全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...
-  2017 

太阳能光热发电技术成熟度预测模型
Prediction model of concentrating solar power technology maturity

DOI: 10.6040/j.issn.1672-3961.0.2016.480

Keywords: 全球能源互联网,预测模型,CSP,GEI,
GEI
,technology maturity,prediction model,CSP

Full-Text   Cite this paper   Add to My Lib

Abstract:

摘要: 研判光热发电(concentrating solar power, CSP)技术发展进程,可为全球能源互联网规划和建设提供重要参考依据。建立基于S型曲线的光热发电技术成熟度(global energy interconnection, GEI)预测模型,通过整理分析光热发电技术专利信息,对模型参数进行回归分析,进而预测未来典型年份光热发电的技术成熟度,并分析政策驱动和资金投入对光热发电技术发展进程的影响。研究表明,当前光热发电技术成熟度较低,仍处于技术发展期的初级阶段,预计在2032年左右,全球光热发电技术高度成熟,将进入大规模商业化应用阶段,在北非、南美洲东西海岸、我国西部等地区推进大型光热电站建设,支撑全球能源互联网构建。
Abstract: Judging concentrating solar power(CSP)technology trend could provide critical reference for global energy interconnection(GEI)planning and construction. The technology maturity prediction model of CSP based on S-shaped growth curve was established, and the CSP patent information was collected and the model parameters regression analysis was made in order to predict the technical maturity of CSP in the future. The impact on CSP technology trend from policy driven and capital investment was studied. The results showed that the current maturity of CSP was much lower, which was still in the early technology development stages. CSP technology maturity would reach a high degree by 2032, entering extensive commercial application stage. Large-scale CSP stations which were built in North Africa, South East and West coast of the Latin America, western China and other regions, would play an important role in supporting the construction of GEI

References

[1]  刘振亚.全球能源互联网[M].北京:中国电力出版社,2015.
[2]  解东升,石少帅,陈士林,等. 高风险岩溶隧道突水突泥灾害前兆规律与应用研究[J].山东大学学报(工学版),2012,42(1):81-86. XIE Dongsheng, SHI Shaoshuai, CHEN Shilin, et al. The precursor law of inrush of clay and water in a high-risk karst tunnel and its application[J]. Journal of Shandong University(Engineering Science), 2012, 42(1):81-86.
[3]  王兴旺.基于专利地图的技术分析预测方法研究[J].情报科学,2013,53(12):88-94. WANG Xingwang. Research on the method of technology analyzing and forecasting based on patent map[J].Information Science, 2013, 53(12):88-94.
[4]  王兴旺,汤琰洁.基于专利地图的技术预测体系构建及其实证研究[J].情报理论与实践,2013,36(3):51-55. WANG Xingwang, TANG Yanjie. Research on technology forecasting system and case study based on patent map[J].Information Studies: Theory & Application, 2013, 36(3):51-55.
[5]  李慧,胡云,李存华. 基于粗糙集理论的瓦斯灾害信息特征提取技术[J].山东大学学报(工学版),2012,42(5):91-95. LI Hui, HU Yun, LI Cunhua. The technique of gas disaster information feature extraction based on rough set theory[J]. Journal of Shandong University(Engineering Science), 2012, 42(5):91-95.
[6]  高子涵,侯元元,潘锐焕.我国太阳能光热发电技术专利分析[J].中国科技信息,2014,11(2):35-38 GAO Zihan, HOU Yuanyuan, PAN Ruihuan. Research on patent information of concentrating solar power technology in China[J].China Science and Technology Information, 2014, 11(2):35-38.
[7]  杜尔顺,张宁,康重庆,等. 太阳能光热发电并网运行及优化规划研究综述与展望[J].中国电机工程学报,2016,36(21):5765-5775. DU Ershun, ZHANG Ning, KANG Chongqing, et al. Reviews and prospects of the operation and planning optimization for grid intergrated concentrating solar power[J].Proceedings of the CSEE, 2016, 36(21):5765-5775.
[8]  谢国辉,李琼慧.全球能源互联网技术创新重点领域及关键技术[J].中国电力,2016,49(3):18-23. XIE Guohui, LI Qionghui. Important fields and key techonology of innovation for global energy interconnection[J]. Electic Power, 2016, 49(3):18-23.
[9]  REN21. Renewables 2016 global status report[R].Paris: REN21 Secretariat, 2016.
[10]  陈德棉,潘皖印,毛家杰.科学预测和技术预测的方法研究[J].科学学研究,1997,15(4):56-62. CHEN Demian, PAN Wanyin, MAO Jiajie. Research on scientific prediction and technology forcasting appoaches[J]. Studies in Science of Science, 1997, 15(4):56-62.
[11]  侯元元,夏勇其,黄裕荣,等.基于专利信息的太阳能光热发电技术竞争态势分析[J].情报探索,2014,8(6):54-58. HOU Yuanyuan, XIA Yongqi, HUANG Yurong, et al. Patent information-based analysis on competitive situation of concentrating solar power technology[J].Information Research, 2014, 8(6):54-58.
[12]  程旋.技术预测模型分析研究[D].北京:华北电力大学,2010. CHENG Xuan. Study on the technology forecasting models[J]. Beijing: North China Electric Power University, 2010.
[13]  郭志波,董健,庞成. 多技术融合的Mean-Shift目标跟踪算法[J].山东大学学报(工学版),2015,45(2):10-16. GUO Zhibo, DONG Jian, PANG Cheng. A Mean-Shift target tracking algorithm fused multi technology[J].Journal of Shandong University(Engineering Science), 2015, 45(2):10-16.
[14]  张恒旭,施啸寒,刘玉田,等.我国西北地区可再生能源基地对全球能源互联网构建的支撑作用[J].山东大学学报(工学版),2016,46(4):96-101. ZHANG Hengxu, SHI Xiaohan, LIU Yutian, et al. Support of the renewable energy base in northwest of China on the construction of global energy interconnection[J]. Journal of Shandong University(Engineering Science), 2016, 46(4):96-101.
[15]  International Energy Agency. Technology roadmap solar thermal electricity[R]. Paris: IEA Publications, 2014.
[16]  赵莉晓.基于专利分析的RFID技术预测和专利战略研究——从技术生命周期角度[J].科学学与科学技术管理,2012,33(11):24-30. ZHAO Lixiao. Technology foresight of radio frequency identificatcion and patent strategy based on patent analysis:based on technology life cycle[J].Science of Science and Management of S.& T., 2012, 33(11):24-30.
[17]  杨良选.技术成熟度多维评估模型研究[D].长沙:国防科学技术大学,2011. YANG Liangxuan. Research on technology maturity multi-dimentional assessment model. Changsha: National University of Defense Technology, 2011.
[18]  王丽芳,蒋国瑞,黄梯云.基于支持向量机的技术成熟度预测[J].科技管理研究,2009,29(5):296-298. WANG Lifang, JIANG Guorui, HUANG Tiyun. Forecasting technology maturity based on suppt vector machine[J].Science and Technology Management Research, 2009, 29(5):296-298.
[19]  施珺,朱敏. 一种基于灰色系统和支持向量机的预测优化模型[J].山东大学学报(工学版),2012,42(5):7-11. SHI Jun, ZHU Min. An optimization model for forecasting based on grey system and support vector machine[J].Journal of Shandong University(Engineering Science), 2012, 42(5):7-11.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133