Study on China’s wind power development path—Based on the target for 2030

As an important form of clean energy, China’s wind power has developed rapidly and been given priority in the national energy strategy in recent years. Factors such as resource potential, technological progress, GDP growth, emission regulation scheme, and grid absorptive capacity may all affect wind power development. This study aims to explore China’s wind power development optimization path during the period 2013–2030 from the perspective of minimum cost. The model is based upon a dynamic programming approach with the restraints of the learning curve and the technology diffusion model to indicate the influence of the above-mentioned factors. We have the following findings: (1) The government could achieve its established 2030 cumulative installed capacity target; GDP growth and incentive policies, which are closely related to construction cost, are critical factors that may impact on wind power development; (2) The grid absorptive ability is the most crucial factor constraining wind power development in the initial stage while learning rate and carbon emission permit price, are critical factors affecting the wind power development in subsequent stages; (3) With the interactions among the relevant factors considered, the wind power development goal of 400GW for 2030 could be achieved several years ahead of schedule even under extremely unfavorable scenarios.

[1]  Malin Song,et al.  Review of hidden carbon emissions, trade, and labor income share in China, 2001–2011 , 2014 .

[2]  K. Sperling,et al.  Evaluation of wind power planning in Denmark – Towards an integrated perspective , 2010 .

[3]  Ling Zhang,et al.  Optimal path for China's strategic petroleum reserve: A dynamic programming analysis , 2012 .

[4]  G. Zou,et al.  The long-term relationships among China's energy consumption sources and adjustments to its renewable energy policy , 2012 .

[5]  Aoife Foley,et al.  Current methods and advances in forecasting of wind power generation , 2012 .

[6]  Yi-Ming Wei,et al.  A model based on stochastic dynamic programming for determining China's optimal strategic petroleum reserve policy , 2009 .

[7]  Umar K. Mirza,et al.  Forecasting the diffusion of wind power in Pakistan , 2011 .

[8]  F. Dinçer,et al.  The analysis on wind energy electricity generation status, potential and policies in the world , 2011 .

[9]  Malin Song,et al.  Review of the network environmental efficiencies of listed petroleum enterprises in China , 2015 .

[10]  Bo Xia,et al.  Development route of the wind power industry in China , 2014 .

[11]  Bernd Möller,et al.  Feasibility study of China’s offshore wind target by 2020 , 2012 .

[12]  María Isabel Blanco,et al.  Can the future EU ETS support wind energy investments , 2008 .

[13]  Joanna I. Lewis,et al.  China's wind power industry: Policy support, technological achievements, and emerging challenges , 2012 .

[14]  A. K. Bhattacharya,et al.  Stochastic study of the wind-energy potential of India , 1990 .

[15]  Lena Neij,et al.  Use of experience curves to analyse the prospects for diffusion and adoption of renewable energy technology , 1997 .

[16]  Xiaolu Zhao,et al.  Status and prospects of Chinese wind energy , 2010 .

[17]  A. Kossoy,et al.  State and Trends of Carbon Pricing 2014 , 2014 .

[18]  Wim Turkenburg,et al.  Global experience curves for wind farms , 2005 .

[19]  Kim B. Clark,et al.  Behind the learning curve: a sketch of the learning process , 1991 .

[20]  Rong-Gang Cong An optimization model for renewable energy generation and its application in China: A perspective of maximum utilization , 2013 .

[21]  Xuesong Zhang,et al.  Maintaining environmental quality while expanding biomass production: Sub-regional U.S. policy simulations , 2013 .

[22]  Paolo Agnolucci,et al.  Wind electricity in Denmark: a survey of policies, their effectiveness and factors motivating their introduction , 2007 .

[23]  Patrik Söderholm,et al.  Empirical challenges in the use of learning curves for assessing the economic prospects of renewable energy technologies , 2007 .

[24]  Cunbin Li,et al.  The investment risk analysis of wind power project in China , 2013 .

[25]  V.V.N. Kishore,et al.  A review of technology diffusion models with special reference to renewable energy technologies , 2010 .

[26]  Peter Lund,et al.  Market penetration rates of new energy technologies , 2006 .

[27]  Nicos Christofides,et al.  A dynamic programming approach for solving single-source uncapacitated concave minimum cost network flow problems , 2006, Eur. J. Oper. Res..

[28]  Patrik Söderholm,et al.  Wind power learning rates: A conceptual review and meta-analysis☆ , 2012 .

[29]  Patrick A. Narbel,et al.  Global wind power development: Economics and policies , 2013 .

[30]  Consolación Gil,et al.  Scientific production of renewable energies worldwide: An overview , 2013 .

[32]  Sufang Zhang,et al.  Large scale wind power integration in China: Analysis from a policy perspective , 2012 .

[33]  Dennis Y.C. Leung,et al.  Wind energy development and its environmental impact: A review , 2012 .

[34]  A. Valle,et al.  Forecasting accuracy of wind power technology diffusion models across countries , 2011 .

[35]  Hwa Meei Liou,et al.  Wind power in Taiwan: Policy and development challenges , 2010 .

[36]  Yan Xu,et al.  Review on wind power development and relevant policies in China during the 11th Five-Year-Plan period , 2012 .

[37]  P. S. Kulkarni,et al.  Wind electric power in the world and perspectives of its development in India , 2009 .

[38]  B. Haack,et al.  Economic analysis of small wind-energy conversion systems , 1982 .

[39]  Karin Ibenholt Explaining learning curves for wind power , 2002 .

[40]  Gianfranco Rizzo,et al.  Application of dynamic programming to the optimal management of a hybrid power plant with wind turbines, photovoltaic panels and compressed air energy storage , 2012 .

[41]  Fan Ying,et al.  Does generation form influence environmental efficiency performance? An analysis of China’s power system , 2012 .

[42]  Kimberley Opie,et al.  Potential for forest carbon plantings to offset greenhouse emissions in Australia: economics and constraints to implementation , 2013, Climatic Change.

[43]  M. Hanewinkel,et al.  Modelling of forest conversion planning with an adaptive simulation-optimization approach and simultaneous consideration of the values of timber, carbon and biodiversity , 2009 .