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In the context of carbon neutrality target, renewable energy sources have been transforming from "supplementary energy" to "main energy", which have promoted the green and low-carbon transition of global energy supply system. In-depth analyzing the spatial patterns and driving mechanisms of renewable energy expansion are of significance for optimizing the spatial layout of clean power, and avoiding the phenomenon of wind and solar power curtailment. In this paper, we proposed an ensemble learning model to examine the nonlinear effects of physical geography, resource endowment, and socio-economic factors on solar photovoltaic (PV) capacity at the prefecture-level city scale in China. Using the city-level multi-sources geospatial big data, we extensively collected a total of 175 related explanatory variables and cumulative installed capacity of solar PV power for 295 prefecture-level cities of China. The recursive feature elimination algorithm (SVM-REF) is firstly used to extract the optimal feature subset of urban PV capacity from multi-dimensional features variables. Furthermore, three advanced machine learning models (random forest, decision tree, extreme gradient boosting) are developed to identify the key influencing factors and nonlinear driving effect of urban solar PV power expansion in China. The results show that China's PV installation capacity is highly concentrated in Northern and Northwest parts of China, with the occupancy over 70% in 2019. Moreover, the XGBoost model has the best prediction accuracy (R2 = 0.97) among three methods. We also found that total amount of urban water resources, average solar radiation, and population density are the most important controlling factors for urban solar PV capacity expansion in China, with contribution of 35.6%, 17.7%, and 13.3%, respectively. We suggested that urban solar PV layout mode in China is recommended to gradually shift from resource orientation to the "resource-environment-demand" comprehensive orientation. The paper provides a replicable, scalable machine learning models for simulating solar PV power capacity at the prefecture-level city scale, and serves as a motivation for decision-making reference of the macro siting optimization and sustainable development of China's green power industry. • This study examines the impact of high-dimensional features variables on urban solar energy development. • Over 70% of solar PV installation capacity is concentrated in Northern and Northwest parts of China. • Water resources, solar radiation, population density have significant impact on solar PV distribution. • Solar PV distribution in China should shift from resource orientation to the comprehensive orientation. [ABSTRACT FROM AUTHOR]

China has set a goal of carbon neutrality by 2060 and is determined to increase wind and solar power to 1200 GW by 2030, even though it already has the world's largest installed capacity of renewables. However, the lack of flexibility in the power system will limit the development of a higher proportion of renewable energy. This paper evaluates the applicability of different types of flexible power sources in China for the first time. Above all, it describes the current situation of China's power industry and the necessity of increasing flexible power sources, establishes an evaluation system from the perspectives of benefit, opportunity, cost & risk, then determines 19 evaluation criteria. The BOCR-ANP-TOPSIS method was used to evaluate the system, where the ANP method determines the weights and the TOPSIS method ranks the alternatives. The result shows that pumped storage hydropower has the highest overall ranking, followed by the flexibility transformation of coal-fired power. Coupled with sensitivity and comparative analysis, valuable findings that will help future energy policymakers are reported. • Evaluating the applicability of different flexible power sources in China for the first time. • The identification of criteria for the selection of flexible power sources was given. • Making recommendations for China's flexible power source development policy. • The BOCR-ANP-TOPSIS method adopted can be extended to select flexible power sources in different regions meticulously. [ABSTRACT FROM AUTHOR]

To achieve the carbon-neutral goal set for 2060, China has promoted renewable energy development for more than two decades. Meanwhile, China's electricity market is undergoing a new round of reform by introducing more market-oriented mechanisms and changing the market structure. This paper investigates the impact of three electric utility market structures experimented with in China on promoting renewable energy through a simulation model. The three market structures are vertical monopoly, weak monopoly (separation of power generators and grids), and monopolistic competition (Cournot-Nash competition). The model is calibrated with the actual data of the regional utility network in Beijing, Tianjin, and Tangshan areas. The simulation results show that the electricity market structure plays a vital role in renewable electricity, and the competition among utilities tends to benefit renewable energy development (wind power here). The study's conclusions are helpful to our understanding of further reforms in the electric utility industry and the long-term clean energy target in China. [ABSTRACT FROM AUTHOR]

Intermittent renewable energy sources have been transformed from supplementary energy to dominant incremental alternative energy sources in the context of carbon neutrality target worldwide. For coastal countries, it is urgent to integrate abundant offshore energy into regional onshore renewable power sources economically and reliably considering the scarcity of land resources and negative environmental impacts. However, limited knowledge on the complex spatiotemporally synergy relationships between energy potential (onshore and offshore) is a barrier in making reasonable policies to promote collaborative development of onshore and offshore energies. This study presents a spatiotemporally explicit modelling framework for assessing the synergy potential characteristics of onshore and offshore renewable energy sources. The framework firstly estimates the technical potential of solar PV and wind energy across the country by using 40 years of hourly meteorological reanalysis data (1980–2020), and then investigates the long-term changes trend, stability, and complementarity of solar-wind energy potential spatiotemporally at both onshore and offshore sub-regions. We find that a considerable amount of solar PV and wind energy sources could be harnessed in China, and onshore potential across the country (178 PWh/yr) is approximate 20 times higher than offshore energy potential (8.9 PWh/yr). Overall, onshore solar-wind energy potentials exhibit statistically insignificant but slightly increasing trend over the past 40 years, while offshore wind potentials observe an opposite change trend for solar PV and wind energy production. Complementarity assessment showed the northeast and offshore parts of China present high complementarity effects for hourly and daily power production of solar-wind systems. Furthermore, inter-regional combination between offshore and onshore northwest regions also exhibits promising synergy effect for renewable energy power output. The results on resources dynamic and complementarity highlight the advantage of interregional resources cooperative configuration and electric power grid interconnections. The analysis performed in this paper could provide a global picture for the integration planning and collaborative development strategies formulation of on-/offshore renewable energy across the country. • Synergy potential of onshore and offshore renewable energy was evaluated. • Trend, stability, and complementarity of solar and wind potential were examined. • Onshore energy potential in China is 20 times higher than offshore potential. • Synergy development strategies of onshore and offshore energy were proposed. [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]

The world has witnessed a surge in renewable power installed capacity in recent years, and there is an emerging trend of renewable penetration in electricity production. However, there is a lack of quantitative comparative study on disaggregated renewable power sources concerning their generation efficiency performance, regional heterogeneity, development potential, and influencing mechanism in the existing literature. In the case of China's 30 provinces, this paper evaluates the hydropower, solar power, and wind power generation efficiency by stochastic frontier analysis method, and then reveals the distribution characteristics and deployment potential of different renewable sources. Furthermore, from the perspective of spatial heterogeneity, geographical detector model is utilized to study the influence mechanism of the generation efficiency of different renewable sources. The main results are as follows. Firstly, production inefficiency prevails in hydropower, solar power, and wind power generation industries. The installed capacity, utilized hours, and auxiliary power consumption have positive impacts on the three renewable energy sources. Every 1% increase in auxiliary power consumption leads to 0.16% increase in solar power generation, which is quite larger than the increase in hydropower and wind power. Secondly, on average, hydropower has the highest level of generation efficiency, followed by wind power and solar power. Kernel density curves indicate the generation efficiency of hydropower, solar power, and wind power displays distinct aggregation characteristics. Different energy types show significant regional differences in deployment potential. Thirdly, annual precipitation accounts for 76% of the spatial heterogeneity in hydropower generation efficiency, followed by hydropower technology innovation and power structure. As for solar power generation efficiency, the most important influencing factors are electricity investment and economic development. By contrast, wind power generation efficiency is primarily affected by power structure, electricity investment, and urbanization. Additionally, there exist distinct synergistic effects among different variables. These results provide insightful policy support for the improvement of renewable power generation efficiency. The study can be extended to the global scale using country-level data. • Stochastic frontier analysis is performed to study renewable generation efficiency. • Production inefficiency prevails in three renewable power generation industries. • Hydropower generation efficiency is higher than wind power and solar power in China. • Different energy types show significant regional differences in deployment potential. • The driving factors of generation efficiency are examined by geographical detector. [ABSTRACT FROM AUTHOR]