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In the context of carbon neutrality, renewable energy, especially wind power, solar PV and hydropower, will become the most important power sources in the future low-carbon power system. Since wind power and solar PV are specifically intermittent and space-heterogeneity, an assessment of renewable energy potential considering the variability of wind power and solar PV with high temporal resolution in different regions will facilitate more accurate identification of the decarbonization pathway of power system. In this paper, the complementary output potential of wind-solar-hydro power every 15 min in 31 Chinese provinces is evaluated by developing a multi-objective optimization model based on Nondominated Sorting Genetic Algorithm II. The results show that the total annual complementary power generation potential in China is 17.57 PWh, of which the three components account for 47.8%, 25.3% and 26.9%, respectively. Inner Mongolia and Tibet are the major renewable energy source provinces with annual complementary power generation potential of 5330 TWh and 3907 TWh, accounting for 30.3% and 13.7% of the total power generation potential, respectively. The temporal potential of wind-solar-hydro power varies greatly, with daily potential is more volatile than monthly. Seasonal and spatial heterogeneity of the complemental renewable potential makes some provinces suffer power shortage during long-hours period especially in winter. Our findings will encourage a higher penetration of renewable energy, the promotion of multi-energy complementarity, and the development of inter-provincial power transmission and energy storage infrastructure in China's future power sector. • A multi-objective wind-solar-hydro complementary optimization model is developed. • Electricity supply and demand have gaps at different temporal and spatial scales. • Wind-solar-hydro complementary potential shows great temporal and spatial variation. • Renewable complementarity can improve China's future power system stability. [ABSTRACT FROM AUTHOR]

As a renewable energy, wind power has been improving energy structures and assisting in the maintenance of strategic energy security. Based on provincial level panel data from 2000 to 2018, this paper developed models to evaluate the effects of different policies on newly installed wind power capacity and investigated the market reform influences on wind development in eight spot market pilots. It was found that on a national level, while the pricing and government guideline policies significantly promoted wind power growth, present market reforms were having an opposite effect. The results also suggested that from a resource endowment heterogeneity perspective, there was a significant difference in the wind capacity promotion policy performances. Additionally, of the eight spot market pilots, market designs had led to a significant reduction in new wind power installations in Gansu and Inner Mongolia, and no significant wind power development effects were found in the other pilots. Based on the research results, a discussion on policy use and suggestions on policy improvements are given. • Effectiveness of different representative wind power policies are examined. • The panel models are developed using data that covers 30 provinces over 2000–2018. • Policy effectiveness varies by policy types and wind resource levels. • The current market designs show little impact on wind power development. • Implications on policy use and improvement suggestions are given. [ABSTRACT FROM AUTHOR]

A kernel density estimation (KDE) model for the probability distribution of wind speed (PDWS) is proposed in this paper for application to wind energy assessment (WEA) in China. Four bandwidth selectors, including normal scale (NS), plug in, biased cross-validation, and least-square cross validation, are proposed for the KDE model. Popular parametric distribution models are also introduced for comparisons with the KDE models. Based on five-year day-average wind speed data from 698 nationwide wind stations in China, the performance and robustness of both parametric and KDE models were evaluated comprehensively on the regional scale. Wind power density (WPD) and wind turbine power output (WTPO), which are the priorities in WEA, are subsequently calculated based on the estimated PDWS models. The ranking results of four individual metrics and one comprehensive metric indicate that the four KDE models outperform the parametric models in fitting the PDWS. The KDE-NS model performs the best compared to the other three KDE models. In addition to the KDE models, the generalized gamma and generalized extreme values were considered as better parametric models in fitting the PDWS. KDE models also performed well in WPD estimation, especially the KDE-NS model with a mean absolute percentage error (MAPE) value as low as 2%. Some parametric models, i.e., Johnson SB and Wakeby, which are not outstanding in PDWS fitting, however perform well in WPD estimation, and their MAPE values can be controlled to remain within 3%. This indicates that the result of the PDWS is not completely equivalent to that of WPD estimation. The WPD and WTPO in most of China's interior areas are less than 40 W / m 2 and 1.2 GWh, respectively. In the eastern coastal areas, middle and eastern Inner Mongolia, and some western provinces, the WPD and WTPO are relatively higher, and can reach or exceed 240 W / m 2 and 3.5 GWh, respectively. • Four KDE models with various bandwidth selectors are proposed for fitting PDWS. • Popular parametric models are introduced for comparisons with the KDE models. • Performance of parametric and KDE models are evaluated on the regional scale. • KDE models generally outperform the parametric models in fitting PDWS. • KDE models also performs well in WEA analysis, especially for the KDE-NS. [ABSTRACT FROM AUTHOR]