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Assessing Carbon Reduction Potential of Rooftop PV in China through Remote Sensing Data-Driven Simulations

Author

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  • Hou Jiang

    (State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China)

  • Ning Lu

    (State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China)

  • Xuecheng Wang

    (School of Geography and Planning, Nanning Normal University, Nanning 530001, China)

Abstract

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.

Suggested Citation

  • Hou Jiang & Ning Lu & Xuecheng Wang, 2023. "Assessing Carbon Reduction Potential of Rooftop PV in China through Remote Sensing Data-Driven Simulations," Sustainability, MDPI, vol. 15(4), pages 1-16, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3380-:d:1066405
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    References listed on IDEAS

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