Assessing Carbon Reduction Potential of Rooftop PV in China through Remote Sensing Data-Driven Simulations
Round 1
Reviewer 1 Report
Review Comments on manuscript sustainability-2181044:
In this paper, a technical framework based on multi-source remote sensing data to fill the gap in assessing carbon reduction potential. The spatio-temporal variations in rooftop PV generations are first simulated on an hourly basis, and then a dispatch analysis is performed in combination with hourly load profiles to quantify the PV curtailment in different scenarios. Our results show that the total rooftop PV potential in China reaches 6.5 PWh yr-1, mainly concentrated in the eastern region where PV generation is highly variable. The carbon reductions from 100% flexible grids with 12 hours of storage capacity are close to the theoretical maximum, while without storage, the potential may be lost by half. The topic is interesting. However, there are several concerns about this manuscript.
1) In the statement “Global installed PV capacity has grown more than eightfold in the last 10 years, providing about 3.6% of the world' total electricity consumption in 2020 [3]” on page 1 (lines 28 to 30), it is recommended to provide a more recent reference and percentage.
2) The authors should provide critical review about previous studies, other than simply description of the previous studies.
3) References should be provided for all the statements in the introduction. For some statements, references are not provided.
4) References should be provided for the figures that are taken from the published literature. For example, Figure 5 (a) seems to be taken from the literature. Similar should be done for other figures taken from the literature.
5) The novelty(s)/contribution(s) of the study is not clear. I recommended that the authors should clearly explain that in the last paragraph of the introduction section using bullets.
6) The conclusions of the study should be presented in bullets.
7) References should be provided for the equations in the paper that are taken from other published literature.
8) The comments in the attached PDF manuscript documents should also be addressed.
9) All the figures should be provided in high quality. In some of the figures, the text is not readable.
10) It is highly recommended to improve the write up of the paper.
The followings are several minor points to be considered.
1) “Global installed” should be changed to “Globally Installed” (Page.1 (Line 28)).
2) Space should be added before the word “to” (Page.3 (Line 106)).
Author Response
Response to Reviewer 1:
Dear Reviewer:
Thank you for your comments concerning our manuscript entitled “Assessing carbon reduction potential of rooftop PV in China using multi-source remote sensing data” (ID: sustainability-2181044). Those comments are all valuable and very helpful for improving our paper. We have studied comments carefully and made correction. Our draft is revised in Revised Mode of Microsoft Word to ensure the revised portion obvious. Our responses are shown in “Blue” color and the changes in the manuscript are shown in “Red” color.
Comments: In this paper, a technical framework based on multi-source remote sensing data to fill the gap in assessing carbon reduction potential. The spatio-temporal variations in rooftop PV generations are first simulated on an hourly basis, and then a dispatch analysis is performed in combination with hourly load profiles to quantify the PV curtailment in different scenarios. Our results show that the total rooftop PV potential in China reaches 6.5 PWh yr-1, mainly concentrated in the eastern region where PV generation is highly variable. The carbon reductions from 100% flexible grids with 12 hours of storage capacity are close to the theoretical maximum, while without storage, the potential may be lost by half. The topic is interesting. However, there are several concerns about this manuscript.
Response: Thank you for your encouraging comments.
Comment 1): In the statement “Global installed PV capacity has grown more than eightfold in the last 10 years, providing about 3.6% of the world' total electricity consumption in 2020 [3]” on page 1 (lines 28 to 30), it is recommended to provide a more recent reference and percentage.
Response: Thank you for your suggestion. In this revision, we collect the total electricity consumption in 2021 as a reference. We have updated the percentage and citation as “Globally installed PV capacity has grown more than eightfold in the last 10 years, providing about 3.6% of the world' total electricity consumption in 2021 [3]”
- IEA. Solar pv; International Energy Agency (IEA): Paris, 2022.
Comment 2): The authors should provide critical review about previous studies, other than simply description of the previous studies.
Response: Thank you for your suggestion. We have provided critical review after the description of the previous studies. The related parts are as follows.
“Several studies have proposed methods to assess rooftop PV potential, which can be broadly classified into geographic information system (GIS) based methods and remote sensing (RS) based methods [11] …GIS-based methods are usually suitable for fine-scale spatio-temporal assessments, and the results can be employed to design effective policies for rooftop PV development in built environments [11]. However, the intensive computational demand is a main obstacle to their application on a large scale [13,16].
The typical RS-based approach integrates multi-source remote sensing data to assess the regional potential of rooftop PV, and the main work usually consists of two aspects, namely building footprint extraction and solar resource estimation [7,19]…Compared to GIS-based methods, remote sensing makes large-scale assessment a reality and the integration of deep learning significantly improves the computational efficiency [7,11]. Therefore, RS-based methods are typically applied to large-scale resource estimation and spatial planning, but not appliable for the design and integration of individual rooftop PV systems [19,27].
In parallel, the light detection and ranging (LiDAR) technology has contributed to the accurate simulation of PV electricity generations at urban scales [28,29]…This kind of solutions own the advantages of both RS-based and GIS-based ones, while the high cost of LiDAR is the main reason why they are not yet widely used.
In contrast to the refined assessment of PV power generation potential, the estimation of PV carbon reduction capacity is relatively crude [32] …There are two main problems associated with such approach. First, the fine-scale spatial and temporal variability of PV power generation is not considered. The variability leads to a mismatch between PV generation and user-side demand, thus a portion of PV electricity is to be curtailed during dispatching, that is, not all PV electricity can be delivered to the grid and then consumed by end users [34,35]. Second, the impact of the grid's own characteristics is ignored. It is known that the grid's ability to absorb variable generations varies with different system flexibility and energy storage capacity [36,37]. In addition, since clean energy already exists in the grid [2,38], it is unlikely that one unit of rooftop PV electricity will replace an equivalent amount of power in the current grid.”
Comment 3): References should be provided for all the statements in the introduction. For some statements, references are not provided.
Response: In this revision, we have added citations for the statements without reference.
Comment 4): References should be provided for the figures that are taken from the published literature. For example, Figure 5 (a) seems to be taken from the literature. Similar should be done for other figures taken from the literature.
Response: In this revision, we have provided the references for all the figures that are taken from the publicly available literature or sources. These figures include Figures 2, 3 and 5. The revised captains for these figures are as follows.
“Figure 2. Settlement and building footprints. (a) Settlement area in China at a spatial resolution of 500m, aggregated from the world settlement footprint products [43]; (b) Building rooftop area at a spatial resolution of 500m, which are calculated based on sub-meter building footprints in Jiangsu Province, China [7].
Figure 5. (a) Spatial division of China's regional grids [53]; (b) Typical daily load profiles of Beijing grid; (c) Annual variations in base and peak loads of Beijing grid.
Figure 6. (a) Renewable energy penetration rate of each regional grid (numbers are sourced from https://www.bjx.com.cn/); (b) Total carbon emissions of the corresponding regions in each regional grid in 2019 [54]. ”
Comment 5): The novelty(s)/contribution(s) of the study is not clear. I recommended that the authors should clearly explain that in the last paragraph of the introduction section using bullets.
Response: Thank you for your suggestion. We have explained the contributions of this study in the last paragraph of the introduction as “The main contributions of this study embody three aspects:
1) The high-resolution mapping on the distribution of China's rooftop PV potential. An empirical relationship is established to estimate the rooftop area from settlement area. Multi-source remote sensing data are integrated to simulate the spatio-temporal variation of rooftop PV electricity generation.
2) The technical framework for calibrating the overestimation of carbon emission reduction. Rooftop PV generation curves and customer-side load profiles are combined to obtain the PV curtailment rates during electricity dispatch. Current PV penetration rates are used to calibrate the grid emission factors for PV-specific ones.
3) The quantification of carbon reductions of China's rooftop PV. We design twelve scenarios with 80%, 90% and 100% flexibility and 0, 4, 8 and 12 hours of storage capacity to reflect the differences in the grid's ability to absorb intermittent PV electricity.”
Comment 6): The conclusions of the study should be presented in bullets.
Response: Thank you for your suggestion. In this revision, we have presented the conclusion in bullets as “In this study, we design a technical framework for integrating multi-source remote sensing data to assess the carbon reduction potential of rooftop PV. The key point is to obtain PV curtailment rate through a dispatch model based on the spatio-temporal simulations of hourly PV generation and the load profiles of each grid. The main conclusions include:
1) The maximum electricity generation of rooftop PV in China can reach 6.5 PWh yr-1, of which more than 80% is concentrated in densely populated areas in the east and characterized by high variability.
2) Unlocking China's full rooftop PV potential can reduce CO2 equivalent emissions by 2.3–5.2 Gt, depending on the grid flexibility and storage capacity.
3) The potential carbon reductions can offset 21%–47% of China's total emissions using the data in 2019 as a reference, thus could make a significant contribution to carbon neutrality.
4) Both carbon reductions and their offset rates vary greatly from grid to grid, highlighting the need for rooftop PV development plans tailored to local conditions.”
Comment 7): References should be provided for the equations in the paper that are taken from other published literature.
Response: In this revision, we have added references for the equations that are taken from other published literature. This equations include Equations 2, 4, 5, and 6. The revised parts are as follows.
“We assume that all rooftop PV systems are south-facing and tilted at an optimal angle () that varies with the latitude () and diffuse fraction () as [48]:
(2)
In addition, the coefficient of variation (CV) of daily averaged CF was calculated to provide a comparable understanding of the variability in rooftop PV generations [50]:
(4)
where and denote the standard deviation and mean of CF, respectively.
We calculate CO2 equivalent emission reductions () based on the approach provided by the Intergovernmental Panel on Climate Change (IPCC) [51]:
(5)
where represents activity data, equaling to the amount of PV electricity consumption () in this study; means emission factor that is associated with each unit of electricity supplied by a grid (tCO2e MWh-1); and and denote curtailment rate and penetration rate, respectively.
The hourly loads throughout the year can be calculated as [52]:
(6)
where denotes the load at hour , the maximum load, and the minimum load, whose values vary depending on whether the day belong to weekdays (blue line in Figure 5b) or weekends (brown line in Figure 5b). denotes the load at hour of day , the peak load of day (red line in Figure 5c), and the base load of day (green line in Figure 5c).”
Comment 8): The comments in the attached PDF manuscript documents should also be addressed.
Response: The comments in the attached PDF manuscript documents are addressed.
Comment 9): All the figures should be provided in high quality. In some of the figures, the text is not readable.
Response: We have updated the high-quality figures to ensure that the texts in the figures are readable.
Comment 10): It is highly recommended to improve the write up of the paper.
Response: We have improved the writing of the paper with the efforts of all co-authors and the help of other experts.
The followings are several minor points to be considered.
Comment 11): “Global installed” should be changed to “Globally Installed” (Page.1 (Line 28)).
Response: Thank you for your careful checks. We have changed as “Globally installed PV capacity has grown more than eightfold in the last 10 years, providing about 3.6% of the world' total electricity consumption in 2021 [3]”
Comment 12): Space should be added before the word “to” (Page.3 (Line 106)).
Response: Thank you for your careful checks. We have added the space as “Second, remote sensing data on total solar radiation, diffuse fraction, and air temperature, as well as PV system parameters are fed into the Global Solar Energy Estimator (GSEE) [39] to simulate PV system's electricity generation efficiency”
Author Response File: 
Author Response.pdf
Reviewer 2 Report
The manuscript entitled "Assessing carbon reduction potential of rooftop PV in China using multi-source remote sensing data" presents a study of the PV impact using multi-source remote sensing data to fill the gap in assessing carbon reduction potential. The paper is well-written, with a great background, and clear declares the study gap. I have two main concerns about this paper:
1. It looks like is not a multi-source remote sensing data study but a modeling study.
2. It is not clear if the empirical equations were previously validated for the study.
Author Response
Response to Reviewer 2:
Dear Reviewer:
Thank you for your comments concerning our manuscript entitled “Assessing carbon reduction potential of rooftop PV in China using multi-source remote sensing data” (ID: sustainability-2181044). Those comments are all valuable and very helpful for improving our paper. We have studied comments carefully and made correction. Our draft is revised in Revised Mode of Microsoft Word to ensure the revised portion obvious. Our responses are shown in “Blue” color and the changes in the manuscript are shown in “Red” color.
Comments: The manuscript entitled "Assessing carbon reduction potential of rooftop PV in China using multi-source remote sensing data" presents a study of the PV impact using multi-source remote sensing data to fill the gap in assessing carbon reduction potential. The paper is well-written, with a great background, and clear declares the study gap. I have two main concerns about this paper:
Response: Thank you for your encouraging comments.
Comment 1): It looks like is not a multi-source remote sensing data study but a modeling study.
Response: We agree with you that the study can be considered as a modeling study. The modeling involves the simulation of PV generation and electricity dispatch. Since most of the inputs to these modellings are spatio-temporally continuous remote sensing data, our paper is entitled " Assessing carbon reduction potential of rooftop PV in China using multi-source remote sensing data". Taking into account your suggestion, we think it is better to change the title to "Assessing carbon reduction potential of rooftop PV in China through remote sensing data-driven simulations". The change has made in the revised manuscript.
Comment 2): It is not clear if the empirical equations were previously validated for the study.
Response: In this paper, we use two empirical formulas, i.e., Equations 1 and 2.
The relationship of Equation 1 is widely observed and validated in this study. In this revision, we have made this point clear as “We count the settlement area and rooftop area of each town in Jiangsu Province and find that these two areas have a significant linear correlation with a coefficient of determination of 0.9247 at the 95% confidence level (Figure 3a). Such correlation is also observed at the global scale [27]. We further validate this correlation at the county level and observe a coefficient of determination of 0.9495 at the 95% confidence level (Figure 3b), implying the stability of the relationship across different scales.”
The equation 2 have been validated in our previous study. We have made this point clear in the revised manuscript as “We assume that all rooftop PV systems are south-facing and tilted at an optimal angle () that varies with the latitude () and diffuse fraction () as [48]:
(2)
This empirical relationship was validated at 98 radiation stations in China [48].
- Liu, Y.; Yao, L.; Jiang, H.; Lu, N.; Qin, J.; Liu, T.; Zhou, C. Spatial estimation of the optimum pv tilt angles in china by incorporating ground with satellite data. Renewable Energy 2022, 189, 1249-1258.
Author Response File: Author Response.pdf
Reviewer 3 Report
The manuscript deals with simulations of PV roof-top systems' potential in China. The estimation of energy production was computed with the use of climate data and PV parameters. Together with energy production CO2 equivalent emission reduction was also obtained.
In my opinion, the subject is interesting and the paper is well-written and organized. Some minor errors should be corrected:
l. 136, 142, 149 (others) - a space between the value and the unit should be added;
Captions of Figures 3 and 4 should be on the same page as a figure.
Author Response
Response to Reviewer 3:
Dear Reviewer:
Thank you for your comments concerning our manuscript entitled “Assessing carbon reduction potential of rooftop PV in China using multi-source remote sensing data” (ID: sustainability-2181044). Those comments are all valuable and very helpful for improving our paper. We have studied comments carefully and made correction. Our draft is revised in Revised Mode of Microsoft Word to ensure the revised portion obvious. Our responses are shown in “Blue” color and the changes in the manuscript are shown in “Red” color.
Comments: The manuscript deals with simulations of PV roof-top systems' potential in China. The estimation of energy production was computed with the use of climate data and PV parameters. Together with energy production CO2 equivalent emission reduction was also obtained. In my opinion, the subject is interesting and the paper is well-written and organized.
Response: Thank you for your encouraging comments.
Comment 1): Some minor errors should be corrected: 136, 142, 149 (others) - a space between the value and the unit should be added;
Response: We have done a thorough check of the manuscript and inserted a space between the value and the unit.
Comment 2): Captions of Figures 3 and 4 should be on the same page as a figure.
Response: We have checked the manuscript to ensure that the captions all figures (including Figures 3 and 4) are on the same page as a figure.
Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
This paper can be accepted in the current form.
