1State Grid Jiangsu Electric Power Company, Nanjing 210024, China 2School of Business, Nanjing University, Nanjing 210093, China 3School of Information Management, Nanjing University, Nanjing 210093, China 4Electric Power Economic Technology Research Institute, State Grid Jiangsu Electric Power Company, Nanjing 210018, China 5State Grid Yangzhou Electric Power Company, Yangzhou 225008, China
[Objective]This paper establishes data extraction model and evaluation mechanism for the return of investment analysis on new energy projects. All data is from the State Grid Jiangsu Electric Power Company in China. [Methods] First, we proposed a new big data management framework based on the State Grid Jiangsu Electric Power Company SG-ERP system architecture. Second, we extracted evaluative data based on Golden Gate technology, and constructed an evaluation system covering the economic, social and environmental aspects of the target projects at different development stages (i.e. decision-making, construction and operation). Finally, we examined the proposed system with the Delphi Law. [Results] We got the weight of variation coefficient and the economic, social and environmental benefits of new energy projects of Y City in 2015. [Limitations] The classification schemes for the evaluation criteria could be further refined. [Conclusions] The proposed system can evaluate the return of investments for new energy power grid projects. The data extraction method, evaluation system and weight algorithm could be used in other studies.
高骞, 杨旸, 胡广伟, 徐超, 沈高锋, 赵健. 电力大数据驱动的新能源项目投资效益#br#
评价研究*——以Y市电网公司SG-ERP系统为例[J]. 数据分析与知识发现, 2016, 32(12): 57-65.
Qian Gao, Yang Yang, Guangwei Hu, Chao Xu, Gaofeng Shen, Jian Zhao. Analyzing Return of Investment for New Energy Project with Big Data: Case Study of SG-ERP System in Y City. Data Analysis and Knowledge Discovery, 2016, 32(12): 57-65.
(Qi Yulin, Jing Shuangyan.Current Situation and Development of Management Information System of Electric Power Communication Network in China[J]. Information of Electric Power, 2000(3): 15-18.)
(Guo Xiaoli, Qu Zhaoyang, Li Xiaodong, et al.Visual Data Mining Model in Power Grid Based on SOM Clustering[J]. Information Science, 2012, 30(2): 206-209.)
(Li Peng, Wu Jiang.Research on Performance Evaluation of Grid Information Platform Based on Fuzzy Hierarchy Balanced Scorecard Method[J]. Information Science, 2014, 32(7): 86-91.)
(Wei Ronghui.Research on Integrated Management, Operation and Maintenance of Competitive Technical Intelligence System for Large Central State-owned Enterprise Groups[J]. Document Information & Knowledge, 2012(1): 18-21.)
[5]
Davidson I E.Evaluation and Effective Management of Non-technical Losses in Power Systems[J]. The Transactions of the South African Institute of Electrical Engineers, 2003, 94(3): 39-42.
[6]
Sun H.Integrated Modeling of Electric Power System Operations and Electricity Market Risks with Applications[J]. Dissertation Abstracts International, 2006, 45(3): 35-52.
(Cai Guangzong, Shang Shanshan.Research on the Establishment of Post Evaluation System for the Operation Process of Power Grid Project[J]. Science and Technology Management Research, 2014, 34(2): 199-204.)
(Liu Shengli, Cao Yang, Feng Yueliang, et al.Research and Application of Distribution Grid Investment Effectiveness Evaluation and Decision-making Model[J]. Power System Protection and Control, 2015, 43(2): 119-125.)
[9]
Albert R, Albert I, Nakarado G L.Structural Vulnerability of the North American Power Grid[J]. Physical Review E, 2004, 69(2): 025103.
[10]
Karki R, Hu P, Billinton R.A Simplified Wind Power Generation Model for Reliability Evaluation[J]. IEEE Transactions on Energy Conversion, 2006, 21(2): 533-540.
(Tang Yongsheng, Wang Liang, Li Chen, et al.Investment Benefit Evaluation Model of Grid Enterprise Based on Improved Gray Correlation Degree Method[J]. Water Resources & Power, 2013, 31(5): 186-189.)
[12]
Karki R, Hu P, Billinton R.A Simplified Wind Power Generation Model for Reliability Evaluation[J]. IEEE Transactions on Energy Conversion, 2006, 21(2): 533-540.
(Zheng Chengde, Wang Quansheng, Chen Xi.Empirical Study on Business Process Performance of ERP System in Chinese Enterprises[J]. Journal of Intelligence, 2010, 29(1): 68-72.)
(Zheng Haiyan, Jin Nong, Ji Cong, et al.Data Analysis Technology and Typical Application of Electric Power User[J]. Power System Technology, 2015, 39(11): 3147-3152.)
[15]
Naumann F, Bilke A, Bleiholder J, et al.Data Fusion in Three Steps: Resolving Inconsistencies at Schema, Tuple, and Value-level[J]. Bulletin of the Technical Committee on Data Engineering, 2010(2): 21-31.
[16]
Solano M A, Ekwaro-Osire S, Tanik M M.High-Level Fusion for Intelligence Applications Using Recombinant Cognition Synthesis[J]. Information Fusion, 2012, 13(1): 79-98.
[17]
Jagadish H V, Gehrke J, Labrinidis A, et al.Big Data and Its Technical Challenges[J]. Communications of the ACM, 2014, 57(7): 86-94.
[18]
Lin G, Liang J, Qian Y.An Information Fusion Approach by Combining Multigranulation Rough Sets and Evidence Theory[J]. Information Sciences, 2015, 314: 184-199.
(Yang Huafei, Li Donghua, Cheng Ming.Analysis and Research on Key Technologies and Construction Ideas of Power Big Data[J]. Power Information & Communication Technology, 2015, 13(1): 7-10.)
[20]
Gulhar M, Kibria G K, Albatineh A N, et al.A Comparison of Some Confidence Intervals for Estimating the Population Coefficient of Variation: A Simulation Study[J]. Statistics and Operations Research Transactions, 2012, 36(1): 45-68.
