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How to Develop Renewable Power in China? A Cost-Effective Perspective

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  • Cong, Rong-Gang
  • Shen, Shaochuan

Abstract

To address the problems of climate change and energy security, Chinese government strived to develop renewable power as an important alternative of conventional electricity. In this paper, the learning curve model is employed to describe the decreasing unit investment cost due to accumulated installed capacity; the technology diffusion model is used to analyze the potential of renewable power. Combined with the investment cost, the technology potential, and scenario analysis of China social development in the future, we develop the Renewable Power Optimization Model (RPOM) to analyze the optimal development paths of three sources of renewable power from 2009 to 2020 in a cost-effective way. Results show that (1) the optimal accumulated installed capacities of wind power, solar power, and biomass power will reach 169000, 20000, and 30000 MW in 2020; (2) the developments of renewable power show the intermittent feature; (3) the unit investment costs of wind power, solar power, and biomass power will be 4500, 11500, and 5700 Yuan/KW in 2020; (4) the discounting effect dominates the learning curve effect for solar and biomass powers; (5) the rise of on-grid ratio of renewable power will first promote the development of wind power and then solar power and biomass power.

Suggested Citation

  • Cong, Rong-Gang & Shen, Shaochuan, 2014. "How to Develop Renewable Power in China? A Cost-Effective Perspective," MPRA Paper 112209, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:112209
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    References listed on IDEAS

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    3. Yi-Ming Wei & Jin-Wei Wang & Tianqi Chen & Bi-Ying Yu & Hua Liao, 2018. "Frontiers of Low-Carbon Technologies: Results from Bibliographic Coupling with Sliding Window," CEEP-BIT Working Papers 116, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
    4. Pasakorn Sakolsatayatorn, 2019. "The Influence of Institutional Factors on Sustainable Renewable Energy Policy Development: New Evidence from the Thai Experience," International Journal of Global Sustainability, Macrothink Institute, vol. 3(1), pages 13-34, December.
    5. Ouyang, Xiaoling & Lin, Boqiang, 2015. "An analysis of the driving forces of energy-related carbon dioxide emissions in China’s industrial sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 838-849.
    6. Cong, Rong-Gang & Caro, Dario & Thomsen, Marianne, 2017. "Is it beneficial to use biogas in the Danish transport sector?–An environmental-economic analysis," MPRA Paper 112291, University Library of Munich, Germany.
    7. Zhi-Fu Mi & Su-Yan Pan & Hao Yu & Yi-Ming Wei, 2014. "Potential impacts of industrial structure on energy consumption and CO2 emission: a case study of Beijing," CEEP-BIT Working Papers 51, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
    8. Yu, Hao & Wei, Yi-Ming & Tang, Bao-Jun & Mi, Zhifu & Pan, Su-Yan, 2016. "Assessment on the research trend of low-carbon energy technology investment: A bibliometric analysis," Applied Energy, Elsevier, vol. 184(C), pages 960-970.
    9. Zhaohua Wang & Xiaoyang Dong, 2016. "Determinants and policy implications of residents’ new energy vehicle purchases: the evidence from China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 82(1), pages 155-173, May.
    10. Wei, Yi-Ming & Kang, Jia-Ning & Yu, Bi-Ying & Liao, Hua & Du, Yun-Fei, 2017. "A dynamic forward-citation full path model for technology monitoring: An empirical study from shale gas industry," Applied Energy, Elsevier, vol. 205(C), pages 769-780.
    11. Zhang, Kun & Wang, Qian & Liang, Qiao-Mei & Chen, Hao, 2016. "A bibliometric analysis of research on carbon tax from 1989 to 2014," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 297-310.
    12. Aslan, Alper, 2016. "The causal relationship between biomass energy use and economic growth in the United States," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 362-366.
    13. Thomassen, Gwenny & Van Passel, Steven & Dewulf, Jo, 2020. "A review on learning effects in prospective technology assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
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    More about this item

    Keywords

    Renewable energy; Sustainable development; Cost-efficient; Carbon-emission;
    All these keywords.

    JEL classification:

    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

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