Study on Evaluation Method for New Energy Big Data Service Project Applying Improved TOPSIS

doi:10.12204/j.issn.1000-7229.2021.03.015

Abstract

Abstract:

The use of comprehensive evaluation for scientific and quantitative analysis of the growing new energy big data service (NEBDS) platform plays an important role in promoting and improving the construction of data service operation. At first, considering the purpose and demand of NEBDS, the comprehensive index system for evaluating the operational efficiency of NEBDS is constructed from the four aspects, i.e., economy, technology, environmental benefits and social services. At the same time, the combination of subjective and objective weight method based on improved analytic hierarchy process and entropy is also studied by application of the principle of minimum discriminatory information. Secondly, in view of the fact that decision-makers have different subjective tendencies in value judgment when facing gains and losses, starting from assessing both the limited rationality and risk avoidance of investors, the comprehensive evaluation method for ranking the optimal new energy big data service projects is proposed by using the prospect theory to improve the TOPSIS method. Finally, the effectiveness of the proposed comprehensive benefit evaluation method is verified by simulation.

Key words: new energy big data service, comprehensive evaluation, combination weighting, prospect theory, TOPSIS

CLC Number:

TM92

CHEN Renjie, LI Huaqu, PENG Xiaotao, YANG Jun, DONG Xuzhu, LIU Shouwen, LI Xiangjie. Study on Evaluation Method for New Energy Big Data Service Project Applying Improved TOPSIS[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(3): 126-134.

Figures/Tables 9

Fig.1 Comprehensive benefit evaluation indices of NEBDS

Table 1 Scale of relative importance of indices

Table 2 Statistics of evaluation indices for different operational scenarios

Table 3 Index importance data

Table 4 Weights of indices

Table 5 Matrix of prospect values

Fig.2 Evaluation results of the comprehensive benefits of different operational scenario considering prospect theory

Fig.3 Evaluation results of the comprehensive benefits of different operational scenario according to TOPSIS method

Fig.4 Sensitivity analysis of each index weight in scenario 1

References 20

[1]	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.
[2]	曾鸣, 刘英新, 周鹏程, 等. 综合能源系统建模及效益评价体系综述与展望[J]. 电网技术, 2018,42(6):1697-1708.
	ZENG Ming, LIU Yingxin, ZHOU Pengcheng, et al. Review and prospects of integrated energy system modeling and benefit evaluation[J]. Power System Technology, 2018,42(6):1697-1708.
[3]	KO W, KIM J. Generation expansion planning model for integrated energy system considering feasible operation region and generation efficiency of combined heat and power[J]. Energies, 2019,12(2):226. doi: 10.3390/en12020226 URL
[4]	薛禹胜, 赖业宁. 大能源思维与大数据思维的融合:(一)大数据与电力大数据[J]. 电力系统自动化, 2016,40(1):1-8.
	XUE Yusheng, LAI Yening. Integration of macro energy thinking and big data thinking: Part one big data and power big data[J]. Automation of Electric Power Systems, 2016,40(1):1-8.
[5]	曹军威, 袁仲达, 明阳阳, 等. 能源互联网大数据分析技术综述[J]. 南方电网技术, 2015,9(11):1-12.
	CAO Junwei, YUAN Zhongda, MING Yangyang, et al. Survey of big data analysis technology for energy Internet[J]. Southern Power System Technology, 2015,9(11):1-12.
[6]	董福贵, 张也, 尚美美. 分布式能源系统多指标综合评价研究[J]. 中国电机工程学报, 2016,36(12):3214-3223. doi: 10.13334/j.0258-8013.pcsee.152536 URL
	DONG Fugui, ZHANG Ye, SHANG Meimei. Multi-criteria comprehensive evaluation of distributed energy system[J]. Proceedings of the CSEE, 2016,36(12):3214-3223.
[7]	陈柏森, 廖清芬, 刘涤尘, 等. 区域综合能源系统的综合评估指标与方法[J]. 电力系统自动化, 2018,42(4):174-182.
	CHEN Baisen, LIAO Qingfen, LIU Dichen, et al. Comprehensive evaluation indices and methods for regional integrated energy system[J]. Automation of Electric Power Systems, 2018,42(4):174-182.
[8]	张涛, 朱彤, 高乃平, 等. 分布式冷热电能源系统优化设计及多指标综合评价方法的研究[J]. 中国电机工程学报, 2015,35(14):3706-3713. doi: 10.13334/j.0258-8013.pcsee.2015.14.027 URL
	ZHANG Tao, ZHU Tong, GAO Naiping, et al. Optimization design and multi-criteria comprehensive evaluation method of combined cooling heating and power system[J]. Proceedings of the CSEE, 2015,35(14):3706-3713.
[9]	黄伟, 郭兆蕊, 华亮亮. 多主体利益协调的区域综合能源系统综合评价[J]. 电力建设, 2019,40(4):81-89.
	HUANG Wei, GUO Zhaorui, HUA Liangliang. Comprehensive evaluation of regional integrated energy system considering multi-participant interest coordination[J]. Electric Power Construction, 2019,40(4):81-89.
[10]	蔡泽祥, 李立浧, 刘平, 等. 能源大数据技术的应用与发展[J]. 中国工程科学, 2018,20(2):72-78.
	CAI Zexiang, LI Licheng, LIU Ping, et al. Application and development of energy big data[J]. Strategic Study of CAE, 2018,20(2):72-78.
[11]	邓雪, 李家铭, 曾浩健, 等. 层次分析法权重计算方法分析及其应用研究[J]. 数学的实践与认识, 2012,42(7):93-100.
	DENG Xue, LI Jiaming, ZENG Haojian, et al. Research on computation methods of AHP wight vector and its applications[J]. Mathematics in Practice and Theory, 2012,42(7):93-100.
[12]	赵书强, 汤善发. 基于改进层次分析法、CRITIC法与逼近理想解排序法的输电网规划方案综合评价[J]. 电力自动化设备, 2019,39(3):143-148,162.
	ZHAO Shuqiang, TANG Shanfa. Comprehensive evaluation of transmission network planning scheme based on improved analytic hierarchy process, CRITIC method and TOPSIS[J]. Electric Power Automation Equipment, 2019,39(3):143-148,162.
[13]	罗毅, 李昱龙. 基于熵权法和灰色关联分析法的输电网规划方案综合决策[J]. 电网技术, 2013,37(1):77-81.
	LUO Yi, LI Yulong. Comprehensive decision-making of transmission network planning based on entropy weight and grey relational analysis[J]. Power System Technology, 2013,37(1):77-81.
[14]	汪颖, 王欢, 王昕. 一种基于改进灰色关联分析的电压暂降源识别方法[J]. 电测与仪表, 2020,57(15):1-7.
	WANG Ying, WANG Huan, WANG Xin. A method of voltage sag source identification based on improved grey relational analysis[J]. Electrical Measurement & Instrumentation, 2020,57(15):1-7.
[15]	李彦斌, 于心怡, 王致杰. 采用灰色关联度与TOPSIS法的光伏发电项目风险评价研究[J]. 电网技术, 2013,37(6):1514-1519.
	LI Yanbin, YU Xinyi, WANG Zhijie. Risk assessment on photovoltaic power generation project by grey correlation analysis and TOPSIS method[J]. Power System Technology, 2013,37(6):1514-1519.
[16]	徐斌, 马骏, 陈青, 等. 基于改进AHP-TOPSIS法的经济开发区配电网综合评价指标体系和投资策略研究[J]. 电力系统保护与控制, 2019,47(22):35-44.
	XU Bin, MA Jun, CHEN Qing, et al. Research on comprehensive evaluation index system and investment strategy of economic development zone distribution network based on improved AHP-TOPSIS method[J]. Power System Protection and Control, 2019,47(22):35-44.
[17]	林良生, 邹平国, 陈红, 等. 基于熵权的逼近理想解排序法在核电设备质量评价中的应用分析[J]. 电力建设, 2014,35(2):91-94. doi: 10.3969/j.issn.1000-7229.2014.02.018 URL
	LIN Liangsheng, ZOU Pingguo, CHEN Hong, et al. Application of TOPSIS method based on entropy weight in nuclear power equipment quality evaluation[J]. Electric Power Construction, 2014,35(2):91-94. doi: 10.3969/j.issn.1000-7229.2014.02.018 URL
[18]	陈琪华, 何育恒, 曾永忠, 等. TOPSIS法在垃圾焚烧发电锅炉蒸汽空气预热器综合评价中的应用[J]. 中国电机工程学报, 2020,40(4):1274-1281.
	CHEN Qihua, HE Yuheng, ZENG Yongzhong, et al. Application of TOPSIS method in the comprehensive evaluation of steam-air preheater on waste incineration boiler[J]. Proceedings of the CSEE, 2020,40(4):1274-1281.
[19]	高山, 王深哲, 李海峰, 等. 基于前景理论的电网规划方案综合决策方法[J]. 电网技术, 2014,38(8):2029-2036. doi: 10.13335/j.1000-3673.pst.2014.08.002 URL
	GAO Shan, WANG Shenzhe, LI Haifeng, et al. Prospect theory based comprehensive decision-making method of power network planning schemes[J]. Power System Technology, 2014,38(8):2029-2036. doi: 10.13335/j.1000-3673.pst.2014.08.002 URL
[20]	KAHNEMAN D, TVERSKY A. Prospect theory: An analysis of decision under risk[J]. Econometrica, 1979,47(2):263. doi: 10.2307/1914185 URL