely illustrative — no actual power system constraints (e.g., Kirchhoff's laws, capacity\nratings) are enforced.\n- Thunderstorm modeling is hardcoded via single-day assumptions, which is scientifically fragile.\n- Plots use cool, hot, or custom colors without consistency. This may be misleading in comparative visuals.\n- No axis labels or color bars for power flow which violates best practices for scientific figures.\n- Arrow plots are overloaded without meaningful scaling — units are unclear.\n- Interpolation from 30-min to 15-min steps is done with spline but never validated. This could introduce artifacts — a plot of\noriginal vs. interpolated would help."}, "author_response": "Response to Reviewers for\nPowering tropical cities: Photovoltaics at scale via decentralized\ne-mobility charging (NCOMMS-25-36776-T)\nWe sincerely thank the Reviewers for their thorough assessment of our manuscript. The\nconstructive feedback has greatly helped us to clarify the broader significance of our work\nand to further demonstrate the robustness of our results. Addressing the comments required\nsubstantial additional analysis, and we thank the Reviewers for the patience.\nWe have carefully addressed all points in the detailed, point-by-point response below. New\nadditions to the manuscript (e.g., new paragraphs or figures) are highlighted in orange, and\nchanges in the text are highlighted in green. The numbering of references in this response\nletter reflects their order of appearance here and may differ from that in the manuscript.\nReviewer: Comments to Authors for considering the following revisions: The article discusses\nthe potential of integrating solar photovoltaics (PV) and electric vehicles (EVs) to address\nenergy challenges in tropical cities, using Singapore as a case study. Key points discussed\nincluded the following:\n1. Challenges of PV in Tropical Climates like in Singapore:\n• Localized thunderstorms cause rapid fluctuations in PV energy output, leading to grid\noverloads.\n• The ”duck curve” problem, where net electricity demand dips at noon and rises sharply in\nthe evening, is exacerbated in tropical climates.\n2. Role of EVs:\n• EVs can act as mobile energy storage units, balancing out local fluctuations in PV genera-\ntion.\n• A decentralized, district-level EV charging strategy is proposed, which aligns with urban\nmobility patterns derived from mobile phone data.\n3. Scenarios and Simulations:\n• Various scenarios were simulated, including “PV only,” “PV+uncontrolled charging,” “V2G\nonly,” and ”PV+V2G.”\n• District-level optimization showed significant benefits in reducing grid stress and managing\nthe duck curve.\n4. Findings:\n• District-level EV charging reduces grid flows and enables greater PV integration compared\nto conventional centralized strategies.\n• Detailed urban mobility patterns are critical for the effectiveness of EVs in balancing fluc-\ntuating energy sources.\nOverall, the study presents a promising solution to the challenges of integrating renewable\nenergy like PVs in tropical cities, but its implementation will require careful consideration of\ndata availability, infrastructure, and policy support.\nResponse: We sincerely thank the Reviewer for the positive and encouraging assessment of\nour manuscript.\nComment 1.1: Before accepting the manuscript, authors are recommended to consider the\nfollowing aspects to revise and improve the impact of their research work. Below are the\nproposed Revisions. It is important to align their data analysis in terms of the Singapore’s\nGreen Plan targets to make it furthermore relevant to their case study.\n1. Explicit Reference to Green Plan Goals:\n• Suggested revision: Clearly state how each aspect of the research aligns with specific goals\nof the Singapore Green Plan 2030 for Solar PV and EVs.\n• Example: Validating their analysis how it supports the Green Plan’s goal to quadruple solar\nenergy deployment by 2025 and beyond by optimizing PV generation and EV charging.\nAlong these lines, authors are recommended to include Policy Recommendations that align\n2\nwith the Green Plan’s initiatives. For example, to support the Green Plan’s target for all\nnewly registered cars to be cleaner-energy models from 2030, policies such as subsidies for\nEV purchases and reduced charging fees during off-peak hours are recommended. For more\nguidance on this aspect, authors are recommended to go through related research (papers are\ngiven below) on solar capacity in Singapore and energy security options while revising this\narticle.\n• Jiong Rui Loh, Sreenivasulu Bellam (2024). Towards net zero: Evaluating energy security\nin Singapore using system dynamics modelling, Applied Energy, (358), 122537.\n• Wei Kit, K., Sreenivasulu Bellam. (2024). Evaluating the growth of Singapore’s solar elec-\ntricity capacity towards Green Plan 2030 targets and beyond using system dynamics modelling\napproach. Applied Energy, Vol. 376A, 124091. Authors are also recommended to cite these\ntwo key articles and any related cross references therein.\nResponse 1.1: We thank the Reviewer for this helpful comment. We followed this suggestion\nand added a new dedicated paragraph to the Discussion section in the main text, drawing on\nthe literature suggested by the Reviewer. The paragraph (see below) is complemented by a\nmore detailed discussion in the new Supplementary Note 15: Alignment with the Singapore\nGreen Plan 2030:\nOur framework can support progress towards policy-driven sustainability and energy security\ngoals. In Singapore, for instance, it may directly contribute to achieving the national net-zero\nemissions target for 2050 [1] by accelerating the large-scale integration of PV (through defer-\nring costly grid reinforcements) and by enhancing the attractiveness of EVs (e.g., by enabling\nnew revenue streams through controlled charging). Indeed, a recent system dynamics model\nestimates that net carbon emission savings can reach up to 29.1 million tonnes by 2040, as-\nsuming a supportive policy scenario that enables the PV capacity to reach ≈8% of the total\nelectricity generation by the same year [2]. Existing policy recommendations towards this goal\ncouldbeextendedto alignmoreclosely withourapproach. Forexample, whileSingapore’s na-\ntional ‘Green Plan 2030’ [2,3] outlines specific interim milestones towards the net-zero target,\nincluding a full transition to cleaner-energy vehicles by 2040, our results suggest the additional\ndesign of complementary incentive programs, such as attractive compensation schemes for EV\nusers participating in controlled charging. A more detailed discussion of the alignment of our\nwork with Singapore’s Green Plan 2030 and beyond is given in Supplementary Note 15.\nSupplementary Note 15: Alignment with the Singapore Green Plan 2030\nOur findings can be aligned with the Singapore Green Plan 2030 [3], particularly with its\n‘Green Transport’ and ‘Green Energy’ targets. As part of the Green Transport target, the\nplan requires all new vehicle registrations to be ‘cleaner-energy’ models by 2030. Our study\nfurther underpins the value of a full transition towards EVs to meet this requirement. The\nGreen Plan also formulates the deployment of 60,000 public EV charging stations. Here, our\nmobility-based insights can be used to prioritize locations for their placement. In regards to\nGreen Energy (which is mostly PV in Singapore), our district-level PV-EV coordination can\nreduce peak net demand and defer grid reinforcements, supporting both carbon emissions\nreduction and system resilience. By allowing to increase the share of PV in the overall energy\nmix while reducing the need for immediate electricity grid reinforcements, our method also\nhelps the country to decrease its dependence on energy imports and thus increase its energy\nsecurity in a timely manner [1]. While the Green Plan’s target of 2 GW of solar capacity by\np\n3\n2030 is an important first milestone towards larger shares of PV [2] and well below Singapore’s\ntechnical potential discussed in the main text, coupling EVs and PV is already valuable at this\nstage, particularly for flattening the city-wide net demand curve (see Supplementary Fig. 29;\nreproduced in Fig. R1-1 below). [...]\nExisting policy recommendations can thereby be extended to better align with our findings.\nExamplesarelocation-dependenttime-of-usetariffswithreducedchargingfeesduringtimesof\nhigh solar outputs, subsidies for (V2G-enabled) EV purchases to accelerate EV adoption, sub-\nsidies or rebates for high-utilization fleets such as taxis to expand the pool of vehicles eligible\nfor V2G services, or compensation schemes and business models for embedding V2G capabil-\nities in public carparks so as to encourage broader EV user participation in V2G programs.\n[...] \nComment 1.2: Integration with Broader Sustainability Goals: • Suggested revision: Discuss\nhow the research contributes to broader sustainability goals in terms of emission savings\nthrough PV and EV integration. For example. by reducing carbon emissions, this research\ncontributes to the Green Plan’s vision of integrating energy mix to save emissions.\nResponse 1.2: We thank the Reviewer for this helpful suggestion. In the revised manuscript,\nwe added this point to the Discussion section in the main text (see also our Response 1.1):\nOur framework can support progress towards policy-driven sustainability and energy-security\ngoals. In Singapore, for instance, it may directly contribute to achieving the national net-zero\na b\n0 5 10 15 20\nHour of day\n)WM(\ndnamed\nediw-ytic\teN\nSystem-level optimization ( 2.0 GW )\np\n13000\n12000\n11000\n10000\n9000\n8000\n0 5 10 15 20\nHour of day\n)WM(\ndnamed\nediw-ytic\teN\nDistrict-level optimization (2.0 G W )\np\n13000\n12000\n1569.7 MW (12.9%) 1067.1 MW (8.8%)\n11000\n10000\n9000\nPV + V2G PV + uncontrolled charging\ndnamedtenediw-ytiC dnamedtenediw-ytiC\nonly\nFigure R1-1: District-level EV charging flattens the city-wide duck curve under Sin-\ngapore’s 2030 PV deployment scenario. City-wide net demand profiles with system-level\n(a) and district-level (b) EV charging optimization for a typical day without thunderstorms\n(weather data from March 22, 2024). The figure follows the same format as Fig. 3 in the\nmain text but the installed PV capacity is 2 GW , corresponding to the Singapore Green Plan\np\n2030 target.\n4\nemissions target for 2050 [1] by accelerating the large-scale integration of PV (through defer-\nring costly grid reinforcements) and by enhancing the attractiveness of EVs (e.g., by enabling\nnew revenue streams for controlled charging). Indeed, a recent system dynamics model esti-\nmates that net carbon emission savings can reach up to 29.1 million tonnes by 2040 assuming\na supportive policy scenario that enables the PV capacity to reach ≈8% of the total electricity\ngeneration by the same year [2].\nFurthermore, we have added the following discussion to the new Supplementary Note 15:\nAlignment with the Singapore Green Plan 2030:\nIn terms of broader sustainability goals, our research supports the Green Plan’s vision of a\ncleaner energy mix by reducing carbon emissions through accelerated solar PV integration and\nby lowering the need for additional gas turbine capacity to cover the daily peak demand (see\nFig. 3 in the main text). Together, these positive gains from the coordinated coupling of EVs\nwith PV should be included in future qualitative and quantitative evaluations of sustainability\npolicy targets in tropical cities [2].\nComment 1.3: Data Privacy Concerns:\n• Challenge to overcome: The reliance on detailed mobility data from mobile phones raises\nconcerns about data privacy and the feasibility of obtaining such data in other regions. Ensur-\ning the privacy and security of detailed urban mobility data from mobile phones. Public trust\nand willingness to share data may be affected, potentially limiting the availability of crucial\ninformation for optimizing EV charging strategies. How this could be overcome?\nResponse 1.3: We agree with the Reviewer that using mobile phone data requires careful\nprivacy safeguards and that access to such data can be challenging. In the revised manuscript,\nwe now explicitly address this concern in the Discussion section as follows (see also our\nResponse 1.5):\nSecond, a potential limitation arises from privacy constraints associated with mobile phone\ndata and the resulting challenges of accessing such data in other urban regions. The current\nframework only requires data that is spatially aggregated at the level of urban districts (typical\narea > 1 km2). Temporally, the data could also be limited to relatively short driving profiles\n(i.e., every few days, a new random sample of anonymized users can be selected). This\nallows for a strong data pre-aggregation by the data provider, which reduces the risks of\ndata misuse, ensures compliance with local and international data protection regulations, and\nthus facilitates data access. Moreover, our framework is not limited to mobile phone data\nbut can be applied to any type of mobility data, such as those generated from agent-based\nmodels. Recent advances in transfer learning also enable pre-trained deep learning mobility\nmodels to be fine-tuned for specific cities using only small amounts of local data (e.g., from\nvolunteer-based efforts) [4], making them suitable for more fine-grained analyses.\nComment 1.4: Future Research Directions:\n• Revision: Outline future research directions that align with the Green Plan’s long-term\nvision. For example, future research should focus on optimizing EV charging at finer spatial\nscales and integrating additional renewable energy sources, supporting the Green Plan’s aim to\nbuild a Resilient Future. For economic factors, how this research and proposed solutions\ncan stimulate economic growth by fostering innovation in renewable energy technologies, aligning\nwith the Green Plan’s vision of a Green Economy in Singapore.\nResponse 1.4: We appreciate this constructive comment by the Reviewer. Following this\nrecommendation, we replaced the following text in the Discussion section of the revised\nmanuscript:\nFirst, the detailed mobility and PV generation data present an opportunity to optimize EV\ncharging at a finer spatial scale, such as the level of a small neighborhood or even an individual\nbuilding. Such optimization would allow users to gain direct monetary benefits through PV\nand V2G and aligns well with concepts of solar energy communities [5,6].\nwith\nFirst, by focusing on urban districts, our analysis may overlook opportunities that emerge at\nfinerspatialresolutions. Futurestudiescouldfurtherleveragedetailedmobility, PVgeneration,\nand local electricity network data to optimize EV charging within neighborhoods or individual\ncarparks. Such optimization would allow users to gain direct monetary benefits through PV\nand V2G and it would align well with concepts of solar energy communities [5,6], thereby\nfurther supporting long-term resilience goals such as those outlined in the Singapore Green\nPlan 2030 (Supplementary Note 15).\nThis revision is then followed by a new section in the new Supplementary Note 15: Alignment\nwith the Singapore Green Plan 2030:\nFuture research could aim to downscale optimization models to individual neighborhood or\ncarpark levels. This will enable EV charging strategies that consider hyper-local mobility de-\nmands, renewable energy availabilities (e.g., rooftop solar output), and electricity distribution\nfeeder constraints. Such an approach could further reduce grid flows, thereby supporting the\nGreen Plan’s long-term sustainability and energy security goals. Finally, as EV adoption and\nPV deployment expand, research on smart charging systems, digital platforms, and predictive\nanalytics tools could stimulate innovation, job creation and investment, thus advancing the\nGreen Plan’s ‘Green Economy’ targets [3].\nComment 1.5: Insights,limitations,conclusions/summary: Agoodparagraph(s)aboutthese\npoints from this research work could be included after the discussion section.\nResponse 1.5: We thank the Reviewer for this helpful suggestion. To address the requested\npoint, we have now restructured and extended the previous Discussion section (to our un-\nderstanding, this is typically the last section of the main text in a Nature Communications\npaper). Specifically:\n1. We now more explicitly list the key insights in the first paragraph by replacing the\nsentence: Using Singapore as a case study, we showed that rapidly [...] with Using\nSingapore as a case study, our key insights are as follows. First, rapidly [...], followed\nby minor modifications.\n2. We have added a new dedicated paragraph that explicitly states the limitations of the\ncurrent study (including the mobile data concern, as discussed in the previous response)\nand connects them to future research:\nOur work has several limitations that open promising avenues for future research. First,\nby focusing on urban districts, our analysis may overlook opportunities that emerge\nat finer spatial resolutions. Future studies could further leverage detailed mobility, PV\n6\ngeneration, and local electricity network data to optimize EV charging within neigh-\nborhoods or individual carparks. Such optimization would allow users to gain direct\nmonetary benefits through PV and V2G and it would align well with concepts of solar\nenergy communities [5,6], thereby further supporting long-term resilience goals such as\nthose outlined in the Singapore Green Plan 2030 (Supplementary Note 15).\nSecond, a potential limitation arises from privacy constraints associated with mobile\nphone data and the resulting challenges of accessing such data in other urban regions.\nThe current framework only requires data that is spatially aggregated at the level of\nurban districts (typical area > 1 km2). Temporally, the data could also be limited to\nrelatively short driving profiles (i.e., each few days, a new random sample of anonymized\nusers can be selected). This allows for a strong data pre-aggregation by the data\nprovider, which reduces the risks of data misuse, ensures compliance with local and\ninternational data protection regulations and thus facilitates data access. Moreover,\nour framework is not limited to mobile phone data but can be applied to any type of\nmobility data, such as those generated from agent-based models. Recent advances in\ntransfer learning also enable pre-trained deep learning mobility models to be fine-tuned\nfor specific cities using only small amounts of local data (e.g., from volunteer-based\nefforts) [4], making them suitable for more fine-grained analyses.\nThird, ouroptimizationframeworkcurrentlyomitsadditionalobjectives, suchasextend-\ning EV battery lifetimes through degradation-aware charging strategies [7] that promise\nto further increase the economic viability of the proposed V2G scheme. Fourth, behav-\nioral factors, such as users’ willingness to participate in V2G programs, preferences for\ncharging speed, and battery state-of-charge thresholds [8–10], are not yet considered.\nIncorporating these factors would further enhance the accuracy of the results. Finally,\nit remains an open challenge to design effective electricity market models and incen-\ntive policies [11,12] that encourage EV charging behaviors consistent with the patterns\nassumed in this work.\n3. We have added a concise concluding statement at the end of the Discussion:\nIn conclusion, our work demonstrates that coordinated bidirectional charging of EVs\nfacilitates the timely and large-scale integration of PV in tropical cities, paving the way\nfor effective implementations that support the transition to a low-carbon urban future.\nComment 1.6: Data Availability options to be included besides having supplementary notes\nprovided.\nResponse 1.6: We appreciate the Reviewer’s suggestion. Following this comment and the\njournal’s editorial policy, we have added the new section ‘Data Availability’:\nElectricity demand and solar irradiance data are publicly available from the Energy Market\nAuthority of Singapore (https://www.ema.gov.sg/resources/statistics). Data for the\nderivationoflocation-specificPVgenerationpotentialsarealsopubliclyavailable, asdescribed\nin [13]. Travel survey and census data are publicly available from the Singapore Depart-\nment of Statistics (https://www.singstat.gov.sg/publications/reference/cop2020).\nAlthough raw mobility data are not publicly available due to privacy considerations, district-\nlevel aggregated data to reproduce the findings and figures in this paper are available at\nhttps://github.com/tropicalcityv2g-hash/Powering-tropical-cities.\nalignment with the Singapore Green Plan 2030 and Green Economy but also provide a com-\nprehensive roadmap for achieving the plan’s sustainability goals with a successful integration\nof PVs and EVs.\nResponse 1.7: Once again, we sincerely thank the Reviewer for the encouraging evaluation\nof our work, and situating it within Singapore’s broader sustainability goals. As outlined\nabove, we have substantially revised our manuscript to explicitly align our findings with the\nSingapore Green Plan 2030 and hope that these additions considerably strengthen our work,\nin particular with respect to its policy relevance.\nComment: This paper assesses the potential of coordinated PV and EV systems planning for\nlarge-scale PV deployment in tropical cities, with a focus on Singapore. The paper is generally\nwell-structuredandpresented. Thereareafewcommentsfortheauthorstoconsiderasbelow:\nResponse: We are very grateful to the Reviewer for the careful reading of our manuscript\nand for the positive feedback. We have carefully addressed each of the specific comments and\nrevised the paper accordingly. We believe that these improvements have further enhanced the\nclarity and overall quality of our work.\nComment 2.1: For the PV generation, why the authors didn’t use the practical data directly\nfromtheutilitycompany? Theindirectestimationfromsolarirradianceisoftennotsoreliable.\nResponse 2.1: We thank the Reviewer for raising this point. We opted for an indirect\nestimation of PV generation based on solar irradiance for the following reasons:\n1. At present, the installed urban PV capacity in Singapore remains largely limited to\nbuilding rooftops, which constrains the usefulness of historical operational data for our\nstudy.\n2. Large-scale PV generation data at the required spatial and temporal resolution were not\naccessible from the utility company at the time of the study.\n3. Theirradiance-basedestimationmethodweadoptedhasbeenextensivelyusedinthelit-\nerature and validated under similar contexts [14–16]. For instance, detailed experiments\nin Singapore have shown that estimating PV power output from irradiance using a fixed\nconversion factor has an nRMSE (normalized Root Mean Square Error) of 5.4% [16],\nwhich is acceptable for the granularity of our study. Furthermore, following the ap-\nproach of the Solar Energy Research Institute of Singapore (SERIS) [14], we included\nonly exposed areas with a minimum annual irradiation (e.g., 750 kWh m−2 year−1 for\nfac¸ades), which provides an additional safeguard against potentially confounding factors\nlike shading. Finally, our estimate for the city-wide total installed capacity for buildings\nand fac¸ades is consistent with the aggregate value estimated by SERIS (details in [13]).\nTo clarify these points, we have expanded the ‘Solar PV generation modeling’ description in\nthe Methods section to include the following statements:\nDue to the limited availability of real-world PV generation data at the level of the analyzed\nintegration scenarios, a conventional simulation-based method was adopted. Specifically, ...\n[...]\nAlthough indirect, such irradiance-based methods have been frequently used in prior studies\nand have shown reliable performance in urban-scale PV potential assessments [14,15].\nComment 2.2: It is needed to discuss potential limitations and biases associated with using\nmobile phone data (e.g., coverage, privacy, and representativeness).\nResponse 2.2: WeagreewiththeReviewerthatthepotentiallimitationsandbiasesofmobile\nphone data need to be carefully discussed. In the original manuscript, we conducted a series\n9\nof validation studies, including a cross-comparison with census data and carpark availability\ncounts, see Supplementary Note 4 in the revised Supplementary information (sections ‘Vali-\ndation of simulated mobility data’ and ‘Extraction and validation of EV-based travel’). These\nanalyses show that, after careful processing, the representativeness (and coverage) of the data\nat hand are sufficient. To emphasize this more clearly, we have added the following sentence\nto the main text, last paragraph of section ‘PV integration and EV charging scenarios’ (see\nalso our Response 3.7 to Reviewer #3):\nA series of validation studies using independent ground-truth datasets, including census data\nand carpark occupancy records, confirm that the mobile phone data are sufficiently repre-\nsentative across Singapore and that our EV mobility estimates are consistent with empirical\nobservations (details in Supplementary Note 4).\nIn addition, in Supplementary Note 4 (section ‘Validation of mobile phone data’) we now\ninclude a short discussion of the potential biases of mobile phone data with respect to their\ncoverage and representativeness:\n[...] While mobile phone-based data represent arguably the most comprehensive human mo-\nbility data available today, they may also be subject to certain biases. For instance, popu-\nlation coverage might vary across geographic regions due to varying market shares of differ-\nent telecom operators. Moreover, certain population groups might be overrepresented (e.g.,\nyounger and wealthier individuals) while others might be underrepresented (e.g., older popu-\nlations) [17]. To test for such biases that might limit the coverage and representativeness of\nthe data, we performed a series of cross-comparisons with census data (as well as a validation\nstudy of the resulting EV mobility, see section ‘Extraction and validation of EV-based travel’).\n[...]\nFinally, wealsoagreewiththeReviewerthatprotectingdataprivacyisimportant. Toexplicitly\naddress this point, we have extended the Discussion section of the main text as follows (see\nalso our Reply 1.3 to Reviewer #1):\nSecond, a potential limitation arises from privacy constraints associated with mobile phone\ndata and the resulting challenges of accessing such data in other urban regions. The current\nframework only requires data that is spatially aggregated at the level of urban districts (typical\narea > 1 km2). Temporally, the data could also be limited to relatively short driving profiles\n(i.e., each few days, a new random sample of anonymized users can be selected). This\nallows for a strong data pre-aggregation by the data provider, which reduces the risks of\ndata misuse, ensures compliance with local and international data protection regulations, and\nthus facilitates data access. Moreover, our framework is not limited to mobile phone data\nbut can be applied to any type of mobility data, such as those generated from agent-based\nmodels. Recent advances in transfer learning also enable pre-trained deep learning mobility\nmodels to be fine-tuned for specific cities using only small amounts of local data (e.g., from\nvolunteer-based efforts) [4], making them suitable for more fine-grained analyses.\nComment 2.3: The coordination of sub models are not clearly presented?\nResponse 2.3: We thank the Reviewer for pointing us to this lack of clarity. To present the\ncoordination of the submodels more clearly, we have added the new Supplementary Note 3:\nOverview of the modeling framework (with a reference in the Methods section of the main\ntext), which includes the following schematic diagram of our framework:\n(PV deployment\nscenario)\n& irr a A in d v s i a S t a a a i n o r l l a e l c l a a b a e t r l i e o d n a P t V a m So o l d a e r l P in V g PV p g r e o n fi e le ra s tion (s d E o y i p s V s t t t e i r c m i m c h i t a z - - l l r a e e g t v v i i o n e e n g l l ) & w E N it V e h t c c d o h e n a m t r r g a o i n n lle d g d\nPower flow\nPower flow\nanalysis\nHistorical Electricity Baseline electricity EV Parameters\nde e m le a c n tr d ic d it a y ta m de o m de a li n n d g (w d it e h m ou a t n i d n t p e r g o r f a il t e io n cap (e a . c g it . y , , b c a h tt a e r r g y i ng\nof PV and EV) rate, adoption rate)\nBattery\nBattery\ndegradation degradation\nsimulation\nM da o t b a s i u l & e rv T p e h r y a o v n e e l E m V o m de o l b in il g ity EV pa m tte o r b n il s ity Unc s c o im h n a t u r r l o g a l i t l n e io g d n EV un N c e o t n d w tr e o it m h lle a d n d E V\ncharging\nData Models/simulation Parameters Results\nFigure R1-2: Overview of the modeling framework. The framework combines solar\nirradiance and space availability data for PV generation modeling, historical electricity demand\ndata for modeling the base demand (without the integration of PV and EV), as well as mobile\nphone and travel survey data for EV mobility modeling. The outputs feed into both controlled\nEV charging optimization and uncontrolled EV charging simulations. Then the obtained net\nelectricity demand, including PV generation and EV (dis-)charging profiles, is fed into the\npower flow analysis and battery degradation modeling.\nComment 2.4: The case study is focused on Singapore, which is described as a “car-lite”\ncity with a low number of cars per resident. The manuscript should address how the findings\nmight differ in cities with higher car ownership or different urban structures.\nResponse2.4: Thankyouforthisvaluablecomment. Intherevisedversionofourmanuscript,\nwe have added a sensitivity analysis showing that a higher EV adoption rate increases the ben-\nefits of the district-level charging optimization in Singapore, while also observing a saturation\neffect, see the new Figure S18 in the new Supplementary Note 9: Sensitivity analysis (and our\nResponse 3.4 to Reviewer #3). At the same time, a higher number of EVs also distributes\nV2G participation across more vehicles, thereby reducing the average battery degradation per\nvehicle due to V2G. Together, this clearly supports our previous statement that in other cities\nwith higher EV adoption rates, we expect that the observed benefits of the district-level EV\ncharging are further amplified.\nWithrespecttodifferenturbanstructures, wegenerallyexpectthatthebenefitsofthedistrict-\nlevelEVchargingoptimizationalsoapplytomorphologiesthatstronglydifferfromSingapore’s\nrather compact polycentric form with its highly dense urban core. Nevertheless, based on our\ncomparison of the mobility patterns during weekends and weekdays (Fig. 4 in the main text),\nwe expect similar ‘second-order’ effects that will need to be established on a case-by-case\nbasis. For instance, in more dispersed, sprawling cities, the electricity demand per area might\nbe lower (less centralized consumption), so that line load fluctuations during thunderstorms\nmight be smaller and might also be absorbed with a lower number of available cars.\nTo make these points more clear we have replaced the following sentence in the Discussion\nsection:\nConsequently, for other urban regions with higher EV adoption rates, we expect that the\n11\nobserved benefits of EV charging for PV integration are further amplified.\nWith the following new text (including a new reference to a recent paper by Miotti et al.,\ndiscussing the effect of urban form on mobility and energy consumption):\nConsequently, we expect that the revealed benefits of the district-level EV charging also apply\nto other urban regions with similar or higher EV adoption rates, as our sensitivity analysis\nshows that higher EV shares are associated with stronger line load reductions (Supplementary\nNote 9). At the same time, a higher number of EVs also distributes V2G participation across\nmore vehicles, which reduces the average battery degradation per vehicle due to V2G. Urban\nstructuresthatdifferfromthecompactspatialorganizationofSingaporemayimplychangesin\nthedetailedenergyconsumptionandmobilitypatterns[18]. Weexpectthatsuchchangesmay\nhave ‘second-order’ effects on the grid load mitigation potential, comparable to the effect of\ndifferent mobility patterns during weekends and weekdays (Fig. 4), which should be evaluated\non a case-by-case basis.\nComment 2.5: The authors use a static grid topology based on existing infrastructure. It\ndoes not seem to account for future grid enhancements or upgrades, which are likely in a 2050\nscenario. This may overestimate the grid stress under high PV penetration scenarios.\nResponse 2.5: We thank the Reviewer for this comment. In the revised manuscript,\nwe demonstrate the robustness of our results under different line upgrade scenarios. Specifically,\n(cid:12) (cid:12)\nwe define the flow limit Fmax of each line ℓ as Fmax = αmax(cid:12)F0 (cid:12), where F0 is the time-\nℓ ℓ (cid:12) ℓ,t(cid:12) ℓ,t\nvarying line load observed in the base “no PV, no EV” scenario and α ≥ 1 is a tunable scaling\nfactor that represents the level of the line upgrades (the larger the value of α the larger\nthe upgrade). A line becomes overloaded if its load in the different PV and EV integration\nscenarios becomes larger than Fmax. Indeed, we observe that the reduction of line overloads\nℓ\nof the proposed district-level optimization scheme remains significant across a wide range of\nα, compared to the system-level optimization and the uncontrolled EV charging. This analysis\ntherefore suggests that the district-level optimization remains useful even when considering\nline upgrades. These additional results are now reported in the new Supplementary Note 13:\nIndicative estimation of grid upgrade costs (see also our Response 3.2 to Reviewer #3).\nIn the current manuscript, we retain a fixed network topology. As such, our study can be seen\nas a ‘conservative’ approach that helps to identify potential bottlenecks and regions where\nfuture network extensions are most needed under different EV and PV integration scenarios.\nTo make this limitation more clear, we have added the following sentence at the end of the\nsection ‘PV integration and EV charging scenarios’ in the main text:\nPossible future changes in the network topology are thereby not considered.\nWe should add here that in the revised manuscript, we have also performed a full AC power\nflow analysis on an extended network topology, see the new Supplementary Note 10: AC\npower flow analysis (and our Response 3.6 to Reviewer #3), again confirming the significant\ngains of the district-level optimization. We have therefore added the following sentence to\nSupplementary Note 10:\nSincetheACpowerflowisperformedonanextendednetworktopology, thisadditionalanalysis\nfurther underpins the robustness of our findings against changes in the network topology.\npractical implementation.\nResponse 2.6: We thank the Reviewer for this helpful comment. To address this point,\nwe have conducted the following additional economic analyses:\n1. We have estimated the minimum economic compensation required for EV users to\nbreak even on the battery degradation and charging infrastructure costs incurred when\nparticipating in the V2G scheme. This estimation is based on an additional, detailed\nbattery degradation study under the specific conditions of the proposed V2G scheme.\nThe compensation costs are comparable (even slightly below) those reported in previous\nV2Gstudies. TheseresultsarenowreportedinthenewSupplementaryNote14: Battery\ndegradation and estimation of economic compensation (see also our Response 3.1 to\nReviewer #3).\n2. In the revised manuscript, we have estimated the incentive payment for EV users under\na typical incentive scheme for demand-side flexibility and compared it to the minimum\ncompensation cost. The new analysis shows that V2G participation would be econom-\nically viable for users under today’s incentive scheme in Singapore. These additional\nresults are also reported in the new Supplementary Note 14 (see also our Response 3.1\nto Reviewer #3).\n3. We have estimated indicative grid upgrade costs necessary to accommodate the various\nPV and EV integration scenarios. Our results suggest that the proposed district-level\nV2G scheme could defer capital investments of several hundred million SGD, with po-\ntential upper-bound savings of up to several billion SGD. These additional results are\nnow reported in the new Supplementary Note 13: Indicative estimation of grid upgrade\ncosts (see also our Response 2.5 and Response 3.2 to Reviewer #3).\nComment 3.1: The study focuses heavily on theoretical optimization and grid load sim-\nulations but lacks any cost-benefit analysis. For instance, no quantitative estimate of the\nfinancial cost of battery degradation from frequent bidirectional charging is provided,\nwhich could significantly offset the theoretical benefits.\nResponse 3.1: First of all, we would like to thank the Reviewer for the thorough assessment\nof our manuscript and for the excellent feedback.\nTo fully address this comment, we have conducted a detailed battery degradation simulation\nstudy, followed by an economic analysis.\nIn this additional study, we have simulated the battery degradation using the MATLAB im-\nplementation of BLAST-lite, which is part of the Battery Lifetime Analysis and Simulation\nToolsuite provided by the U.S. National Renewable Energy Laboratory [19,20]. We have\nthereby simulated the behavior of NMC (Nickel Manganese Cobalt) and LFP (Lithium Iron\nPhosphate) batteries, which are the most common types in EVs today (each type covers\n≈50% of EVs) [21]. In line with the literature [22], we first consider a duration of 10 years\ndue to uncertainties involved in longer periods (which is also reflected in battery warranties\nthat are typically shorter than 10 years [23,24]), and we use an initial battery capacity of\n71 kWh as used in the revised main text (this value corresponds to today’s average battery\nsize in Singapore’s EVs [25,26]).\nOur new Supplementary Fig. 27a,d, reproduced here in Fig. R1-3a,d, shows the results of\nthese additional simulations, suggesting that the proposed district-level V2G scheme does not\nlead to a reduction of the average battery end-of-life (EOL, commonly defined as the point\nin time when the usable capacity declines to 80% of its initial value) below the considered 10\nyears period. Note that, although LFP cells generally exhibit slower degradation than NMC\ncells, their aging behavior is highly dependent on operating conditions [27]. In our simulation,\nunder frequent full-charge and high-SOC V2G cycles, NMC cells do not degrade faster than\nLFP cells and may even degrade more slowly (Fig. R1-3a,d). While not universally reported,\nthis observation aligns with evidence that degradation is highly sensitive to SOC range and\ncycling strategy [28].\nCompared with uncontrolled charging, the proposed V2G scheme helps preserve a higher\ncapacity in NMC batteries over an extended period and effectively reduces the number of\nbatteries reaching EOL within 10 years (see the 10th–90th percentile ranges in Fig. R1-3a).\nThis may be attributed to the V2G operation maintaining a more favorable SOC [7] which\nreduces the rate of calendar aging (Fig. R1-3c). For LFP batteries, the V2G scheme leads\nto a faster capacity decline compared with uncontrolled charging, resulting in some batteries\nreaching their EOL shortly before 10 years (Fig. R1-3d).\nIn the revised manuscript, we now also provide an estimate of the expected economic compen-\nsation for EV users which is required to break even the battery degradation and bidirectional\ncharger costs, when considering time periods beyond 10 years. To that end, we have di-\nrectly applied the V2G compensation calculation method by Sagaria et al. [22]. The expected\ncompensation for the battery degradation is calculated from the number of battery cycles for\ndriving that are lost because V2G shortens the battery lifetime. Following the notation in [22]\n14\n0 5 10 15\nTime (years)\nyticapac\nllarevO\nNMC — 71 kWh\n0.8\nEOL = 14.88\n0.6\n0.4\n0.2\nUncontrolled charging\nV2G\n0\n0 5 10 15\nTime (years)\n)gnilcyc(\nyticapaC\nNMC — 71 kWh\n1\n0.8\n0.6\n0.4\n0.2\n0\n0 5 10 15\nTime (years)\n)radnelac(\nyticapaC\nNMC — 71 kWh\n1\n0.8\n0.6\n0.4\n0.2\n0\n0 5 10 15\nTime (years)\nyticapac\nllarevO\nLFP — 71 kWh\n0.8\nEOL = 10.44\n0.6\n0.4\n0.2\n0\n0 5 10 15\nTime (years)\n)gnilcyc(\nyticapaC\nLFP — 71 kWh\n1\n0.8\n0.6\n0.4\n0.2\n0\n0 5 10 15\nTime (years)\n)radnelac(\nyticapaC\na b c\nd e f\nLFP — 71 kWh\n1\n0.8\n0.6\n0.4\n0.2\nNMC and LFP batteries. The batteries have an average initial capacity of 71 kWh. The\ndistrict-level “PV+V2G” scheme (red curves) is compared to the “PV+uncontrolled charging”\nscheme (blue curves), which considers unidirectional charging only. The solid lines indicate\nthe average remaining battery capacity for each year, while the shaded areas represent the\n10th–90th percentile range. The top row (a,b,c) corresponds to the NMC and the bottom\nrow (d,e,f) to the LFP chemistry. Columns show, respectively, (a,d) overall degradation,\n(b,e) cycling-relateddegradation, and(c,f)calendar-relateddegradation. Thehorizontaldashedline\nmarks the 80% end-of-life (EOL) threshold.\nit is given as\nComp (SGD ) = Cost −Cost ·cycles , (1)\nexp 2025 Bat Opp ev(V2G)\nwhere the total battery cost, Cost = C ·cost/kWh·1.5, includes battery replacement\nBat B\ncosts of 50% of the battery cost, cost/kWh, and C is the battery capacity. Moreover,\nB\nCost is the EV opportunistic cost, being the monetary value of one cycle of battery\nOpp\noperation for driving,\nCost\nCost (SGD /cycle) = Bat , (2)\nOpp 2025\ncycles\nev\nwherecycles isthecumulativenumberofcyclestillEOLusedfordrivingwithout V2Gservice\nev\n(uncontrolled charging), while cycles in equation 1 is the corresponding number with\nev(V2G)\nV2G service. The battery degradation cost per cycle used for V2G is then\nComp\nCost (SGD /cycle) = exp , (3)\nBD 2025\ncycles\nv2g\n15\nwhere cycles is the number of cycles for V2G service till EOL. The cost for a bidirectional\nv2g\ncharger is\nCostof charger\nCost (SGD /cycle) = , (4)\nINF 2025\ncycles\nv2g\nso that the V2G compensation cost per MWh is\nCost +Cost\nV2G (SGD /MWh) = BD INF ·1000. (5)\ncomp 2025\nC\nB\nUsing the parameters shown in the new Supplementary Tables 4 and 5, reproduced here in Ta-\nbles 1 and 2, we estimate that the proposed V2G scheme demands an expected compensation\nfor battery degradation (Comp ) of SGD 911 and SGD 2827 for NMC and LFP batteries,\nexp\nrespectively. Further considering the charger costs by assuming two chargers per vehicle (due\nto the high out-of-home V2G activity), we finally estimate that the V2G scheme demands a\nminimum V2G compensation (V2G ) of SGD 11.20 and SGD 24.16 per MWh V2G energy\ncomp\nflow for NMC and LFP batteries, respectively. These compensation costs are comparable to\nthose reported in [22].\nA comprehensive cost-benefit analysis would require us to assume a detailed energy market\nand incentive model that captures the future integration of V2G aggregators and participants\ninto the grid [22]. The development of such potential market and incentive models is an active\nfield of research (see, e.g., [12]) and beyond the scope of our paper. Nevertheless, incentive\nschemes for demand-side flexibility commonly return a fraction of the total electricity cost\nsavings, defined as the product of the price reduction and the total electricity consumption,\nto participating users. This fraction is typically governed by an adjustment factor between\n0 and 1. In the case of Singapore, this factor is set at one third according to the official\nguidelines of the Energy Market Authority (EMA) [29]. Using electricity demand and tariff\nTable 1: Input parameters for V2G economic compensation, NMC batteries.\nParameter Symbol Value Comment\nCost of charger (SGD ) Cost 3008 Value from [22]\nBattery capacity (kWh) C 71 Value used in main text\nB\nBattery cost (SGD /kWh) cost/kWh 75.2 Value from [22]\nev\nNumber of cycles for driving, with V2G cycles 779 Based on Fig. R1-3\nev(V2G)\nNumber of cycles for V2G service cycles 4929 Based on Fig. R1-3\nv2g\nTable 2: Input parameters for V2G economic compensation, LFP batteries.\nParameter Symbol Value Comment\nCost of charger (SGD ) Cost 3008 Value from [22]\nBattery capacity (kWh) C 71 Value used in main text\nB\nBattery cost (SGD /kWh) cost/kWh 60.2 Value from [22]\nev\nNumber of cycles for driving, with V2G cycles 599 Based on Fig. R1-3\nev(V2G)\nNumber of cycles for V2G service cycles 3401 Based on Fig. R1-3\nv2g\n16\na b\nPeak Non-peak\n)hWM/$S(\necirp\nyticirtcelE\nx\n400\n5000 2.5\n350 4000\n3000\n300 2000\n1000 2\n200\n1.5\n100\n1\n50\n0\n0 5 10 15 20\nHour of day\n)$S(\ntsoc\nyticirtcel\nDistrict-level optimization\nUncontrolled charging\nPeak period\nE\nFigure R1-4: Electricity price and total cost variations under different charging sce-\nnarios. a Boxplots of the half-hourly electricity prices over a period of 6 months (January 1st\nto June 30th, 2025) during peak and non-peak hours. The central line indicates the median,\nboxes denote the inter-quartile range, and diamonds mark the mean values. Because extreme\nprice spikes reach up to 4,500 SGD/MWh, the main plot is truncated at the 99th percentile\nfor clarity, while the inset displays the distribution of the top 1% of prices, highlighting rare\nhigh-price events during peak periods. b Modeled total system cost for system-level optimiza-\ntion, district-level optimization, and uncontrolled charging. The demand profiles correspond\nto those shown in Fig. 3. The shaded background distinguishes the peak (light-red) from\nnon-peak periods, defined according to demand exceeding the threshold (i.e., 90th percentile\nof the demand).\ndata [30], we calculate the average electricity price during peak hours (defined as periods\nwhen demand is above the 90th percentile) to be 226.6 SGD/MWh, and 110.5 SGD/MWh\nduring non-peak hours, see our new Supplementary Fig. 28a, reproduced here in Fig. R1-4a.\nBased on the difference between the net electricity demand profiles under the “PV+V2G”\nand “PV+uncontrolled charging” scenarios (see Fig. 3 in the main text), and following the\nmethod in [31], we can then estimate that the total daily electricity cost savings are, on\naverage, ≈SGD 3.4 million for district-level V2G (Fig. R1-4b). Applying EMA’s incentive\nrule, we thus get a payment of SGD 1.72 per vehicle and day. There is, on average, 0.89 V2G\ncycles per day, so that the incentive payment becomes SGD 27.22 per MWh V2G energy\nflow, which is higher than the minimum compensation cost estimated above. While this calculation\nomits the coordination costs of V2G aggregators, it provides an indication that V2G would\nbe economically viable for users under today’s incentive scheme. Moreover, savings from\ndeferred grid reinforcements, expected advances in battery technology [21], and optimized\ncharging strategies that extend battery lifetime (e.g., [32]) could further enhance the net\nbenefits and could be considered in more detailed follow-up studies.\nWe have summarized these additional analyses in the new Supplementary Note 14: Battery\ndegradation and estimation of economic compensation. In the main text we have added the\nfollowing new paragraph:\nThese main benefits of the proposed V2G scheme must be considered in relation to the\nassociated costs. For EV users, these costs are primarily due to accelerated EV battery\n17\ndegradationandinvestmentinbidirectionalchargers[22]. Usingadetailedbatterydegradation\nsimulation and an economic analysis (details in Supplementary Note 14), we estimate that\nthe required compensation cost to break even with battery degradation and charger costs is\nSGD 11.20 - 24.16 per MWh of V2G energy flow. These values are comparable to those\npreviously reported for low-usage V2G schemes and in the range of incentive payments from\ncurrent demand-side flexibility programs.\nComment 3.2: The authors should clarify what is the economic trade-off between decentral-\nized V2G control and upgrading the grid infrastructure? Also, they should highlight whether\nthe district-level V2G systems realistically implementable, considering the required communi-\ncation and control infrastructure?\nResponse 3.2: We thank the Reviewer for these constructive suggestions. To address these\npoints, we have conducted an additional study that estimates the costs of grid infrastructure\nupgrades, followed by a discussion of the required communication and control infrastructure.\nTo provide an indicative, order-of-magnitude estimate of the grid infrastructure upgrade costs,\nwe adapted a commonly used model for assessing the impact of increasing line flow limits [33]\nto our setting. Specifically, since we need to assume increased flow limits to accommodate\nhigher electricity demands in the 2050 scenario, we introduce a tunable scaling factor α ≥ 1\n(cid:12) (cid:12)\nso that the flow limit of each line ℓ becomes Fmax = αmax(cid:12)F0 (cid:12), where F0 is the time-\nℓ (cid:12) ℓ,t(cid:12) ℓ,t\nvarying line load in the base case scenario (“No PV, no EV”). A line becomes overloaded, and\nthus needs to be upgraded, if |F | > Fmax, where F is the time-varying flow simulated for\nℓ,t ℓ ℓ,t\none of our three PV and EV integration scenarios (“PV+uncontrolled charging”,“PV+V2G”\nwith system- and district-level optimization). Our new Supplementary Fig. 26, reproduced\nhere in Fig. R1-5, shows that district-level “PV+V2G” substantially reduces the number of\noverloaded lines and associated cumulative line length compared with the other scenarios over\na broad range of α.\n1 1.1 1.2 1.3 1.4 1.5 1.6\nsenil\ndedaolrevO\n#\nNumber of overloaded lines\nPV+V2G: district-level optimization\n70\n60\n50\n40\n30\n20\n10\n0\n1 1.1 1.2 1.3 1.4 1.5 1.6\n)mk(\nhtgnel\nlatoT\na b\nTotal length of overloaded lines\n250\n200\n150\n100\n50\nFigure R1-5: Reduction of line overloads with district-level “PV+V2G”. a Number of\noverloaded lines versus the flow-limit scaling factor α (‘overloading threshold’). b Correspond-\ning cumulative length of overloaded lines. Here the length is the straight distance between a\nline’s end points (as the crow flies).\nreinforcement is associated with high costs. As an extreme example, the city-state recently\ncompleted a large-scale network reinforcement project: a new 40 km underground tunnel for\nhigh-voltagecables, constructedatadepthof60m, whichcostapproximatelySGD2.4billion,\nor≈SGD60millionperkm[35]. Theinternationalliteratureontheeconomicsofunderground\nelectricity networks [36,37] further indicates that capital expenditures show strong variations\nthat span an order of magnitude. Without having access to more systematic cost estimates for\nconstructing new or upgrading existing electricity lines in Singapore, this yields an indicative\nrange of SGD 6–60 million per km, in line with international surveys [36,38,39]. Using\nthe lower bound of this range for a conservative estimate of cost savings, we add 25%\nto the line lengths to account for deviations from straight-line routing due to construction\nconstraints [40], and set α = 1.33 to ensure that line loads in the base case remain well within\nallowable limits.\nUnder these assumptions, the estimated lower bound of the potential savings when applying\ndistrict-level instead of system-level V2G amounts to approximately SGD 519 million, while\nthe upper bound can reach several billion SGD. Moreover, these savings rapidly increase if the\ngrid is operated tighter to its security margins (reducing the value of α in Fig. R1-5). District-\nlevel V2G also reduces line upgrade costs compared with uncontrolled unidirectional charging\n(again up to several billion SGD), which further strengthens the economic viability of V2G by\nadding to the substantial savings from flattening the evening peak in electricity demand (see\nour Response 3.1) and to the potential monetary benefits of ancillary grid services, such as\nvoltage and frequency stabilization, which are not explicitly addressed in this paper.\nIn the revised manuscript, we report these new analyses in the new Supplementary Note 13:\nIndicative estimation of grid upgrade costs. Moreover, we have added the following sentences\nto the Discussion section in the main text:\nElectricitynetworkreinforcementsrequiresubstantialcapitalinvestmentsandlengthyplanning\nprocesses, especially in dense urban areas [41]. The introduced district-level V2G scheme\nmitigates both the duck curve problem and this infrastructure challenge. Drawing on the\nliteratureontheeconomicsofelectricitynetworkplanning, weestimateforSingaporepotential\nsavings of several hundred million up to several billion SGD (see Supplementary Note 13).\nAs correctly highlighted by the Reviewer, district-level V2G enables savings through deferred\ngrid upgrades but also requires investments in communication and control infrastructure. To\nthe best of our knowledge, and as noted in a comprehensive report by the U.S. National\nRenewable Energy Laboratory [42], the costs of such systems have received far less attention\nthan the costs of bidirectional charging hardware and battery degradation (which we already\nconsideredinoureconomicanalysisinResponse3.1). Wethereforeleaveadetailedquantifica-\ntion of these costs to future research. However, we expect that transitioning from system-level\nto district-level optimization will not require appreciably higher investments in communication\nand control infrastructure, as these components are likewise essential in system-level V2G\nimplementations [43]. In the revised manuscript, we have added the following text in the\nDiscussion section:\nThe district-level V2G scheme also requires communication and control infrastructure. Al-\nthough detailed cost estimates are currently difficult to quantify [42], these components are\nsimilarly essential in other V2G systems [43], and we therefore expect their costs to be within\na comparable range.\nRegarding the feasibility of the communication and control systems required for a district-level\nand China [47] have demonstrated the operational viability of localized V2G infrastructure\nunder diverse regulatory and technological environments. For instance, Shenzhen, China,\nconducted one of the world’s largest V2G demonstrations on March 28, 2025, involving more\nthan 760 charging stations and 17,000 EVs citywide. Supported by its new power load man-\nagement system and the EV charging platform (‘ShunyiChong’), the trial successfully moni-\ntored and coordinated real-time grid–vehicle interactions, confirming the technical feasibility\nof large-scale V2G deployment.\nTo address this point in the revised manuscript, we have added a new paragraph ‘Practical\nfeasibility of V2G implementation’ in Supplementary Note 1. Moreover, in the Discussion of\nthe main text, we have added the following sentence:\nSeveral large-scale V2G pilot projects worldwide have already demonstrated the practical fea-\nsibility of implementing the required communication and control systems (Supplementary\nNote 1).\nComment 3.3: 3. Quantitativeimpactofthisassumptionisnotanalyzed—howwouldresults\nchange with higher-capacity batteries, or faster charging infrastructure? Also, assumptions\non 100% availability and connection compliance (i.e., all cars being plugged in when parked\n>30 min) are highly optimistic and not validated.\nResponse 3.3: We thank the Reviewer for this valuable comment. To address these points,\nwe have conducted a series of additional sensitivity analyses and extended the discussion of\nsome of our key assumptions.\nSpecifically, we have added the new Supplementary Note 9: Sensitivity analysis, where we\nnow explore more systematically how variations in key parameters affect our main results,\nincluding:\n1. EV battery capacity: In addition to the current average battery size in Singapore\n(71 kWh), which is now used in the main text, we have also tested small batteries\n(30 kWh) and relatively large batteries (120 kWh). As shown in the new Supplementary\nFig. 16, reproduced here in Fig. R1-6, battery capacity has only a marginal impact on\nthe V2G performance in terms of reducing line loads. This result may appear counter-\nintuitive; however, higher-capacity batteries may not be fully utilized in the simulated\nV2G operations, potentially because the (dis)charging rate is limited to 7.2 kW (the\nimpact of the charging rate is discussed below) and local thunderstorms typically last\nonly a relatively short period (≈2 hours).\n2. (Dis)charging rates: We have additionally tested the impact of higher (dis)charging\nrates. As shown in our new Supplementary Fig. 17, reproduced here in Fig. R1-7, the\nbenefits of district-level V2G remain stable across different charging rates, whereas the\nperformance of system-level optimization varies substantially. In particular, on thun-\nderstorm days, system-level optimized V2G increasingly exacerbates line loadings as\ncharging rates rise. Even on days without thunderstorms, it leads to higher loads when\ncharging rates are high (50 kW). These findings highlight the importance of district-level\noptimization, especially as fast-charging infrastructure continues to expand [21].\n20\na b\n0 200 400 600 800 1000\nc Max load 'PV + uncontrolled charging' (MW) d\ne f\n)WM(\ndaol\nxaM\nBattery capacity: C = 30 kWh - Without thunderstorms\n1000 bat\n800\n600\nincrease\ndecrease\n0\n0 200 400 600 800 1000\nMax load 'PV + uncontrolled charging' (MW)\n)WM(\ndaol\nxaM\nBattery capacity: C = 30 kWh - With thunderstorms\n1000 bat\n800\n600\n400\n200\n0\n0 200 400 600 800 1000\nMax load 'PV + uncontrolled charging' (MW)\n)WM(\ndaol\nxaM\nBattery capacity: C = 71 kWh - Without thunderstorms\n1000 bat\n800\n600\n400\n200\n0\n0 200 400 600 800 1000\nMax load 'PV + uncontrolled charging' (MW)\n)WM(\ndaol\nxaM\nBattery capacity: C = 71 kWh - With thunderstorms\n1000 bat\n800\n600\n400\n200\n0\n0 200 400 600 800 1000\nMax load 'PV + uncontrolled charging' (MW)\n)WM(\ndaol\nxaM\nBattery capacity: C = 120 kWh - Without thunderstorms\n1000 bat\n800\n600\n400\n200\n0\n0 200 400 600 800 1000\nMax load 'PV + uncontrolled charging' (MW)\n)WM(\ndaol\nxaM\nDistrict-level optimization\nSystem-level optimization\nBattery capacity: C = 120 kWh - With thunderstorms\n1000 bat\n800\n600\n400\n200\nc,d 71 kWh. e,f 120 kWh. Each row compares system-level and district-level optimization\nwithout thunderstorms (a,c,e) and with thunderstorms (b,d,f). As in Fig. 2c-f in the main\ntext, the dashed line denotes the one-to-one reference between the maximum line load in the\n“PV+V2G” and “PV+uncontrolled charging” scenarios. Points above the line indicate cases\nwhere “PV+V2G” leads to higher line loads than “PV+uncontrolled charging”, while points\nbelow indicate load reduction. Red and blue lines represent linear fits to the system-level\noptimization (red data points) and district-level optimization (blue data points), respectively.\nWe acknowledge that assuming perfect EV availability and connection compliance represents\nan optimistic upper bound. While we did not explicitly vary the fraction of vehicles plugged\nin when parked, we conducted an additional sensitivity analysis with respect to EV adoption\nrates (see the new Fig. R1-8 in our Response 3.4). Even relatively low EV adoption levels\n(40% and below) are sufficient for district-level V2G to yield significant line load reductions.\nThis indicates that effective V2G operation can be achieved even with substantially lower\nEV availability and connection compliance than assumed in the main text. Nevertheless, this\naspect could be further quantified in a follow-up study. To address this point, we have added\nthe following new paragraph in the Supplementary Note 9:\nBecause the EV adoption rate reflects the number of vehicles available for V2G, and because\neven relatively low adoption levels (40% and below) still yield significant line load reductions,\ntheseresultsalsoindicatethateffectiveV2Goperationcanbeachievedevenwithsubstantially\nlower EV availability and connection compliance than assumed in the main text. Nevertheless,\nthis aspect could be further quantified in a follow-up study.\n21\na b\n0 200 400 600 800 1000\nMax load 'PV + uncontrolled charging' (MW)\nc d\ne f\n)WM(\ndaol\nxaM\nCharging rate: 7.2 kW - Without thunderstorms\n1000\n800\n600\nincrease\ndecrease\n0\n0 200 400 600 800 1000\nMax load 'PV + uncontrolled charging' (MW)\n)WM(\ndaol\nxaM\nCharging rate: 7.2 kW - With thunderstorms\n1000\n800\n600\n400\n200\n0\n0 200 400 600 800 1000\nMax load 'PV + uncontrolled charging' (MW)\n)WM(\ndaol\nxaM\nCharging rate: 30 kW - Without thunderstorms\n1000\n800\n600\n400\n200\n0\n0 200 400 600 800 1000\nMax load 'PV + uncontrolled charging' (MW)\n)WM(\ndaol\nxaM\nCharging rate: 30 kW - With thunderstorms\n1000\n800\n600\n400\n200\n0\n0 200 400 600 800 1000\nMax load 'PV + uncontrolled charging' (MW)\n)WM(\ndaol\nxaM\nCharging rate: 50 kW - Without thunderstorms\n1000\n800\n600\n400\n200\n0\n0 200 400 600 800 1000\nMax load 'PV + uncontrolled charging' (MW)\n)WM(\ndaol\nxaM\nDistrict-level optimization\nSystem-level optimization\nCharging rate: 50 kW - With thunderstorms\n1000\n800\n600\n400\n200\nc,d 30 kW. e,f 50 kW. Each row compares system-level and district-level V2G optimization\nwithout thunderstorms (a,c,e) and with thunderstorms (b,d,f). Symbols and notations are as\nin Fig. R1-6.\nComment 3.4: How sensitive are the findings to changes in EV adoption rate, PV capacity\nfactor, or frequency of thunderstorms?\nResponse 3.4: We thank the Reviewer for highlighting the importance of further assessing\nthe robustness of our results. Similar to Response 3.3, in the new Supplementary Note 9 we\nhave conducted additional sensitivity analyses on key model parameters:\n1. EV adoption rates: We have now also assessed the sensitivity of our results with respect\ntothetotalEVfleetsize, soastosimulatelow, medium, andhighadoptionfutures. The\nnew Supplementary Fig. 18, reproduced here in Fig. R1-8, shows that higher EV adop-\ntion levels increase the line load reduction potential of V2G, primarily due to the higher\ntotal available (dis)charging power. Across EV adoption levels (20–100%), district-level\nV2G consistently lowers the maximum line loads relative to uncontrolled charging, with\nthe advantage increasing with adoption but saturating beyond 80%. System-level con-\ntrol can again induce higher maximum line loads compared with uncontrolled charging\nscenario. This further underscores the robustness of the district-level V2G scheme. It\nalso suggests that in other tropical cities with higher levels of car ownership, the gains\nare likely to be at least as large as those observed in Singapore.\n22\na b\n0 200 400 600 800 1000\nc d\nMax load 'PV + uncontrolled charging' (MW)\ne f\ng h\ni j\n)WM(\ndaol\nxaM\nEV adoption: 20% - Without thunderstorms\n1000\n800\n600\nincrease\ndecrease\n0\n0 200 400 600 800 1000\nMax load 'PV + uncontrolled charging' (MW)\n)WM(\ndaol\nxaM\nEV adoption: 20% - With thunderstorms\n1000\n800\n600\n400\n200\n0\n0 200 400 600 800 1000\nMax load 'PV + uncontrolled charging' (MW)\n)WM(\ndaol\nxaM\nEV adoption: 40% - Without thunderstorms\n1000\n800\n600\n400\n200\n0\n0 200 400 600 800 1000\nMax load 'PV + uncontrolled charging' (MW)\n)WM(\ndaol\nxaM\nEV adoption: 40% - With thunderstorms\n1000\n800\n600\n400\n200\n0\n0 200 400 600 800 1000\nMax load 'PV + uncontrolled charging' (MW)\n)WM(\ndaol\nxaM\nEV adoption: 60% - Without thunderstorms\n1000\n800\n600\n400\n200\n0\n0 200 400 600 800 1000\nMax load 'PV + uncontrolled charging' (MW)\n)WM(\ndaol\nxaM\nEV adoption: 60% - With thunderstorms\n1000\n800\n600\n400\n200\n0\n0 200 400 600 800 1000\nMax load 'PV + uncontrolled charging' (MW)\n)WM(\ndaol\nxaM\nEV adoption: 80% - Without thunderstorms\n1000\n800\n600\n400\n200\n0\n0 200 400 600 800 1000\nMax load 'PV + uncontrolled charging' (MW)\n)WM(\ndaol\nxaM\nEV adoption: 80% - With thunderstorms\n1000\n800\n600\n400\n200\n0\n0 200 400 600 800 1000\nMax load 'PV + uncontrolled charging' (MW)\n)WM(\ndaol\nxaM\nEV adoption: 100% - Without thunderstorms\n1000\n800\n600\n400\n200\n0\n0 200 400 600 800 1000\nMax load 'PV + uncontrolled charging' (MW)\n)WM(\ndaol\nxaM\nLocal optimization\nSystem optimization\nEV adoption: 100% - With thunderstorms\n1000\n800\n600\n400\n200\ne,f 60%. g,h 80%. i,j 100%. Each row compares system-level and district-level optimization\nwithout thunderstorms (a,c,e,g,i) and with thunderstorms (b,d,f,h,j). Symbols and notations\nare as in Fig. R1-6.\n2. PV capacity factor: To test the robustness of our results against changes in the PV\ncapacity factor, we have re-scaled the solar generation profiles. Our new Supplementary\nFig. 19, reproduced here in Fig. R1-9, shows that district-level optimization generally\noutperforms system-level control, with the difference becoming more pronounced at\nhigher PV capacities. On thunderstorm days, system-level optimization may again in-\ncrease line loading, whereas district-level optimization remains robust. As such, these\nresults again reinforce the conclusions in the main text.\n23\na b\n0 200 400 600 800 1000\nMax load 'PV + uncontrolled charging' (MW)\nc d\ne f\n)WM(\ndaol\nxaM\nPV capacity: BAS 2050 (2.5 GW ) - Without thunderstorms\n1000 p\n800\n600\nincrease\ndecrease\n0\n0 200 400 600 800 1000\nMax load 'PV + uncontrolled charging' (MW)\n)WM(\ndaol\nxaM\nPV capacity: BAS 2050 (2.5 GW ) - With thunderstorms\n1000 p\n800\n600\n400\n200\n0\n0 200 400 600 800 1000\nMax load 'PV + uncontrolled charging' (MW)\n)WM(\ndaol\nxaM\nPV capacity: ACC 2050 (5 GW) - Without thunderstorms\n1000 p\n800\n600\n400\n200\n0\n0 200 400 600 800 1000\nMax load 'PV + uncontrolled charging' (MW)\n)WM(\ndaol\nxaM\nPV capacity: ACC 2050 (5 GW) - With thunderstorms\n1000 p\n800\n600\n400\n200\n0\n0 200 400 600 800 1000\nMax load 'PV + uncontrolled charging' (MW)\n)WM(\ndaol\nxaM\nPV capacity: NEA 2050 (7 GW) - Without thunderstorms\n1000 p\n800\n600\n400\n200\n0\n0 200 400 600 800 1000\nMax load 'PV + uncontrolled charging' (MW)\n)WM(\ndaol\nxaM\nPV capacity: NEA 2050 (7 GW) - With thunderstorms\n1000 p\n800\n600\n400\n200\n0\n0 200 400 600 800 1000\nMax load 'PV + uncontrolled charging' (MW)\n)WM(\ndaol\nxaM\nPV capacity: MAX 2050 (8.6 GW) - Without thunderstorms\n1000 p\n800\n600\n400\n200\n0\n0 200 400 600 800 1000\nMax load 'PV + uncontrolled charging' (MW)\n)WM(\ndaol\nxaM\na b\nc d\ng h\ne f PV capacity: MAX 2050 (8.6 GW) - With thunderstorms\n1000 p\n800\n600\n400\n200\nscenarios. a,b ’ACC 2050’ with 5.0 GW . c,d ’NEA 2050’ with 7.0 GW . e,f ’MAX 2050’\np p\nwith 8.6 GW . These represent accelerated (‘ACC 2050’), near-maximum (‘NEA 2050’),\np\nand maximum (‘MAX 2050’) PV deployment scenarios for 2050, respectively. Each row\ncompares system-level and district-level optimization without thunderstorms (a,c,e) and with\nthunderstorms (b,d,f). Symbols and notations are as in Fig. R1-6.\nRegardingthunderstormfrequency, webelievethatdetailedmeteorologicalmodelingisbeyond\nthe scope of this study. Nevertheless, we address this aspect indirectly by incorporating a\nwide range of PV output variability across the simulated two-month period. Importantly,\nthe proposed district-level EV charging optimization effectively mitigates line loads on both\nthunderstorm and non-thunderstorm days (Fig. 2d,f). We therefore expect this result to hold\nacross a broad spectrum of plausible thunderstorm frequencies.\nOverall, our extensive analyses clearly show that the district-level V2G optimization remains\nhighly effective across a wide range of different operating conditions.\nIn addition to the Supplementary Note 9, we have added the following sentence in the main\ntext, section ‘Mitigating the impact of thunderstorms through EV charging’:\nAn extensive sensitivity study shows that district-level V2G optimization remains effective\nacross a wide range of different operating conditions, including varying battery capacities,\n(dis)chargingrates, EVadoptionlevels, andPVintegrationscenarios(SupplementaryNote9).\nneeds to be statistically qualified.\nResponse 3.5: We thank the Reviewer for this helpful comment. To address this point,\nwe now explicitly report the mean and standard deviation of these peak load reductions in\nthe main text (section ‘Mitigating the impact of thunderstorms through EV charging’,\n3rd\nparagraph):\nDistrict-level EV charging optimization, in contrast, is able to substantially reduce the peak\nloads compared to the combination of PV with uncontrolled EV charging (Fig. 2f), yielding\nreductions of 18.1 ± 11.8% (mean ± standard deviation).\nComment 3.6: The analysis focuses solely on line load magnitudes and peak demand, ne-\nglecting critical metrics like voltage stability, frequency deviations, line losses, and transformer\nloading. Without this, the real operational impact on the grid cannot be fully assessed.\nResponse 3.6: We thank the Reviewer for drawing our attention to this important point.\nIn response, we have conducted a comprehensive AC power flow analysis to investigate the\nmethod’s influence on the grid.\nThe results in our new Supplementary Fig. 20, reproduced here in Fig. R1-10, show that\nall bus voltages remain within acceptable operating limits (0.95−1.05 p.u.), confirming that\nthe proposed V2G strategy does not introduce undesirable voltage deviations. At the same\ntime, the AC power flow analysis confirms the line load mitigation potential, see the new\nSupplementary Fig. 21, reproduced here in Fig. R1-11.\nWith respect to frequency deviations, our analysis is based on steady-state AC power-flow\nmodeling. In this framework, generation is dispatched to balance demand at each time step,\nandthesystemfrequencyremainsnominal(50Hz). Consequently, explicitfrequencydynamics\nsuch as inertia, governor response and AGC behavior, lie outside the scope of this study\n(Supplementary Fig. S12 shows the required fast-reacting gas turbine capacity).\na b\n10 20 30 40 50 60 70\nNode index\n).u.p(\negatloV\nWithout thunderstorms\n1.06\nSystem-level optimization\n1.04\nDistrict-level optimization\nPV + uncontrolled charging\n1.02\n1\n0.98\n0.96\n0.94\n10 20 30 40 50 60 70\nNode index\n).u.p(\negatloV\nWith thunderstorms\n1.06\n1.04\n1.02\n1\n0.98\n0.96\n0.94\nFigure R1-10: Voltage levels in the AC power flow analysis. a Voltage levels on days\nwithout thunderstorms. b Voltage levels on days with thunderstorms. The shaded area\ndenotes the envelope between the minimum and maximum bounds of the simulations, all of\nwhich fall within the acceptable range of variation (indicated by the dashed lines).\n25\na b\n0 50 100 150\nMaximum load: 'PV+uncontrolled charging' (MW)\n)WM(\ndaol\nmumixaM\nWithout thunderstorms\n140\n1\n1\n0\n2\n0\n0\nload\nincrease\n80\n4\n6\n0\n0\nload\ndecrease\nLocal optimization\n0\n0 50 100 150\nMaximum load: 'PV+uncontrolled charging' (MW)\n)WM(\ndaol\nmumixaM\nWith thunderstorms\n140\n120\n100\n80\n60\n40\n20\nloads in the system-level and district-level “PV+V2G” scenarios versus the “PV+uncontrolled\ncharging” scenario on days without thunderstorms. b Same analysis for days with thunder-\nstorms. Consistent with the results from the DC power flow approximation in the main text\n(Fig. 2), district-level V2G optimization clearly reduces the maximum line loads due to thun-\nderstorms, whereas system-level V2G optimization exacerbates the maximum loads.\na b c\n0 5 10 15 20\nHour of day\n)WM(\nssol\nrewop\nmetsyS\nWithout thunderstorms\n70\n60\n50\n40\n30\n0 5 10 15 20\nHour of day\nPV + uncontrolled charging\nSystem-level optimization\nDistrict-level optimization\n)WM(\nssol\nrewop\nmetsyS\nWith thunderstorms\n70\n60\n50\n40\nthunderstorms thunderstorms\nPV + uncontrolled charging\nSystem-level optimization\nDistrict-level optimization\n)hWM(\nssol\nygrene\nyliad\nlatoT\na b c\n1400\n1200 1151 1183 10761076\n9891009\n1000\n800\n600\n400\n200\n0\n0 5 10 15 20\nHour of day\nUncontrolled charging\nSystem-level optimization\nDistrict-level optimization\n)WM(\nssol\nrewop\nmetsyS\nWithout thunderstorms\n70\n60\n50\n40\n30\n0 5 10 15 20\nHour of day\nPV + uncontrolled charging\nSystem-level optimization\nDistrict-level optimization\n)WM(\nssol\nrewop\nmetsyS\nWith thunderstorms\n70\n60\n50\n40\nthunderstorms thunderstorms\nPV + uncontrolled charging\nSystem-level optimization\nDistrict-level optimization\n)hWM(\nssol\nygrene\nyliad\nlatoT\n1400\n1200 1151 1183 10761076\n9891009\n1000\n800\n600\n400\n200\nSystem-level optimization\nDistrict-level optimization\nFigure R1-12: System power loss and energy loss. a,b Average system power loss during\ndays without thunderstorms (a) and with thunderstorms (b). c Average daily energy loss.\nOur AC power flow analysis also quantifies line losses, which are about ≈10% lower in the\ntwo “PV+V2G” scenarios compared to the “PV+uncontrolled charging” scenario, see the new\nSupplementary Fig. 22, reproduced here in Fig. R1-12, which further enhances the economic\nviability of the general V2G operation.\nFinally, our new Supplementary Fig. 23, reproduced here in Fig. R1-13, presents the trans-\nformer loadings, all of which remain within their rated capacities. The results show that\nboth system-level and district-level optimizations reduce transformer loading compared with\nuncontrolled charging.\n26\n0 100 200 300 400 500 600\nTransformer loading: 'PV+uncontrolled' (MVA)\n)AVM(\ngnidaol remrofsnarT\n400 kV→230 kV (500 MVA) — Without thunderstorms\n500\n2\n3 4\n0\n0 0\n0\n0 0\nl\nl\no\no\na\na\nd\nd i\nd\nn\ne\nc\nc\nre re a a s s e e\nSystem-level\n0\n0 100 200 300 400 500 600\nTransformer loading: 'PV+uncontrolled' (MVA)\n)AVM(\ngnidaol remrofsnarT\n400 kV→230 kV (500 MVA) — With thunderstorms\n600\n500\n400 300\n200\n100\n0\n0 40 80 120 160 200 240\nTransformer loading: 'PV+uncontrolled' (MVA)\n)AVM(\ngnidaol\nremrofsnarT\n230 kV→66 kV (200 MVA) — Without thunderstorms\n240\n200\n160\n120\n80\n40\n0\n0 40 80 120 160 200 240\nTransformer loading: 'PV+uncontrolled' (MVA)\n)AVM(\ngnidaol\nremrofsnarT\na b\nc d\n230 kV→66 kV (200 MVA) — With thunderstorms\n240\n200\n160\n120\n80\n40\nMaximum loading of the 400-kV/230-kV transformers on days without (a) and with (b) thun-\nderstorms; transformer rating: 500 MVA. c,d Maximum loading of the 230-kV/66-kV trans-\nformers on days without (c) and with (d) thunderstorms; transformer rating: 200 MVA. In all\ncases, both system- and district-level “PV+V2G” reduce the transformer loadings compared\nto “PV+uncontrolled charging”, and the loadings are within the rated capacities.\nIn the revised manuscript, we have summarized this additional AC power flow study in the\nnew Supplementary Note 10: AC power flow analysis, which is now referenced in the main\ntext (section ‘Mitigating the impact of thunderstorms through EV charging’):\nMoreover, a detailed AC power flow analysis shows that network voltage levels remain within\nacceptable limits (Supplementary Note 10).\nComment 3.7: While mobile phone data is used to simulate EV mobility, the accuracy\nand resolution of this input is not quantified. What is the uncertainty in vehicle location\nor mode inference? Also, how is the transition from aggregate phone data to specific EV\nbehavior validated?\nResponse 3.7: We thank the Reviewer for this comment, and we apologize for the lack\nof clarity with respect to the accuracy and validation of mobile phone data for EV mobility\nmodeling.\nThe underlying GPS dataset provides relatively high spatial accuracy, with location errors no\ngreater than 250 meters after pre-processing. This resolution is sufficient for our study, which\nfocuses on larger-scale mobility patterns rather than fine-grained trajectory details. To make\nthis point more clear, we have added the following sentence in the Supplementary Note 4,\nsection ‘Mobile phone data pre-processing’:\nSubsequently, data points with a low GPS signal accuracy were discarded, so that all points\nhave location errors no greater than 250m after pre-processing. This resolution is sufficient\nfor our study that focuses on larger-scale mobility patterns rather than fine-grained trajectory\ndetails.\nWhileweinferthemobilitypatternsofEVsfrommobilephonedatabyfollowingtheestablished\nmethod of Xu et al. [31], we also conducted a series of validations at multiple stages against\nindependent Singapore datasets so as to ensure accuracy and representativeness:\n1. Validation of mobile phone data as a proxy for general mobility patterns: Both home\nand work locations derived from mobile phone data are in strong agreement with census\ndata (Supplementary Note 4, Supplementary Figs. 4 and 5).\n2. Validation of mode inference and vehicle location:\n(a) The daily vehicle-kilometers traveled derived from mobile phone data is in strong\nagreement with the reported value from Singapore’s transport agency (Supple-\nmentary Note 4, section ‘Extraction and validation of EV-based travel’). This also\nshows that the total energy consumption of EVs is accurate.\n(b) As a more fine-grained spatio-temporal validation, the distribution of departure\nand arrival times extracted from the GPS data aligns well with publicly available\nstatistics from carpark availability data (Supplementary Note 4, Supplementary\nFig. 9).\nThese validations underpin the representativeness of the mobile phone data input used in\nour simulations, particularly at the more aggregated level required for the district-scale V2G\nmodeling. We also note that the general method of deriving EV mobility from mobile phone\ndata has been extensively used and validated in previous studies in different geographic areas,\nfor instance in references [31] and [48].\nTo make it more clear that we have conducted thorough validation analyses, we have added\nthe following sentence in the main text, at the end of the section ‘PV integration and EV\ncharging scenarios’ (see also our Response 2.2 to Reviewer #2):\nA series of validation studies using independent ground-truth datasets, including census data\nand carpark occupancy records, confirm that mobile phone data are sufficiently representative\nacrossSingaporeandthatourEVmobilityestimatesareconsistentwithempiricalobservations\n(details in Supplementary Note 4).\nstrong result but must be contextualized against line ratings and safety margins.\nResponse 3.8: We thank the Reviewer for this helpful suggestion. To provide the necessary\ncontext, we have added a sentence that explicitly links such large load increases to line ratings\nand security margins. The revised text now includes the following concise sentence (line 135):\nOn several lines, maximum loads increase by more than 100% on days with thunderstorms\n(characterized by strong irradiance fluctuations) compared with cloud-free, high-irradiance\ndays. This shows that even conservative security margins of 33-50% can be insufficient if line\nflow limits do not account for thunderstorms (a 100% load increase corresponds to a required\nsecurity margin of 50%).\nInaddition,wehaveincludedamoresystematicsensitivityanalysisshowingthatthunderstorms\nalso increase the number of lines exceeding their limits in the “PV+uncontrolled charging”\nscenario when line flow limits are determined from the base (“no PV, no EV”) scenario. These\nresultsarepresentedinthenewSupplementaryFig. 15inSupplementaryNote8(wherehigher\nvalues of the flow-limit scaling factor α correspond to higher security margins).\nComment 3.9: Therearesomeredundancieswithinthetext, e.g., theduckcurveisexplained\nin both the intro and discussion. Also, some terms (e.g., ”baseline demand”) are not clearly\ndefined early on.\nResponse 3.9: We thank the Reviewer for this helpful observation. In response, we have\ncarefully reviewed the manuscript for redundancy and terminological clarity.\nWith respect to the explanation of the duck curve, we noted that the phenomenon was\nexplained in both the Introduction and Supplementary Note 1. To improve conciseness and\navoid repetition, we have removed the redundant explanation in Supplementary Note 1.\nTo more clearly define the term “baseline (or base) demand” at the first mention, we have\nextended the text in section ‘PV integration and EV charging scenarios’ (first paragraph) as\nfollows:\n[...] For the same year, the city’s peak base electricity demand (i.e., without EVs and PV) is\nestimated to be 11.5 GW (see Methods) [...]\nMoreover, we have slightly revised the Methods to better define the term:\n[...] The base electricity demand (not considering PV and EV integration) was modeled using\nthe bottom-up approach [...]\nFinally, we have also conducted a broader review to eliminate other minor redundancies,\nimprove the mathematical notation, and ensure that all key technical terms are defined at\nfirst use.\nWe thank the Reviewer for the careful assessment of our code in our Code Ocean capsule.\nWe\nhave now fully transitioned to a GitHub repository, where all datasets, codes, and associated\ndocumentations are hosted:\nhttps://github.com/tropicalcityv2g-hash/Powering-tropical-cities\nA permanent link to this GitHub repository will be included in the paper upon publication.\nAll the revised code, incorporating the Reviewer’s suggestions, is available in this repository.\nThe Code Ocean capsule has not been updated.\nComment 3.10: The linear model assumes error-free input and doesn’t report confidence\nintervals for 2030/2050 forecasts.\nResponse 3.10: We thank the Reviewer for this helpful observation. In the revised analysis,\nwe now use the 2050 projection of 11.5 GW reported by the Solar Energy Research Institute\nof Singapore (SERIS) [14] for the peak base demand. As such, we have removed this linear\nmodel.\nAccordingly, we have revised the text of Supplementary Note 2 as follows:\nForthepeakbasedemand,weusethe2050projectionof11.5GWreportedbytheSolarEnergy\nResearch Institute of Singapore (see table 5.9 in [14]), which is based on an extrapolation\nthat does not account for different charging patterns of a fully electrified vehicle fleet (a\nlinear extrapolation of historical peak demand data, again without accounting for future EV\ncharging, yieldsasimilarestimate). AsshowninSupplementaryFig.1, thehalf-hourlydemand\nprofile has a small variation across the year. Therefore, we assume that the relative half-hourly\nprofile in 2050 (in percentage of the peak base demand) is the same as the average profile of\n2022 (the solid curve in Supplementary Fig. 1, divided by its maximum value).\nMoreover, in the Methods section (first paragraph) we adapted the text to:\nNext, the future demand profile for the year 2050 was obtained by scaling the peak demand to\n11.5 GW, as projected by the Solar Energy Research Institute of Singapore [14], and assuming\nthat the relative half-hourly variations remain unchanged (Supplementary Note 2). [...]\nComment 3.11: There’s no train/test split, k-fold CV, or comparison with other models\n(e.g., exponential smoothing, time series models).\nResponse 3.11: Thank you for this helpful comment. We agree that, in principle,\nvalidation approaches such as train/test splits, k-fold cross-validation, or benchmarking against\nalternative forecasting models (e.g., ARIMA, exponential smoothing) would strengthen the\nrobustness of the analysis. However, in our case, the available dataset consisted of only 13\nannual peak demand observations (2009-2022, with data for 2020 removed). This limited\nsample size makes it impractical to perform data-intensive validation or model comparison\nwithout substantially reducing the information available for model training.\nAs mentioned in Response 3.10, we have removed this part, and we now use the projection\nreported by SERIS [14] for the peak base demand of 2050.\nconstraints (e.g., Kirchhoff’s laws, capacity ratings) are enforced.\nResponse 3.12: We thank the Reviewer for this important technical observation. Indeed,\nthe main purpose of the visualization (Supplementary Fig. 10 in the revised Supplementary\nInformation) was to illustrate the topology of the studied electricity network together with a\nsnapshot of the flow directions.\nIn the revised code, we have added a clarifying comment at the very beginning to indicate that\nthe figure serves only to illustrate the topology. To avoid confusion, we have also removed\nthe arrows (see our Response 3.16).\nWe emphasize that in all power flow simulations, actual power system constraints such as\nKirchhoff’s Law are enforced and capacity ratings considered (see Supplementary Notes 5,\n10\nand 13).\nComment 3.13: Thunderstorm modeling is hardcoded via single-day assumptions, which is\nscientifically fragile.\nResponse 3.13: We thank the Reviewer for raising this important point. We believe the\ncomment refers to the thunderstorm day illustrated in Fig. 1c and Fig. 2a,b, where we show\nthe impact of severe thunderstorms on line loading and the potential of EVs to mitigate this\nimpact. We would like to clarify that, although these figures use a selected representative\nday, their purpose is illustrative, to demonstrate how sudden PV generation drops during\nthunderstorms can stress the grid and how V2G can help alleviate these effects.\nIn our study, we used two months of solar irradiance data, which includes 30 days with sig-\nnificant thunderstorm-induced PV variability. Our broader simulation framework incorporates\nthe full multi-day PV output variability and therefore generalizes the illustrative observations\nshown in Fig. 1c and Fig. 2a,b. The comprehensive results are presented in Figs. 1d, 2c-f\nand 4.\nIn the revised code for generating the figures, we have added several clarifying comments that\nthe single-day examples (Fig. 1c and Fig. 2a,b) are for illustrative purposes only, and that\nthe full simulations (Figs. 1d, 2c-f and 4) load and process multi-day PV output data.\nComment 3.14: Plots use cool, hot, or custom colors without consistency. This may be\nmisleading in comparative visuals.\nResponse 3.14: We thank the Reviewer for pointing us to this inconsistency in our color\nschemes. To improve clarity and comparability, we have revised all figures to use a consistent\ntwo-color diverging scheme (blue–red series) across scenarios.\nComment 3.15: No axis labels or color bars for power flow which violates best practices for\nscientific figures.\nResponse 3.15: We thank the Reviewer for this helpful comment. We agree that clear axis\nlabels and color bars are essential for scientific clarity and reproducibility. For all revised panels\nin Fig. 1b (which is now the only figure illustrating power flows; see also our Response 3.12),\n31\nwe have updated the color bar indicating the flow magnitude of each line, normalized by the\nmaximum value of the same day, and retained the distance scale to indicate the geographic\nextent.\nComment 3.16: Arrow plots are overloaded without meaningful scaling — units are unclear.\nResponse 3.16: We thank the Reviewer for this helpful observation. The original arrow\nplots of the previous Supplementary Fig. 9 (now Supplementary Fig. 10) were intended to\nillustrate the topology of Singapore’s electricity network, particularly highlighting the dense\nconnectivity in the downtown core. To improve clarity, we have taken the following steps:\n• Wehaveremovedthearrowstomakeitmoreclearthattheplotsaresolelyforillustrating\nthe network topology (see our Response 3.12),\n• added a clarifying comment at the beginning of the code that the figure is for illustrating\nthe network topology only,\n• made the zoom-in area visually more clear.\nAs mentioned in our Response 3.15, in Fig. 1b (now the only figure illustrating power flows),\nwe have updated the color bar indicating the flow magnitude of each line. Each value is\nnormalized by that day’s maximum and is therefore dimensionless.\nComment 3.17: Interpolation from 30-min to 15-min steps is done with spline but never\nvalidated. This could introduce artifacts — a plot of original vs. interpolated would help.\nResponse 3.17: We thank the Reviewer for this helpful comment. In response, we have\nplotted the original 30-minute time series alongside the interpolated 15-minute series (see\nFig. R1-14), as suggested. 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The paper now presents a clearer, more policy-relevant contribution on\nintegrating solar photovoltaics (PV) and electric vehicles (EVs) for urban energy resilience in tropical cities, with Singapore\nas a case. Importantly, the revised manuscript aligns its technical choices and findings with specific 2030 targets under the\nSingapore Green Plan 2030—notably for solar deployment, energy storage, and EV infrastructure—thereby strengthening\nboth academic and applied value.\nThe authors have strengthened the manuscript in several key areas as below:\n1. Clarification of PV Challenges in Tropical Climates: The authors have significantly improved the discussion of the unique\noperating challenges faced by PV systems in tropical regions. Clearer descriptions of intra-day PV fluctuations and their grid\nimpacts in the tropics, with Singapore-specific context and references to forecast tools and variability events, better motivate\nthe study’s control and optimization focus as solar scales to the Green Plan’s 2030 threshold. These revisions enhance the\ncontextual framing and strengthen the motivation for the study.\n2. Expanded Explanation of EV Roles and District-Level Charging Strategies: In response to earlier comments, the authors\nhave added more detailed discussion on the potential of EVs as flexible, mobile energy storage units. The revised text\nclarifies district charging patterns and operational flexibility (managed charging/V2G) as complementary grid resources that\ncan absorb midday PV and support evening ramps—exactly the operational need created by high solar penetration\nenvisioned under the Green Plan. The district-level EV charging strategy is now better substantiated with references to\nmobility data and urban activity patterns, making the argument much more convincing. The revised text shows clearer links\nbetween mobility patterns, charging behavior, and real-time grid balancing.\n3. Improved Presentation of Scenarios and Simulation Approach\nThe authors have expanded and clarified the simulation framework, including the definitions and assumptions behind the\nfour key scenarios (“PV only,” “PV + uncontrolled charging,” “V2G only,” and “PV + V2G”). Clearer parameterization and\npolicy linked scenario rationales, e.g., using forecast-driven and district-level controls as one would expect in a power\nsystem targeting ≥2 GWp solar by 2030 with expanded charging networks and ESS support. These additional explanation of\noptimization techniques and district-level load balancing enhances the technical transparency of the study. These revisions\nmake the methodology more reproducible and scientifically rigorous.\n4. Strengthened Presentation of Key Findings\nThe manuscript now provides a more coherent and detailed analysis of how district-level EV charging can mitigate grid\nstress, smooth PV fluctuations, and address the duck curve. The authors have sharpened the interpretation of results,\nclarified regional implications, and provided stronger empirical grounding. The integration of mobility data into the energy\nmodel is now clearly articulated as a key innovation. By placing results within the Green Plan’s trajectory, the manuscript\nnow offers a credible operational pathway for integrating ≥2 GWp PV by 2030 with pervasive EV charging. The interpretation\nnow explicitly references national targets (solar capacity, EV chargers) and grid tools (forecasting, ESS).\n5. Broader Considerations of Data, Infrastructure, and Policy\nThe discussion now systematically addresses the data availability (e.g., anonymized mobility and charger telemetry),\ninfrastructure readiness (grid hosting capacity, siting across HDB/commercial carparks), and policy frameworks (EV\nCharging Act, TR25 safety standards, licensing, uptime and data sharing) that underpin a scalable, safe, and reliable EV PV\nintegration strategy. The strengthened results section shows that district level, optimized EV charging and V2G (a) reduce\nfeeder/substation loading, (b) absorb midday PV, and (c) flatten evening ramps—all of which are operational enablers for the\nGreen Plan’s solar scale up. This complements national measures like short term solar forecasting and energy storage\ndeployment intended to manage intermittency as PV grows.\nOverall Assessment\nThe authors have constructively and comprehensively addressed Comments 1.1–1.7. The manuscript is now clearer, more\nrigorous, and—critically—policy aligned with the Singapore Green Plan 2030 targets for Solar PV (1.5 GWp by 2025—\nachieved; ≥2 GWp by 2030), Energy Storage (≥200 MWh by 2025—achieved early), and EV infrastructure & adoption\n(cleaner energy new cars from 2030; ~60,000 chargers by 2030). The integration of district level EV charging/V2G with\nurban mobility data offers a credible operational pathway to mitigate intermittency and the duck curve while enabling deeper\nPV penetration in a tropical, dense urban system like Singapore.\nRecommendation: I am satisfied that all suggested revisions have been adequately addressed, and I recommend the\nrevised manuscript for publication.\nThank you.", "reviewer_2": "The authors have carefully addressed the reviewers' comments. It is acceptable from my point of view."}, "author_response": ""}]

Decision letter