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Achieving grid parity of wind power in China – Present levelized cost of electricity and future evolution
- Tu, Qiang, Betz, Regina, Mo, Jianlei, Fan, Ying, Liu, Yu
- Applied energy 2019 v.250 pp. 1053-1064
- carbon dioxide, carbon markets, coal, data collection, discount rate, electricity, issues and policy, prices, wind, wind power, China
- China has adopted an ambitious plan for wind power to achieve grid parity with the on-grid price of coal-fired power in 2020. Whether this target can be achieved is a great concern for policy makers as well as potential investors. To address this issue, we first estimate the future levelized cost of electricity (LCOE) of wind power using a learning curve method, and then determine whether grid parity can be achieved by comparing it with the on-grid price of coal-fired power. Specially, the effect of carbon pricing on the grid-parity is explored, and a sensitivity analysis on how the discount rates, learning rates, and curtailment rates affect grid parity is conducted. The learning rate of onshore wind power is estimated using a panel dataset consisting of information of 2059 onshore wind projects in China from 2006 to 2015. Based on this learning rate, the future LCOE of Chinese onshore wind power from 2016 to 2025 is calculated. The results show that the LCOE of onshore wind power decreases by 13.91% from 0.40 RMB/kWh in 2016 to 0.34 RMB/kWh in 2025. By comparing the LCOE with the on-grid price of coal-fired power, the grid parity of onshore wind power may be achieved in 2019. With the implementation of the carbon pricing policy, the grid parity will be achieved earlier. More specifically, with the carbon price reaching 10, 35, and 60 RMB/t CO2, the grid parity can be achieved in 2019, 2017, and 2016, respectively. The results of the sensitivity analysis show that in the case of high discount rates, low learning rates, high curtailment rates, high O&M cost and low capacity factor, the grid parity will be delayed, and a high carbon price will be required to achieve the grid parity.