Predictive Analysis of Water Usage for Thermal Power Generation in Next Twenty Years

doi:10.3969/j.issn.1000－7229.2013.08.004

Abstract

Abstract: Through the analysis of the economic development situation in China and the characteristics of power structure in recent years， the electricity demand situation in China in the next 20 years was predicted with using electricity elasticity coefficient. Based on the five feasible power development programs， the water consumption of thermal power plants in the next 20 years were predicted and analyzed. The results show that， in the future thermal power construction in China， the DC cooling unit should be gradually reduced， the subcritical， supercritical， and ultra supercritical units should be constructed speedily， as well as the application of direct cooling technology with sea water in coastal region and the air-cooling technology should be strengthened. In addition， the development and large-scale application of water-saving technology should be also strengthened.

Key words: water resources, thermal power generation, water-saving technology, water intake and water consumption, forecast

CHE Dejing， MENG Jie， CHEN Yonghui， LI Pengfei， WEI Gaosheng. Predictive Analysis of Water Usage for Thermal Power Generation in Next Twenty Years[J]. ELECTRIC POWER CONSTRUCTION, 2013, 34(8): 17-21.

References

［1］中电联统计信息部. 2010年全国电力工业统计快报［J］. 中国电力企业管理，2011（2）：108-109. ［2］张丽艳，冯杰，杨涛.我国火电行业用水效率分析与节水对策［J］.水电能源科学，2012，30（2）:190-192. ［3］韩买良.火力发电行业用水分析及对策［J］.工业水处理，2010，30（2）:4-7. ［4］姜蓓蕾，王丽丽.我国火电行业用水效率及节水措施分析［J］.水利科技与经济，2010，16（3）:264-266. ［5］杨文静，孟文俊.火力发电厂节水措施［J］.内蒙古水利，2010（2）:68-69. ［6］中华人民共和国统计局.2007中国统计年鉴［M］.北京:中国统计出版社，2007. ［7］中华人民共和国统计局.2008中国统计年鉴［M］.北京:中国统计出版社，2008. ［8］中华人民共和国统计局.2009中国统计年鉴［M］.北京:中国统计出版社，2009. ［9］中华人民共和国统计局.2010中国统计年鉴［M］.北京:中国统计出版社，2010. ［10］中华人民共和国统计局.2011中国统计年鉴［M］.北京:中国统计出版社，2011. ［11］《中国电力年鉴》编辑委员会.2007中国电力年鉴［M］.北京:中国电力出版社，2007. ［12］《中国电力年鉴》编辑委员会.2008中国电力年鉴［M］.北京:中国电力出版社，2008. ［13］《中国电力年鉴》编辑委员会.2009中国电力年鉴［M］.北京:中国电力出版社，2009. ［14］《中国电力年鉴》编辑委员会.2010中国电力年鉴［M］.北京:中国电力出版社，2010.［15］《中国电力年鉴》编辑委员会.2011中国电力年鉴［M］.北京:中国电力出版社，2011. ［16］水力发电学会.中国水力发电年鉴（2011）［M］.北京:中国电力出版社，2011. ［17］周丽芳.中国核电发展浅析［J］.经济研究导报，2011（117）:200-201. ［18］罗玉福，张宏友.风电发展与风动专业建设［J］.辽宁大学学报，2012，39（3）:286-288. ［19］李晓云，张华，席兵.火电厂用水与节水技术［M］.北京:中国水利水电出版社，2008.

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