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City-level analysis of subsidy-free solar photovoltaic electricity price, profits and grid parity in China

Nature Energy volume 4, pages 709–717 (2019)Cite this article

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Abstract

In recent years, China has become not just a large producer but a major market for solar photovoltaics (PV), increasing interest in solar electricity prices in China. The cost of solar PV electricity generation is affected by many local factors, making it a challenge to understand whether China has reached the threshold at which a grid-connected solar PV system supplies electricity to the end user at the same price as grid-supplied power or the price of desulfurized coal electricity, or even lower. Here, we analyse the net costs and net profits associated with building and operating a distributed solar PV project over its lifetime, taking into consideration total project investments, electricity outputs and trading prices in 344 prefecture-level Chinese cities. We reveal that all of these cities can achieve—without subsidies—solar PV electricity prices lower than grid-supplied prices, and around 22% of the cities’ solar generation electricity prices can compete with desulfurized coal benchmark electricity prices.

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Fig. 1: Historical LCOE of solar PV power generation in China.
Fig. 2: Grid parity indices for distributed PV projects in 344 prefecture-level cities without subsidies.
Fig. 3: Economic feasibility and profitability of solar PV in 344 prefecture-level cities without subsidies.
Fig. 4: Comparison between GPIu and GPIp values.

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Data availability

Data on electricity market prices and desulfurized coal benchmark prices were compiled from the National Development and Reform Commission, and the price bureaus of different provinces. The historical module costs in Fig. 1 are available from the National Survey Report of PV Power Applications in China (2011–2013 and 2015–2017)(http://iea-pvps.org/index.php?id=93&tx_damfrontend_pi1=&tx_damfrontend_pi1%5BcatPlus%5D=&tx_damfrontend_pi1%5BcatEquals%5D=&tx_damfrontend_pi1%5BcatMinus%5D=57&tx_damfrontend_pi1%5BcatPlus_Rec%5D=&tx_damfrontend_pi1%5BcatMinus_Rec%5D=&tx_damfrontend_pi1%5BtreeID%5D=201&tx_damfrontend_pi1%5Bid%5D=93), Photovoltaic Power Systems Programme Annual Report (2000–2018) (http://www.iea-pvps.org/index.php?id=6) and NDRC Energy Research Institute of China (www.eri.org.cn). The historical lending interest rate data are available from The World Bank (https://data.worldbank.org/indicator/FR.INR.LEND?locations=CN&view=chart). The calculation results are provided in Supplementary Table 3. Investment, operation and maintenance costs data were compiled from the China Photovoltaic Industry Association (China PV industry development roadmap; http://www.chinapv.org.cn/road_map.html), National Survey Report of PV Power Applications in China (2011–2013 and 2015–2017) and Photovoltaic Power Systems Programme Annual Report (2000–2018). Policies data are available from the Law Information Database of Peking University (http://www.pkulaw.cn/). The climatic data were retrieved from the Meteonorm global database (http://www.meteonorm.com). The other data that support the plots within this paper and other findings of this study are available from the corresponding authors on request.

Code availability

Solar power production analysis and sensitivity analysis were conducted using MATLAB. The codes are available on request from the corresponding authors. The solar power production analysis code can also be accessed via the open-source package OptiCE written in MATLAB language at http://optice.net/team%20and%20projects.html.

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Acknowledgements

The authors acknowledge the support from the Swedish Knowledge Foundation (KK-stiftelsen) Future Energy Profile through the projects iREST and FREE, and the National Key Research and Development Program of China (grant number 2016YFE0102400). The authors acknowledge S. Wang (Beijing Xianjian Energy Consulting) for initial discussion. The authors acknowledge Dr. H. Zhan from Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, for the figures formatting. Y.Y. acknowledges financial support from the China Scholarship Council.

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Authors and Affiliations

  1. Department of Chemical Engineering, KTH Royal Institute of Technology, Stockholm, Sweden

    Jinyue Yan & Pietro Elia Campana

  2. School of Business, Society and Engineering, Mälardalen University, Västerås, Sweden

    Jinyue Yan, Ying Yang & Pietro Elia Campana

  3. School of Social Sciences, Tsinghua University, Beijing, China

    Jijiang He

  4. Energy Internet Research Institute, Tsinghua University, Beijing, China

    Jijiang He

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Contributions

J.Y. designed the study with the assistance from J.H. All authors were involved in data collection. P.E.C. and Y.Y. developed the electricity production and prices dataset. Y.Y. performed the solar PV grid parity calculations, mapping work and analysis. J.Y. and Y.Y. formulated the structure of the manuscript. Y.Y. wrote the manuscript with support from J.Y., P.E.C. and J.H. J.Y., P.E.C. and Y.Y. revised the manuscript.

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Correspondence to Jinyue Yan or Ying Yang.

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Supplementary Notes 1–5, Supplementary Figs. 1–4, Supplementary Tables 1–5 and Supplementary refs. 1–33.

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Yan, J., Yang, Y., Elia Campana, P. et al. City-level analysis of subsidy-free solar photovoltaic electricity price, profits and grid parity in China. Nat Energy 4, 709–717 (2019). https://doi.org/10.1038/s41560-019-0441-z

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