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Abstract The recent recurrence of wind curtailment in China underscores the rigidity and vulnerability of China's electricity regulatory regime. Using the theory of "fragmented authoritarianism", this paper identifies key institutional barriers causing wind curtailment in China and develops a framework for analyzing China's electricity regulatory regime. Specifically, the fragmented institutional authorities and responsibilities lead to departmentalism and coordination problems, both vertically and horizontally. The resulting mismatches in the investment, generation, and distribution of the electric power sector reflect direct causes for China's wind curtailment. This paper recommends that an electric power system reform with established regional spot markets is needed to fix China's wind curtailment problem. Highlights • Complex reasons lie behind serious wind curtailment in China. • Wind curtailment has its roots in the electricity regulation regime of China. • Fragmented authority causes coordination problems in the power sector. • Mismatches permeates the investment, generation and distribution of power. • A regional power spot market is needed to fix the wind curtailment problem. [ABSTRACT FROM AUTHOR]

Domestic wind turbine manufacturing sector in China has experienced development stages starting from scratch to mass production. During the 11th FYP period (2006–2010), the main goal of wind power policy in China is to promote the commercialization of wind power by large-scale deployment of wind farms. This goal has been realized to a great extent and now the cost of wind power generation is nearly comparable to coal-fired power generation in China. The industry policy, which devotes to mass production of domestic wind turbines, is also largely successful. The purpose of the paper is to provide an overview on wind turbine manufacturing sector in China. The policy evolution in different stages, achievements and challenges pertinent to the sector are addressed in the paper. Key findings are that the misleading industry policy, which provides strong incentive to blind entrance and “competition for scale and price” and restrains innovation as well, is the key obstacle for the sustainable development of the sector. Deficient technology standard and qualification system and the misplaced franchise bidding system also indulge vicious competition and oversupply. Creating a level playground for all turbine supplies, providing strong incentive to innovative manufacturers, establishing thorough and practicable standard and qualification system, and fine-tuning the directive of the franchise bidding system towards technology and service are the primary policy implications proposed by our study. [ABSTRACT FROM AUTHOR]

In order to mitigate global climate change and air pollution, the Chinese government has assigned high priority to expanding low-carbon power generation in China. Recent studies have shown that wind power is one of the most promising renewable energy option in China. Although many studies have estimated the generation potential of onshore wind power, their results vary widely from 1783 TWh to 39,000 TWh. Therefore, we examine the different assumptions in these papers and identify three main factors influencing the results. The three influencing factors are: weather data set, land utilisation factor, and wind turbine configuration. For our model-based analysis, we define a reference scenario which is used to compare the results. Our analysis shows using a different weather data set increases the generation potential to roughly 35,000 TWh. This is 54% higher than the generation potential of the reference scenario. The land utilisation factor also has a large influence, ranging between −10% and −51%. The studies' assumptions and data should be subjected to careful scrutiny, as the calculated wind power potentials are widely used to develop decarbonisation strategies for the energy system. • Study results on wind power potentials in China vary from 1783 TWh to 39,000 TWh • Influencing factors: weather data set, land utilisation, and wind turbine configuration. • Model-based sensitivity analysis with nine scenario settings. • Chosen weather data set has the highest impact on the calculated results with a variation of 54%. [ABSTRACT FROM AUTHOR]