China in global wind power development: Role, status and impact

Zhang, Shijie; Wei, Jing; Chen, Xi; Zhao, Yan

doi:10.1016/j.rser.2020.109881

Cited by 100 publications

(30 citation statements)

References 48 publications

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“…Therefore, offshore wind power is bound to play an important role in the future world energy structure. China has become the global leader in wind power, its wind power development has greatly promoted the growth rate of global wind power, and it has become an indispensable force in global wind power development (Zhang et al 2020). China has more than 18,000 km of continental coastline.…”

Section: Literature Reviewmentioning

confidence: 99%

The influencing factors and hierarchical relationships of offshore wind power industry in China

Yang

Zhao

2021

Environ Sci Pollut Res

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As a new and cost-effective renewable energy power generation technology, offshore wind power is getting more and more attention. The development of offshore wind power industry is affected by policy-making, technology management, resources and environment, market supply and demand, and the relationship among the influencing factors is complex. This paper analyzes the factors that affect offshore wind power industry from a unique and comprehensive perspective. Fourteen factors are selected and interpretative structural model (ISM) is established to study the relationship between the influencing factors of offshore wind power industry. The results show that 14 influencing factors can be divided into five levels: the first level is the surface factors, including the economic incentive policy, operation mechanism, industrial chain, energy market mechanism, investment, and financing mechanism; the second and third levels are the intermediate factors, including generation cost, operation management, and offshore wind power technology; the fourth and fifth levels are deep-seated factors, including development planning and grid price, site selection, R&D investment, environmental protection policy, and offshore wind power supply. Deep-seated factors have a direct impact on the intermediate factors, the intermediate factors have an important impact on the surface factors, and the surface factors directly affect the development of offshore wind power industry. The influence of the 14 factors selected in this paper on offshore wind power industry is from bottom to top, from deep to shallow.

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Section: Literature Reviewmentioning

confidence: 99%

The influencing factors and hierarchical relationships of offshore wind power industry in China

Yang

Zhao

2021

Environ Sci Pollut Res

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“…Up to 2020, China's cumulative installed capacity of wind energy exceeded 216 million kilowatts, accounting for around 40% of the world total amount. China has become one of the leaders in the development of global wind power [3]. The cumulative installed capacity of new energy power generation in the State Grid Operating Area is 350 million kilowatts, of which the installed capacity of wind power generation is 169 million kilowatts with a yearly increase of 16%.…”

Section: Introductionmentioning

confidence: 99%

A comprehensive review on deep learning approaches in wind forecasting applications

Luo

Yang

et al. 2022

CAAI Trans on Intel Tech

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The effective use of wind energy is an essential part of the sustainable development of human society, in particular, at the recent unprecedented pressure in shaping a low carbon energy environment. Accurate wind resource and power forecasting play a key role in improving the wind penetration. However, it has not been well adopted in the real-world applications due to the strong stochastic characteristics of wind energy. In recent years, the application boost of deep learning methods provides new effective tools in wind forecasting. This paper provides a comprehensive overview of the forecasting models based on deep learning in the field of wind energy. Featured approaches include timeseries-based recurrent neural networks, restricted Boltzmann machines, convolutional neural networks as well as auto-encoder-based approaches. In addition, future development directions of deep-learning-based wind energy forecasting have also been discussed. K E Y W O R D S deep learning, deep neural networks, learning (artificial intelligence)This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…In recent years, in order to solve the energy supply crisis and environmental pollution problems, China has vigorously promoted the development of renewable energy, and the installed capacity of wind power and photovoltaics has achieved rapid growth. In 2010, the cumulative installed capacity of wind power ranked first in the world [1]. As of the end of June 2020, the cumulative grid-connected capacity of wind power reached 216.75 GW.…”

Section: Introductionmentioning

confidence: 99%

Bidding strategy of thermal power compound differential evolution game under the market mechanism of peak regulation auxiliary service

Dong

et al. 2021

IET Generation Trans & Dist

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As the main provider of peak regulation auxiliary services, thermal power units are particularly important to ensure their revenue in an uncertain environment. To obtain the optimal bidding strategy for thermal power units, a thermal power peak regulation bidding model based on the Northeast Power Grid's auxiliary service market bidding mechanism is established. Secondly, the evolutionary game theory is introduced into the bidding strategy of thermal power units. Meanwhile, in order to solve the multi-party game problem, a compound differential evolution game algorithm is constructed to solve the two-tiers bidding. Finally, based on the actual operating data of a typical day, the efficiency of the compound differential evolution game algorithm bidding strategy is verified, and the bidding strategy of thermal power units in different situations is discussed. The results show that when all units participate in peak regulation, the first-tier quotation is an optimal strategy at 0.19 yuan kWh −1 and below, and the second-tier quotation is distributed between 0.6-0.8 yuan kWh −1. When only some units are involved in peak regulation, the first-tier quotation of thermal power units adopts a high price strategy, mostly at 0.3 yuan kWh −1 , and the second-tier quotation is still based on a high price strategy.

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China in global wind power development: Role, status and impact

Cited by 100 publications

References 48 publications

The influencing factors and hierarchical relationships of offshore wind power industry in China

The influencing factors and hierarchical relationships of offshore wind power industry in China

A comprehensive review on deep learning approaches in wind forecasting applications

Bidding strategy of thermal power compound differential evolution game under the market mechanism of peak regulation auxiliary service

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