• Energy Research

Developing rooftop photovoltaic (PV) has become an important initiative for achieving carbon neutrality in China, but the carbon reduction potential assessment has not properly considered the spatial and temporal variability of PV generation and the curtailment in electricity dispatch. In this study, we propose a technical framework to fill the gap in assessing carbon reduction potential through remote sensing data-driven simulations. The spatio-temporal variations in rooftop PV generations were simulated on an hourly basis, and a dispatch analysis was then performed in combination with hourly load profiles to quantify the PV curtailment in different scenarios. Our results showed that the total rooftop PV potential in China reached 6.5 PWh yr−1, mainly concentrated in the eastern region where PV generation showed high variability. The carbon reduction from 100% flexible grids with 12 h of storage capacity is close to the theoretical maximum, while without storage, the potential may be halved. To maximize the carbon reduction potential, rooftop PV development should consider grid characteristics and regional differences. This study has important implications for the development of rooftop PV and the design of carbon-neutral pathways based on it.

Surface solar radiation drives the water cycle and energy exchange on the earth's surface, and its diffuse component can promote carbon uptake in ecosystems by increasing the plant productivity. The accurate knowledge of their spatial distribution is of great importance to many studies and applications, such as the estimation of agricultural yield, carbon dynamics of terrestrial systems, site selection of solar power plants, as well as trends of regional climate changes. Therefore, we produce the hourly surface radiation datasets based on the hourly Multi-functional Transport Satellite (MTSAT) satellite imagery and the ground observations from the China Meteorology Administration (CMA) through deep learning techniques. The deep network is trained using training samples in 2008, and then utilized to generate the hourly radiation for other years. This dataset provides the gridded surface global and diffuse solar radiation in 2018 within 71.025°E - 141.025°E and 14.975°N - 59.975°N with an increment of 0.05°. Both the direct predicted hourly values and the integrated daily and monthly total values are available. The dataset should be useful for the analysis of the regional differences and temporal cycles of solar radiation in fine scales, and the impact of diffuse radiation on plant growth etc.

Surface solar radiation drives the water cycle and energy exchange on the earth's surface, and its diffuse component can promote carbon uptake in ecosystems by increasing the plant productivity. The accurate knowledge of their spatial distribution is of great importance to many studies and applications, such as the estimation of agricultural yield, carbon dynamics of terrestrial systems, site selection of solar power plants, as well as trends of regional climate changes. Therefore, we produce the hourly surface radiation datasets based on the hourly Multi-functional Transport Satellite (MTSAT) satellite imagery and the ground observations from the China Meteorology Administration (CMA) through deep learning techniques. The deep network is trained using training samples in 2008, and then utilized to generate the hourly radiation for other years. This dataset provides the gridded surface global and diffuse solar radiation in 2012 within 71.025°E - 141.025°E and 14.975°N - 59.975°N with an increment of 0.05°. Both the direct predicted hourly values and the integrated daily and monthly total values are available. The dataset should be useful for the analysis of the regional differences and temporal cycles of solar radiation in fine scales, and the impact of diffuse radiation on plant growth etc.

Surface solar radiation drives the water cycle and energy exchange on the earth's surface, and its diffuse component can promote carbon uptake in ecosystems by increasing the plant productivity. The accurate knowledge of their spatial distribution is of great importance to many studies and applications, such as the estimation of agricultural yield, carbon dynamics of terrestrial systems, site selection of solar power plants, as well as trends of regional climate changes. Therefore, we produce the hourly surface radiation datasets based on the hourly Multi-functional Transport Satellite (MTSAT) satellite imagery and the ground observations from the China Meteorology Administration (CMA) through deep learning techniques. The deep network is trained using training samples in 2008, and then utilized to generate the hourly radiation for other years. This dataset provides the gridded surface global and diffuse solar radiation in 2009 within 71.025°E - 141.025°E and 14.975°N - 59.975°N with an increment of 0.05°. Both the direct predicted hourly values and the integrated daily and monthly total values are available. The dataset should be useful for the analysis of the regional differences and temporal cycles of solar radiation in fine scales, and the impact of diffuse radiation on plant growth etc.

Surface solar radiation drives the water cycle and energy exchange on the earth's surface, and its diffuse component can promote carbon uptake in ecosystems by increasing the plant productivity. The accurate knowledge of their spatial distribution is of great importance to many studies and applications, such as the estimation of agricultural yield, carbon dynamics of terrestrial systems, site selection of solar power plants, as well as trends of regional climate changes. Therefore, we produce the hourly surface radiation datasets based on the hourly Multi-functional Transport Satellite (MTSAT) satellite imagery and the ground observations from the China Meteorology Administration (CMA) through deep learning techniques. The deep network is trained using training samples in 2008, and then utilized to generate the hourly radiation for other years. This dataset provides the gridded surface global and diffuse solar radiation in 2016 within 71.025°E - 141.025°E and 14.975°N - 59.975°N with an increment of 0.05°. Both the direct predicted hourly values and the integrated daily and monthly total values are available. The dataset should be useful for the analysis of the regional differences and temporal cycles of solar radiation in fine scales, and the impact of diffuse radiation on plant growth etc.

Surface solar radiation drives the water cycle and energy exchange on the earth's surface, and its diffuse component can promote carbon uptake in ecosystems by increasing the plant productivity. The accurate knowledge of their spatial distribution is of great importance to many studies and applications, such as the estimation of agricultural yield, carbon dynamics of terrestrial systems, site selection of solar power plants, as well as trends of regional climate changes. Therefore, we produce the hourly surface radiation datasets based on the hourly Multi-functional Transport Satellite (MTSAT) satellite imagery and the ground observations from the China Meteorology Administration (CMA) through deep learning techniques. This dataset provides the gridded surface global and diffuse solar radiation within 71.025°E - 141.025°E and 14.975°N - 59.975°N with an increment of 0.05°, covering the period from 2007 to present. Both the direct predicted hourly values and the integrated daily and monthly total values are available. The verification experiment in 2007 reveals that the root mean square error (RMSE) between our predictions and in-situ measurements is about 0.32 MJ/m^2^ (90 W/m^2^), 2.14 MJ/m^2^ and 1.30 MJ/m^2^ in hourly, daily and monthly scales, respectively. The dataset should be useful for the analysis of the regional differences and temporal cycles of solar radiation in fine scales, and the impact of diffuse radiation on plant growth etc.

AbstractQuantifying future changes in solar photovoltaic (PV) potential is of great significance for energy planning and policy making to achieve carbon neutrality. To constrain the uncertainties of applying directly Coupled Model Intercomparison Project Phase 6 (CMIP6) projections to such quantification, we construct the performance‐weighted CMIP6 solar irradiance and air temperature data, and then assess the future changes in China's PV potential and accompanying economic benefits under three typical climate change scenarios. We find that the high emission scenario will substantially damage China's PV potential, resulting in a reduction of 314 TWh/year in electricity generation by the planned installed capacity in 2100, with a corresponding loss of US $25 billion/year. However, the low emission scenario will enhance the PV potential, yielding an additional electricity generation of 226 TWh/year and a corresponding bonus of US $18 billion/year in 2100. China's commitment to carbon neutrality will lead to low‐carbon emissions, which brings great economic rewards to its PV industry.