|
|
Data Transactions in Energy Internet: Architecture and Key Technologies |
Guo Qinglai1, 2, Wang Bohong1, 2, Tian Nianfeng1, 2, Sun Hongbin1, 2, Wen Bojian3 |
1. Department of Electrical Engineering Tsinghua University Beijing 100084 China; 2. State Key Laboratory of Control and Simulation of Power Systems and Generation Equipments Tsinghua University Beijing 100084 China; 3. Information Center Guangdong Electric Power Company Guangzhou 510630 China |
|
|
Abstract With the development of big data technology, data have become an increasingly important special asset. However, the protection of privacy by many members in the energy Internet makes the data in the energy Internet cut off from each other and unable to circulate, which seriously limits the potential of data to be converted into actual economic value. Under the background of the gradual maturity of energy Internet, the high integration of energy and data information will greatly promote the coordinated development of them. Therefore, it is of great strategic significance for the development of energy Internet to build a complete data transaction system architecture and study the related technology implementation. This paper summarizes the data transaction architecture in the energy Internet, and comprehensively surveys and discusses many problems in data transaction, such as data value evaluation, data pricing, data right confirmation, data privacy protection, etc. Besides, this paper explains the relationships between these problems and gets their preliminary solutions based on information theory, game theory, block chain technology, and other existing theories or technologies, which will guide the formation and promotion of data transaction technology in the energy Internet.
|
Received: 12 March 2020
|
|
|
|
|
[1] Liu Yue, He Jia, Guo Minjie, et al.An overview of big data industry in China[J]. China Communications, 2014, 11(12): 1-10. [2] 申建建, 曹瑞, 苏承国, 等. 水火风光多源发电调度系统大数据平台架构及关键技术[J]. 中国电机工程学报, 2019, 39(1): 43-55. Shen Jianjian, Cao Rui, Su Chengguo, et al.Big data platform architecture and key techniques of power generation scheduling for hydro-thermal-wind-solar hybrid system[J]. Proceedings of the CSEE, 2019, 39(1): 43-55. [3] 孙宏斌, 郭庆来, 潘昭光. 能源互联网: 理念、架构与前沿展望[J]. 电力系统自动化, 2015, 39(19): 1-8. Sun Hongbin, Guo Qinglai, Pan Zhaoguang.Energy internet: concept, architecture and frontier outlook[J]. Automation of Electric Power Systems, 2015, 39(19): 1-8. [4] 孙宏斌, 郭庆来, 潘昭光, 等. 能源互联网:驱动力、评述与展望[J]. 电网技术, 2015, 39(11): 3005-3013. Sun Hongbin, Guo Qinglai, Pan Zhaoguang, et al.Energy internet: driving force, review and outlook[J]. Power System Technology, 2015, 39(11): 3005-3013. [5] Shannon C E.A mathematical theory of communication[J]. The Bell System Technical Journal, 1948, 27(3): 379-423. [6] 钟义信. 信息科学原理[M]. 北京: 北京邮电大学出版社, 2013. [7] 康重庆, 姚良忠. 高比例可再生能源电力系统的关键科学问题与理论研究框架[J]. 电力系统自动化, 2017, 41(9): 2-11. Kang Chongqing, Yao Liangzhong.Key scientific issues and theoretical research framework for power systems with high proportion of renewable energy[J]. Automation of Electric Power Systems, 2017, 41(9): 2-11. [8] 姚良忠, 朱凌志, 周明, 等. 高比例可再生能源电力系统的协同优化运行技术展望[J]. 电力系统自动化, 2017, 41(9): 36-43. Yao Liangzhong, Zhu Lingzhi, Zhou Ming, et al.Prospects of coordination and optimization for power systems with high proportion of renewable energy[J]. Automation of Electric Power Systems, 2017, 41(9): 36-43. [9] 鲁宗相, 黄瀚, 单葆国, 等. 高比例可再生能源电力系统结构形态演化及电力预测展望[J]. 电力系统自动化, 2017, 41(9): 12-18. Lu Zongxiang, Huang Han, Shan Baoguo, et al.Morphological evolution model and power forecasting prospect of future electric power systems with high proportion of renewable energy[J]. Automation of Electric Power Systems, 2017, 41(9): 12-18. [10] 程耀华, 张宁, 王佳明, 等. 面向高比例可再生能源并网的输电网规划方案综合评价[J]. 电力系统自动化, 2019, 43(3): 33-42. Cheng Yaohua, Zhang Ning, Wang Jiaming, et al.Comprehensive evaluation of transmission network planning for integration of high-penetration renewable energy[J]. Automation of Electric Power Systems, 2019, 43(3): 33-42. [11] 贵阳大数据交易所. 2016年中国大数据交易产业白皮书[R]. 贵阳, 2016. [12] 中国信息通信研究院. 大数据白皮书(2018年)[R]. 北京, 2018. [13] 郭春芳. 不确定性分析视角下大数据信息服务定价研究[D]. 北京: 北京交通大学, 2019. [14] 倪渊, 李子峰, 张健. 基于AGA-BP神经网络的网络平台交易环境下数据资源价值评估研究[J]. 情报理论与实践, 2019, 43(1): 135-142. Ni Yuan, Li Zifeng, Zhang Jian.Research on data resources value assessment model based on AGA-BP neural network in the background of network platform transaction[J]. Information Studies: Theory & Application, 2019, 43(1): 135-142. [15] 宋立丰, 宋远方, 国潇丹. 基于数据权的现实与虚拟闲置资产共享——区块链视角下的共享经济发展研究[J]. 经济学家, 2019(8): 39-47. Song Lifeng, Song Yuanfang, Guo Xiaodan.Reality and virtual idle asset sharing based on data right-research on the development of shared economy from the perspective of blockchain[J]. Economist, 2019(8): 39-47. [16] 吴江. 数据交易机制初探——新制度经济学的视角[J]. 天津商业大学学报, 2015, 35(3): 3-8. Wu Jiang.A preliminary study of the mechanism of data transaction-a view of new institutional economics[J]. Journal of Tianjin University of Commerce, 2015, 35(3): 3-8. [17] 朱宝丽. 数据产权界定:多维视角与体系建构[J]. 法学论坛, 2019, 34(5): 78-86. Zhu Baoli.Definition of data property rights: multidimensional perspectives and system construction[J]. Legal Forum, 2019, 34(5): 78-86. [18] 史宇航. 数据交易法律问题研究[D]. 上海: 上海交通大学, 2017. [19] 韩璇, 袁勇, 王飞跃. 区块链安全问题:研究现状与展望[J]. 自动化学报, 2019, 45(1): 206-225. Han Xuan, Yuan Yong, Wang Feiyue.Security problems on blockchain: the state of the art and future trends[J]. Acta Automatica Sinica, 2019, 45(1): 206-225. [20] 许重建, 李险峰. 区块链交易数据隐私保护方法[J]. 计算机科学, 2020, 49(3): 281-286. Xu Chongjian, Li Xianfeng.Data privacy protection method of block chain transaction[J]. Computer Science, 2020, 49(3): 281-286. [21] 王伟, 蒋菱, 王峥, 等. 基于向量评估遗传算法的智能电网大数据交易模型研究[J]. 电网与清洁能源, 2016, 32(10): 1-8. Wang Wei, Jiang Ling, Wang Zheng, et al.Trade model of smart grid big data based on vector evaluated genetic algorithm[J]. Power System and Clean Energy, 2016, 32(10): 1-8. [22] 奈存剑. 虚拟化数据管理平台研究[D]. 武汉: 华中科技大学, 2013. [23] Coase R H.The federal communications commission[J]. The Journal of Law & Economics, 1959, 2: 1-40. [24] Coase R H.The nature of the firm[J]. Economica, 1937, 4(16): 386-405. [25] Coase R H.The problem of social cost[J]. The Journal of Law & Economics, 1960, 3: 1-44. [26] Wang Bohong, Guo Qinglai, Yang Tianyu.From uncertainty elimination to profit enhancement: role of data in demand response[C]//2019 IEEE Innovative Smart Grid Technologies-Asia (ISGT Asia), 2019: 2952-2957. [27] Chen Runze, Sun Hongbin, Guo Qinglai, et al.A generation-interval-based mechanism for managing the power generation uncertainties of variable generation[J]. IEEE Transactions on Sustainable Energy, 2016, 7(3): 1060-1070. [28] Huang Chaoming, Yang Hongtzer, Huan Ching-lieng.Bi-objective power dispatch using fuzzy satisfaction-maximizing decision approach[J]. IEEE Transactions on Power Systems, 1997, 12(4): 1715-1721. [29] Miranda V, Hang P S.Economic dispatch model with fuzzy wind constraints and attitudes of dispatchers[J]. IEEE Transactions on Power Systems, 2005, 20(4): 2143-2145. [30] Lee D, Shin H, Baldick R.Bivariate probabilistic wind power and real-time price forecasting and their applications to wind power bidding strategy development[J]. IEEE Transactions on Power Systems, 2018, 33(6):6087-6097. [31] Bitar E Y, Rajagopal R, Khargonekar P P, et al.bringing wind energy to market[J]. IEEE Transactions on Power Systems, 2012, 27(3): 1225-1235. [32] Dvorkin Y, Lubin M, Backhaus S, et al.Uncertainty sets for wind power generation[J]. IEEE Transactions on Power Systems, 2016, 31(4): 3326-3327. [33] Narahari Y.Game theory and mechanism design[M]. Singapore: World Scientific, 2014. [34] Samadi P, Mohsenian-Rad H, Schober R, et al.Advanced demand side management for the future smart grid using mechanism design[J]. IEEE Transactions on Smart Grid, 2012, 3(3): 1170-1180. [35] Tang W, Jain R.Aggregating correlated wind power with full surplus extraction[J]. IEEE Transactions on Smart Grid, 2018, 9(6): 6030-6038. [36] Nekouei E, Alpcan T, Chattopadhyay D.Game-theoretic frameworks for demand response in electricity markets[J]. IEEE Transactions on Smart Grid, 2015, 6(2): 748-758. [37] Contreras-Ocaña J E, Ortega-Vazquez M A, Zhang B. Participation of an energy storage aggregator in electricity markets[J]. IEEE Transactions on Smart Grid, 2019, 10(2): 1171-1183. [38] 彭云. 大数据环境下数据确权问题研究[J]. 现代电信科技, 2016, 46(5): 17-20. Peng Yun.Research on authenticating data rights in big data environment[J]. Modern Science & Technology of Telecommunications, 2016, 46(5): 17-20. [39] 于戈, 聂铁铮, 李晓华, 等. 区块链系统中的分布式数据管理技术-挑战与展望[J/OL]. 计算机学报, 2019: 1-27[2020-05-14]. http://kns.cnki.net/kcms/detail/11. 1826.tp.20191029.1604.004.html. Yu Ge, Nie Tiezheng, Li Xiaohua, et al.The challenge and prospect of distributed data management techniques in blockchain systems[J]. Chinese Journal of Computers, 2019: 1-27[2020-05-14]. The challenge and prospect of distributed data management techniques in blockchain systems[J]. Chinese Journal of Computers, 2019: 1-27[2020-05-14]. http://kns.cnki.net/kcms/detail/ 11.1826.tp.20191029.1604.004.html. [40] 王海龙, 田有亮, 尹鑫. 基于区块链的大数据确权方案[J]. 计算机科学, 2018, 45(2): 15-19. Wang Hailong, Tian Youliang, Yin Xin.Blockchain-based big data right confirmation scheme[J]. Computer Science, 2018, 45(2): 15-19. [41] 李兆璨, 王利明, 葛思江, 等. 基于正交编码的大数据纯文本水印方法[J]. 计算机科学, 2019, 46(12): 148-154. Li Zhaocan, Wang Liming, Ge Sijiang, et al.Big data plain text watermarking based on orthogonal coding[J]. Computer Science, 2019, 46(12): 148-154. [42] 张佳乐, 赵彦超, 陈兵, 等. 边缘计算数据安全与隐私保护研究综述[J]. 通信学报, 2018, 39(3): 1-21. Zhang Jiale, Zhao Yanchao, Chen Bing, et al.Survey on data security and privacy-preserving for the research of edge computing[J]. Journal on Communications, 2018, 39(3): 1-21. [43] 宋磊, 罗其亮, 罗毅, 等. 电力系统实时数据通信加密方案[J].电力系统自动化, 2004, 28(14): 76-81. Song Lei, Luo Qiliang, Luo Yi, et al.Encryption on power systems real-time data communication[J]. Automation of Electric Power Systems, 2004, 28(14): 76-81. [44] 尹传烨. 电力信息系统安全策略应用研究[D]. 武汉: 华中科技大学, 2006. [45] 房梁, 殷丽华, 郭云川, 等. 基于属性的访问控制关键技术研究综述[J]. 计算机学报, 2017, 40(7): 1680-1698. Fang Liang, Yin Lihua, Guo Yunchuan, et al.A survey of key technologies in attribute-based access control scheme[J]. Chinese Journal of Computers, 2017, 40(7): 1680-1698. [46] 王保义, 邱素改, 张少敏. 电力调度自动化系统中基于可信度的访问控制模型[J]. 电力系统自动化, 2012, 36(12): 76-81. Wang Baoyi, Qiu Sugai, Zhang Shaomin.A credibility-based access control model in dispatching automation system[J]. Automation of Electric Power Systems, 2012, 36(12): 76-81. [47] Gupta A, Harinarayan V, Rajaraman A.Virtual database technology[C]//Proceedings 14th International Conference on Data Engineering, 1998: 297-301. [48] 移动计算, 不移动数据[Z/OL]. 即云天下(北京)数据科技有限公司.http://www.bigknow.com.cn/. [49] 霍成义. 云计算数据隐私保护关键技术研究[J]. 信息安全研究, 2019, 5(12): 1106-1109. Huo Chengyi.Research on key technologies of cloud computing data privacy protection[J]. Journal of Information Security Research, 2019, 5(12): 1106-1109. [50] 孙茂华. 安全多方计算及其应用研究[D]. 北京: 北京邮电大学, 2013. [51] 罗永龙, 徐致云, 黄刘生. 安全多方的统计分析问题及其应用[J]. 计算机工程与应用, 2005, 41(24): 141-143. Luo Yonglong, Xu Zhiyun, Huang Liusheng.Secure multi-party statistical analysis problems and their applications[J]. Computer Engineering and Applications, 2005, 41(24): 141-143. [52] 李锋. 面向数据挖掘的隐私保护方法研究[D]. 上海: 上海交通大学, 2008. [53] Lan J, Guo Q, Sun H.Demand side data generating based on conditional generative adversarial networks[J]. Energy Procedia, 2018, 152: 1188-1193. [54] Xin Shujun, Guo Qinglai, Wang Jianhui, et al.Information masking theory for data protection in future cloud-based energy management[J]. IEEE Transactions on Smart Grid, 2018, 9(6): 5664-5676. [55] 辛蜀骏. 面向能量管理系统的信息-物理耦合分析理论研究[D]. 北京: 清华大学, 2017. |
|
|
|