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The Scientific World Journal
Volume 2014, Article ID 946932, 7 pages
http://dx.doi.org/10.1155/2014/946932
Research Article

How to Develop Renewable Power in China? A Cost-Effective Perspective

1School of Economics and Management, North China Electric Power University, Beijing 102206, China
2Centre for Environmental and Climate Research (CEC), Lund University, 22362 Lund, Sweden
3State Key Laboratory Breeding Base of Green Chemistry Synthesis Technology, College of Chemical Engineering and Materials Science, Zhejiang University of Technology, Hangzhou 310014, China

Received 30 August 2013; Accepted 13 November 2013; Published 21 January 2014

Academic Editors: P. Del Río and I. Dyner

Copyright © 2014 Rong-Gang Cong and Shaochuan Shen. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

To address the problems of climate change and energy security, Chinese government strived to develop renewable power as an important alternative of conventional electricity. In this paper, the learning curve model is employed to describe the decreasing unit investment cost due to accumulated installed capacity; the technology diffusion model is used to analyze the potential of renewable power. Combined with the investment cost, the technology potential, and scenario analysis of China social development in the future, we develop the Renewable Power Optimization Model (RPOM) to analyze the optimal development paths of three sources of renewable power from 2009 to 2020 in a cost-effective way. Results show that (1) the optimal accumulated installed capacities of wind power, solar power, and biomass power will reach 169000, 20000, and 30000 MW in 2020; (2) the developments of renewable power show the intermittent feature; (3) the unit investment costs of wind power, solar power, and biomass power will be 4500, 11500, and 5700 Yuan/KW in 2020; (4) the discounting effect dominates the learning curve effect for solar and biomass powers; (5) the rise of on-grid ratio of renewable power will first promote the development of wind power and then solar power and biomass power.