期刊首页 优先出版 当期阅读 过刊浏览 作者中心 关于期刊 English

《中国工程科学》 >> 2018年 第20卷 第2期 doi: 10.15302/J-SSCAE-2018.02.011

能源大数据技术的应用与发展

1. 华南理工大学电力学院,广州 510641;

2. 南方电网科学研究院有限责任公司,广州 510080

资助项目 :中国工程院咨询项目“‘互联网+’行动计划的发展战略研究”(2016-ZD-03) 收稿日期: 2018-03-13 修回日期: 2018-03-26 发布日期: 2018-05-31 13:20:27.000

下一篇 上一篇

摘要

能源大数据技术作为“互联网+”智慧能源的重要组成部分,对推动我国能源革命、促进能源转型以及刺激能源行业创新发展具有重大作用。本文以“互联网+”智慧能源为背景,阐述了能源大数据技术的结构形态与关键特征;立足于大数据在我国新时代下能源行业发展的重要支撑意义,探讨了能源大数据技术的主要应用领域,重点讨论了目前实现能源大数据的主要制约因素;最后提出了几点发展对策,力求助力我国能源大数据的建设与完善,推动“互联网+”智慧能源的深度发展。

图片

图1

参考文献

[ 1 ] 李立浧, 张勇军, 陈泽兴, 等. 智能电网与能源网融合的模式及其发展前景 [J]. 电力系统自动化, 2016, 40(11): 1–9.
Li L C, Zhang Y J, Chen Z X, et al. Merger between smart grid and energy-net: Mode and development prospects [J]. Automation of Electric Power Systems, 2016, 40(11):1–9. Chinese. 链接1 链接2

[ 2 ] Jiang H, Wang K, Wang Y, et al. Energy big data: A survey [J]. IEEE Access, 2016 (4): 3844–3861. 链接1 链接2

[ 3 ] 刘敦楠, 唐天琦, 赵佳伟, 等. 能源大数据信息服务定价及其在电力市场中的应用 [J]. 电力建设, 2017, 38(2): 52–59.
Liu D N, Tang T Q, Zhao J W, et al. Big energy data information service pricing and its application in electricity market [J]. Electric Power Construction, 2017, 38(2):52–59. Chinese. 链接1 链接2

[ 4 ] 黄小庆, 陈颉, 田世明, 等. 电动汽车充电站规划、运行中的大数据集成应用 [J]. 电网技术, 2016, 40(3): 762–767.
Huang X Q, Chen J, Tian S M, et al. Big data integration for opti-mal planning and operation of electric vehicle charging stations [J]. Power System Technology, 2016, 40(3):762–767. Chinese. 链接1 链接2

[ 5 ] 张勇军, 陈泽兴, 蔡泽祥, 等. 新一代信息能源系统: 能源互联网[J]. 电力自动化设备, 2016, 36(9): 1–7.
Zhang Y J, Chen Z X, Cai Z X, et al. New generation of cyber-en-ergy system: Energy Internet [J]. Electric Power Automation Equipment, 2016, 36(9): 1–7. Chinese. 链接1 链接2

[ 6 ] 田世明, 栾文鹏, 张东霞, 等. 能源互联网技术形态与关键技术[J]. 中国电机工程学报, 2015, 35(14): 3482–3494.
Tian S M, Luan W P, Zhang D X, et al. Technical forms and key technologies on energy Internet [J]. Proceedings of the CSEE, 2015, 35(14): 3842–3894. Chinese.

[ 7 ] 陈启鑫, 刘敦楠, 林今, 等. 能源互联网的商业模式与市场机制( 一) [J]. 电网技术, 2015, 39(11): 3050–3056.
Chen Q X, Liu D N, Lin J, et al. Business models and market 007Strategic Study of CAE 2018 Vol. 20 No. 2mechanisms of Energy Internet (I) [J]. Power System Technology, 2015, 39(11): 3050–3056. Chinese.

[ 8 ] Naimi A I, Westreich D J. Big data: A revolution that will trans-form how we live, work, and think [J]. Mathematics & Computer Education, 2013, 47(17): 181–183.

[ 9 ] McAfee A, Brynjolfsson E, Davenport T H, et al. Big data: The management revolution [J]. Harvard Business Review, 2012, 90(10): 60–68. 链接1 链接2

[10] Zhou K, Fu C, Yang S. Big data driven smart energy management: From big data to big insights [J]. Renewable and Sustainable Energy Reviews, 2016 (56): 215–225. 链接1 链接2

[11] 张东霞, 苗新, 刘丽平, 等. 智能电网大数据技术发展研究 [J]. 中国电机工程学报, 2015, 35(1): 2–12.
Zhang D X, Miao X, Liu L P, et al. Research on development strat-egy for smart grid big data [J]. Proceedings of the CSEE, 2015, 35(1): 2–12. Chinese.

[12] Billinton R, Gao Y. Multistate wind energy conversion system models for adequacy assessment of generating systems incorpo-rating wind energy [J]. IEEE Transaction on Energy Converstion, 2008, 23(1): 163–170. 链接1 链接2

[13] Kaldellis J. Optimum autonomous wind–power system sizing for remote consumers, using long-term wind speed data [J]. Applied Energy, 2002, 71(3): 215–233. 链接1 链接2

[14] 鲁宗相, 徐曼, 乔颖, 等. 风电功率预测的新型互联网运营模式设计 [J]. 电网技术, 2016, 40(1): 125–131.
Lu Z X, Xu M, Qiao Y, et al. New Internet based operation pattern design of wind power forecasting system [J]. Power System Tech-nology, 2016, 40(1): 125–131. Chinese. 链接1 链接2

[15] 刘世成, 张东霞, 朱朝阳, 等. 能源互联网中大数据技术思考 [J]. 电力系统自动化, 2016, 40(8): 14–21.
Liu S C, Zhang D X, Zhu Z Y, et al. A view on big data in en-ergy internet [J]. Automation of Electric Power Systems, 2016, 40(8):14–21. Chinese. 链接1 链接2

[16] B.A.U.M. Consult Gmbh. Smart Energy made in Germany [R].
B.A.U.M. Consult Gmbh. Smart Energy made in Germany [R]. Munich: B.A.U.M. Consult Gmbh, 2012.

[17] 王喜文, 王叶子. 德国信息化能源(E-Energy) 促进计划 [J]. 电力需求侧管理, 2011, 13(4): 75–76.
Wang X W, Wang Y Z. Introduction of German smart grid “E-En-ergy” project promotion [J]. Power Demand Side Management, 2011, 13(4):75–76. Chinese. 链接1 链接2

[18] Liu Y, Zhan L, Zhang Y, et al. Wide-area measurement system de-velopment at the distribution level: An FNET/GridEye example [J]. IEEE Transactions on Power Delivery, 2015, 31(2): 721–731. 链接1 链接2

[19] Zhou D, Guo J, Zhang Y, et al. Distributed data analytics platform for wide-area synchrophasor measurement systems [J]. IEEE Transactions on Smart Grid, 2016, 7(5): 2397–2405. 链接1 链接2

[20] 彭小圣, 邓迪元, 程时杰, 等. 面向智能电网应用的电力大数据关键技术 [J]. 中国电机工程学报, 2015, 35(3): 503–511.
Peng X S, Deng D Y, Cheng S J, et al. Key technologies of electric power big data and its application prospects in smart grid [J]. Pro-ceedings of the CSEE, 2015, 35(3): 503–511. Chinese.

[21] Magavern S. Greening the buffalo niagara medical campus [R]. New York: The Open Buffalo Innovation Lab, 2015.

[22] Becker T, Curry E, Jentzsch A, et al. New horizons for a data-driven economy: Roadmaps and action plans for technology, businesses, policy, and society [M]. London: Springer International Publish-ing, 2016. 链接1 链接2

[23] 李存斌, 李小鹏, 田世明, 等. 能源互联网电力信息深度融合风险传递: 挑战与展望 [J]. 电力系统自动化, 2017, 41(11): 17–25.
Li C B, Li X P, Tian S M, et al. Challenges and prospects of risk transmission in deep fusion of electric power and information for Energy Internet [J]. Automation of Electric Power Systems, 2017, 41(11): 17–25. Chinese. 链接1 链接2

相关研究