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1.6 Million transactions replicate distributed PV market slowdown by COVID-19 lockdown

Author

Listed:
  • Zhang, Haoran
  • Yan, Jinyue
  • Yu, Qing
  • Obersteiner, Michael
  • Li, Wenjing
  • Chen, Jinyu
  • Zhang, Qiong
  • Jiang, Mingkun
  • Wallin, Fredrik
  • Song, Xuan
  • Wu, Jiang
  • Wang, Xin
  • Shibasaki, Ryosuke

Abstract

Solar PV has seen a spectacular market development in recent years and has become a cost competitive source of electricity in many parts of the world. Yet, prospective observations show that the coronavirus pandemic could impact renewable energy projects, especially in the distributed market. Tracking and attributing the economic footprint of COVID-19 lockdowns in the photovoltaic sector poses a significant research challenge. Based on millions of financial transaction records and 44 thousand photovoltaic installation records, we tracked the spatio-temporal sale network of the distributed photovoltaic market and explored the extent of market slowdown. We found that a two-month lockdown duration can be assessed as a high-risk threshold value. When the lockdown duration exceeds the threshold value, the monthly value-added loss reaches 67.7%, and emission reduction capacity is cut by 64.2% over the whole year. We show that risks of a slowdown in PV deployment due to COVID-19 lockdowns can be mitigated by comprehensive incentive strategies for the distributed PV market amid market uncertainties.

Suggested Citation

  • Zhang, Haoran & Yan, Jinyue & Yu, Qing & Obersteiner, Michael & Li, Wenjing & Chen, Jinyu & Zhang, Qiong & Jiang, Mingkun & Wallin, Fredrik & Song, Xuan & Wu, Jiang & Wang, Xin & Shibasaki, Ryosuke, 2021. "1.6 Million transactions replicate distributed PV market slowdown by COVID-19 lockdown," Applied Energy, Elsevier, vol. 283(C).
    • Handle: RePEc:eee:appene:v:283:y:2021:i:c:s0306261920317232
    DOI: 10.1016/j.apenergy.2020.116341
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    3. Muhammad Waqas Rana & Sufang Zhang & Shahid Ali & Iqra Hamid, 2022. "Investigating Green Financing Factors to Entice Private Sector Investment in Renewables via Digital Media: Energy Efficiency and Sustainable Development in the Post-COVID-19 Era," Sustainability, MDPI, vol. 14(20), pages 1-19, October.
    4. Tian, Jinfang & Yu, Longguang & Xue, Rui & Zhuang, Shan & Shan, Yuli, 2022. "Global low-carbon energy transition in the post-COVID-19 era," Applied Energy, Elsevier, vol. 307(C).
    5. Şahin, Utkucan & Ballı, Serkan & Chen, Yan, 2021. "Forecasting seasonal electricity generation in European countries under Covid-19-induced lockdown using fractional grey prediction models and machine learning methods," Applied Energy, Elsevier, vol. 302(C).
    6. Liu, Zhengguang & Guo, Zhiling & Song, Chenchen & Du, Ying & Chen, Qi & Chen, Yuntian & Zhang, Haoran, 2023. "Business model comparison of slum-based PV to realize low-cost and flexible power generation in city-level," Applied Energy, Elsevier, vol. 344(C).
    7. Lazo, Joaquín & Aguirre, Gerson & Watts, David, 2022. "An impact study of COVID-19 on the electricity sector: A comprehensive literature review and Ibero-American survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    8. Zhao, Wei & Zhang, Haoran & Zheng, Jianqin & Dai, Yuanhao & Huang, Liqiao & Shang, Wenlong & Liang, Yongtu, 2021. "A point prediction method based automatic machine learning for day-ahead power output of multi-region photovoltaic plants," Energy, Elsevier, vol. 223(C).
    9. Song, Zhe & Liu, Jia & Yang, Hongxing, 2021. "Air pollution and soiling implications for solar photovoltaic power generation: A comprehensive review," Applied Energy, Elsevier, vol. 298(C).

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