Overview of Wind Power in China: Status and Future

Yang, Jian‐Bo; Liu, Qunyi; Li, Xin; Cui, Xiandan

doi:10.3390/su9081454

Cited by 49 publications

(21 citation statements)

References 36 publications

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“…2) Southwest Highland: Highland area with medium precipitation, lack of wind. 3) Northeast China: Plenty of wind [28], no monsoon and lack of precipitation.…”

Section: A Results Of Community Detectionmentioning

confidence: 99%

Chinese cities’ air quality pattern and correlation

Zhang

Guan

et al. 2020

J. Stat. Mech.

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Air quality impacts people's health and daily life, affects the sensitive ecosystems, and even restrains a country's development. By collecting and processing the time series data of Air Quality Index (AQI) of 363 cities of China from Jan. 2015 to Mar. 2019, we dedicated to characterize the universal patterns, the clustering and correlation of air quality of different cities by using the methods of complex network and time series analysis. The main results are as follows: 1) The Air Quality Network of China (AQNC) is constructed by using the Planar Maximally Filtered Graph (PMFG) method. The geographical distances on the correlation of air quality of different cities have been studied, it is found that 100 km is a critical distance for strong correlation. 2) Seven communities of AQNC have been detected, and their patterns have been analyzed by taking into account the Hurst exponent and climate environment, it is shown that the seven communities are reasonable, and they are significantly influenced by the climate factors, such as monsoon, precipitation, geographical regions, etc. 3) The motifs of air quality time series of seven communities have been investigated by the visibility graph, for some communities, the evolutionary patterns of the motifs are a bit stable, and they have the long-term memory effects. While for others, there are no stable patterns.

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“…2) Southwest Highland: Highland area with medium precipitation, lack of wind. 3) Northeast China: Plenty of wind [28], no monsoon and lack of precipitation.…”

Section: A Results Of Community Detectionmentioning

confidence: 99%

Chinese cities’ air quality pattern and correlation

Zhang

Guan

et al. 2020

J. Stat. Mech.

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“…An avalanche of studies [4][5][6][7] has identified greenhouse gases(GHGs) as a major contributor to global warming with energy-related GHGs accounting for majority of total GHGs. Emissions from consumption of coal [8], natural gas, petroleum products and other GHG-emitting sources of energy has contributed to the hefty weight of energy-related GHGs to total GHG. Due to the emission-free and sustainable [9][10][11][12][13] characteristics of renewable-and-sustainable sources of energy (RSSE), increasing the share of RSSEs in future energy-mix [8] dominates the submitted Intended Nationally Determined Contributions (INDCs) of parties to the 2015 Paris accord.…”

Section: Introductionmentioning

confidence: 99%

“…Emissions from consumption of coal [8], natural gas, petroleum products and other GHG-emitting sources of energy has contributed to the hefty weight of energy-related GHGs to total GHG. Due to the emission-free and sustainable [9][10][11][12][13] characteristics of renewable-and-sustainable sources of energy (RSSE), increasing the share of RSSEs in future energy-mix [8] dominates the submitted Intended Nationally Determined Contributions (INDCs) of parties to the 2015 Paris accord. New technologies for renewable energy generation such as the wave energy linear generator proposed by Franzitta et al [14] are paramount to achieving energy independence.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Renewable Energy Consumption under Zero Assumptions

Oppong

Acheampong

et al. 2018

Sustainability

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Renewable energy, as an environmentally friendly and sustainable source of energy, is key to realizing the nationally determined contributions of the United States (US) to the December 2015 Paris agreement. Policymakers in the US rely on energy forecasts to draft and implement cost-minimizing, efficient and realistic renewable and sustainable energy policies but the inaccuracies in past projections are considerably high. The inaccuracies and inconsistencies in forecasts are due to the numerous factors considered, massive assumptions and modeling flaws in the underlying model. Here, we propose and apply a machine learning forecasting algorithm devoid of massive independent variables and assumptions to model and forecast renewable energy consumption (REC) in the US. We employ the forecasting technique to make projections on REC from biomass (REC-BMs) and hydroelectric (HE-EC) sources for the 2009-2016 period. We find that, relative to reference case projections in Energy Information Administration's Annual Energy Outlook 2008, projections based on our proposed technique present an enormous improvement up to~138.26-fold on REC-BMs and 24.67-fold on HE-EC; and that applying our technique saves the US~2692.62 PJ petajoules (PJ) on HE-EC and~9695.09 PJ on REC-BMs for the 8-year forecast period. The achieved high-accuracy is also replicable to other regions.

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“…The wind power could help with emissions reduction and energy conservation [3]. And the wind energy is abundant in China due to its vast land and long coastline [3]. It is clearly to find that the wind power is a sustainable and clean generation with environmentally friendly production using green and renewable energy [4][5][6], then it is essential to promote this generation for adjusting the coal-dominant energy structure and realizing the low-carbon society.…”

Section: Introductionmentioning

confidence: 99%

“…As for the wind power, it is a clean and renewable energy for power generating. The wind power could help with emissions reduction and energy conservation [3]. And the wind energy is abundant in China due to its vast land and long coastline [3].…”

Section: Introductionmentioning

confidence: 99%

From Generating Cost to Environmental Impact: Comparing Wind Power with Thermal Power

Li¹,

Cui²,

Liang³

et al. 2018

dtcse

Self Cite

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Abstract. As the fast development of China's economy, the power generation has increased rapidly to support the economic growth. In China, thermal power, as a traditional power generation, consumes more fossil energy and brings great damage on environment. As for wind power, a clean and renewable power generation, uses wind energy and is beneficial to environment. In order to describe the differences between these two generations in detail, this paper compared the thermal power with the wind power from the generating total cost to the environmental impact in a quantitative way. And the scenario analysis was adopted for predicting the energy conservation and the emission reduction by wind power. The results show that: (1) Coal is still the dominant input for energy consumption in China and the installed capacity of thermal power takes 65% to total installed capacity. As for the wind power, wind energy only has a share of 5.89% to the total energy consumption and the installed capacity of wind power contributes 8% on total installed capacity. (2) The total cost of thermal power is higher than that of wind power, with 0.47 yuan/KWh. (3) Comparing with the thermal power, the wind power save 238.87 million tons of coal equivalent in total. In addition, the wind power reduces 668.54 million tons of CO 2 emissions, 1.73 million tons of SO 2 emissions, 1.8 million tons of NO x emissions and 0.3 million tons of smoke emissions during 2008 to 2015. (4) The coal conservation and the emission reduction by wind power are much greater in high economic growth rate scenario.

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Overview of Wind Power in China: Status and Future

Cited by 49 publications

References 36 publications

Chinese cities’ air quality pattern and correlation

Chinese cities’ air quality pattern and correlation

Forecasting Renewable Energy Consumption under Zero Assumptions

From Generating Cost to Environmental Impact: Comparing Wind Power with Thermal Power

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