Evaluation of scenario applicability of new energy storage based on BMW and TOPSIS methods

doi:10.19799/j.cnki.2095-4239.2024.0744

Abstract

Abstract:

The existing technical routes and application scenarios of new energy storage projects are relatively simple. In the future, with the gradual expansion of new energy storage technology types and scenario applications, it will be necessary to establish a scenario applicability evaluation system considering various technology types and to compare the maturity of various scenarios and the comprehensive performance of various types of energy storage in different scenarios. First, to meet the power system regulation requirements of active power balance at different time scales under typical scenarios, the technical requirements of energy storage in different scenarios were compared from the viewpoints of power, duration, and response time. Based on the set energy-based scenarios, power-based scenarios, and comprehensive scenarios, typical configuration schemes commonly used in practice were given. Second, an energy storage suitability evaluation method that combined BWM and TOPSIS was proposed. A comprehensive evaluation index system comprising 11 indicators in four categories (technology, economy, potential, and sustainability) was established. Finally, three scenarios were weighted by expert scoring and the BMW weighting method, and 11 typical configuration schemes were evaluated by the TOPSIS method. The research results provide guidance for the planning and layout of new energy storage technologies.

Key words: new energy storage, scenario applicability evaluation, best-worst method, TOPSIS method

CLC Number:

TK 02

Juan ZHAO, Bingchen LI, Zhe WANG, Fen YUE, Zijia HUI, Xing ZHANG. Evaluation of scenario applicability of new energy storage based on BMW and TOPSIS methods[J]. Energy Storage Science and Technology, 2025, 14(1): 443-455.

Figures/Tables 16

Table 1

Table 2

Table 3

Fig.1

Table 4

Table 5

Fig.2

Fig.3

Table 6

Table 7

Table 8

Table 9

Fig.4

References 40

1	赵唯嘉, 张宁, 康重庆, 等. 光伏发电出力的条件预测误差概率分布估计方法[J]. 电力系统自动化, 2015, 39(16): 8-15. DOI: 10.7500/AEPS20141017007.
	ZHAO W J, ZHANG N, KANG C Q, et al. A method of probabilistic distribution estimation of conditional forecast error for photovoltaic power generation[J]. Automation of Electric Power Systems, 2015, 39(16): 8-15. DOI: 10.7500/AEPS20141017007.
2	赵书强, 王明雨, 胡永强, 等. 基于不确定理论的光伏出力预测研究[J]. 电工技术学报, 2015, 30(16): 213-220. DOI: 10.19595/j.cnki. 1000-6753.tces.2015.16.027.
	ZHAO S Q, WANG M Y, HU Y Q, et al. Research on the prediction of PV output based on uncertainty theory[J]. Transactions of China Electrotechnical Society, 2015, 30(16): 213-220. DOI: 10.19595/j.cnki.1000-6753.tces.2015.16.027.
3	赖昌伟, 黎静华, 陈博, 等. 光伏发电出力预测技术研究综述[J]. 电工技术学报, 2019, 34(6): 1201-1217. DOI: 10.19595/j.cnki.1000-6753.tces.180326.
	LAI C W, LI J H, CHEN B, et al. Review of photovoltaic power output prediction technology[J]. Transactions of China Electrotechnical Society, 2019, 34(6): 1201-1217. DOI: 10.19595/j.cnki.1000-6753.tces.180326.
4	李明节, 陈国平, 董存, 等. 新能源电力系统电力电量平衡问题研究[J]. 电网技术, 2019, 43(11): 3979-3986. DOI: 10.13335/j.1000-3673.pst.2019.0440.
	LI M J, CHEN G P, DONG C, et al. Research on power balance of high proportion renewable energy system[J]. Power System Technology, 2019, 43(11): 3979-3986. DOI: 10.13335/j.1000-3673.pst.2019.0440.
5	鲁宗相, 林弋莎, 乔颖, 等. 极高比例可再生能源电力系统的灵活性供需平衡[J]. 电力系统自动化, 2022, 46(16): 3-16.
	LU Z X, LIN Y S, QIAO Y, et al. Flexibility supply-demand balance in power system with ultra-high proportion of renewable energy[J]. Automation of Electric Power Systems, 2022, 46(16): 3-16.
6	张军六, 李佳朋, 唐震, 等. 基于本地测量的高比例新能源电力系统不平衡功率估算与附加功率控制策略[J]. 电力科学与技术学报, 2022, 37(3): 50-60. DOI: 10.19781/j.issn.1673-9140.2022.03.006.
	ZHANG J L, LI J P, TANG Z, et al. Local measurement based unbalanced active power estimation and supplementary power modulation for power systems with high proportions of renewable energy[J]. Journal of Electric Power Science and Technology, 2022, 37(3): 50-60. DOI: 10.19781/j.issn.1673-9140.2022.03.006.
7	张宁, 康重庆, 陈治坪, 等. 基于序列运算的风电可信容量计算方法[J]. 中国电机工程学报, 2011, 31(25): 1-9. DOI: 10.3321/j.issn: 1000-3673.2006.21.004.
	ZHANG N, KANG C Q, CHEN Z P, et al. Wind power credible capacity evaluation model based on sequence operation[J]. Proceedings of the CSEE, 2011, 31(25): 1-9. DOI: 10.3321/j.issn: 1000-3673.2006.21.004.
8	何俊, 邓长虹, 徐秋实, 等. 风光储联合发电系统的可信容量及互补效益评估[J]. 电网技术, 2013, 37(11): 3030-3036. DOI: 10.13335/j.1000-3673.pst.2013.11.012.
	HE J, DENG C H, XU Q S, et al. Assessment on capacity credit and complementary benefit of power generation system integrated with wind farm, energy storage system and photovoltaic system[J]. Power System Technology, 2013, 37(11): 3030-3036. DOI: 10.13335/j.1000-3673.pst.2013.11.012.
9	何俊, 邓长虹, 徐秋实, 等. 基于等可信容量的风光储电源优化配置方法[J]. 电网技术, 2013, 37(12): 3317-3324. DOI: 10.13335/j.1000-3673.pst.2013.12.014.
	HE J, DENG C H, XU Q S, et al. Optimal configuration of distributed generation system containing wind PV battery power sources based on equivalent credible capacity theory[J]. Power System Technology, 2013, 37(12): 3317-3324. DOI: 10.13335/j.1000-3673.pst.2013.12.014.
10	赵龙, 李文升, 曹永吉, 等. 计及源荷匹配特性的新能源可信容量评估方法[J]. 现代电力, 2023, 40(5): 687-695. DOI: 10.19725/j.cnki.1007-2322.2022.0047.
	ZHAO L, LI W S, CAO Y J, et al. Renewable energy credible capacity assessment method considering source-load matching characteristics[J]. Modern Electric Power, 2023, 40(5): 687-695. DOI: 10.19725/j.cnki.1007-2322.2022.0047.
11	薛松, 王跃锦. 基于异地输送的可再生能源消纳成本仿真分析[J]. 电网技术, 2015, 39(3): 647-654. DOI: 10.13335/j.1000-3673.pst.2015.03.010.
	XUE S, WANG Y J. Simulation and analysis on accommodating cost for power generated by trans-regional renewable energy sources[J]. Power System Technology, 2015, 39(3): 647-654. DOI: 10.13335/j.1000-3673.pst.2015.03.010.
12	吴垠. 考虑发用电资源协调的可再生能源消纳成本优化机制研究[D]. 南京: 东南大学, 2022. DOI: 10.27014/d.cnki.gdnau.2022.004800.
	WU Y. Study on the optimization mechanism of renewable energy consumption cost considering the coordination of power generation and consumption resources[D]. Nanjing: Southeast University, 2022. DOI: 10.27014/d.cnki.gdnau.2022.004800.
13	郝宇霞. 新能源发电的消纳成本优化模型及应用研究[D]. 北京: 华北电力大学(北京), 2023.
	HAO Y X. Research on optimization model and application of accomodation cost of new energy generation[D]. Beijing: North China Electric Power Uinicersity(Beijing), 2023.
14	张志, 邵尹池, 伦涛, 等. 电化学储能系统参与调峰调频政策综述与补偿机制探究[J]. 电力工程技术, 2020, 39(5): 71-77, 84.
	ZHANG Z, SHAO Y C, LUN T, et al. Review on the policies and compensation mechanism of BESS participation in the auxiliary service of frequency and peak modulation[J]. Electric Power Engineering Technology, 2020, 39(5): 71-77, 84.
15	廖东颖, 朱丹蕾, 谢保胜, 等. 参与辅助服务的电化学储能系统调峰调频技术及机制探究[J]. 电工技术, 2022(6): 127-131. DOI: 10.19768/j.cnki.dgjs.2022.06.042.
	LIAO D Y, ZHU D L, XIE B S, et al. Research on peak shaving and frequency modulation technology and mechanism of electrochemical energy storage system participating in auxiliary services[J]. Electric Engineering, 2022(6): 127-131. DOI: 10. 19768/j.cnki.dgjs.2022.06.042.
16	陈雷, 文婷. 考虑调峰调频双重约束的储能规划方法研究[J]. 南方能源建设, 2023, 10(2): 62-70. DOI: 10.16516/j.gedi.issn2095-8676.2023.02.009.
	CHEN L, WEN T. Research on energy storage planning method considering the dual constraints of peak shaving and frequency modulation[J]. Southern Energy Construction, 2023, 10(2): 62-70. DOI: 10.16516/j.gedi.issn2095-8676.2023.02.009.
17	李秀慧, 崔炎. 考虑调峰调频需求的新能源电网储能优化配置[J]. 储能科学与技术, 2022, 11(11): 3594-3602. DOI: 10.19799/j.cnki.2095-4239.2022.0331.
	LI X H, CUI Y. Optimal allocation of energy storage in renewable energy grid considering the demand of peak and frequency regulation[J]. Energy Storage Science and Technology, 2022, 11(11): 3594-3602. DOI: 10.19799/j.cnki.2095-4239.2022.0331.
18	王森, 李凤婷, 张高航, 等. 面向新能源高渗透系统调峰和调频的系统级储能容量需求分析[J]. 电力自动化设备, 2024, 44(1): 24-31. DOI: 10.16081/j.epae.202304005.
	WANG S, LI F T, ZHANG G H, et al. Capacity demand analysis of system-level energy storage for peak regulation and frequency regulation of power system with high penetration of renewable energy[J]. Electric Power Automation Equipment, 2024, 44(1): 24-31. DOI: 10.16081/j.epae.202304005.
19	黎淑娟. 储能电池参与电网快速调频的综合选型与优化配置[D]. 长沙: 湖南大学, 2018.
	LI S J. Comprehensive selection and optimal configuration of energy storage battery participating in fast frequency regulation of power grid[D]. Changsha: Hunan University, 2018.
20	黎淑娟, 李欣然, 黄际元, 等. 调频用储能电源的技术经济选型分析[J]. 太阳能学报, 2018, 39(12): 3550-3557. DOI: 10.19912/j.0254-0096.2018.12.035.
	LI S J, LI X R, HUANG J Y, et al. Technical and economic selection analysis of energy storage power supply for frequency regulation[J]. Acta Energiae Solaris Sinica, 2018, 39(12): 3550-3557. DOI: 10.19912/j.0254-0096.2018.12.035.
21	赵松, 张谦, 霍红岩, 等. 基于层次分析法AHP和TOPSIS的储能参与新能源一次调频选型方案[J]. 现代电力, 2024, 41(1): 134-143. DOI: 10.19725/j.cnki.1007-2322.2022.0175.
	ZHAO S, ZHANG Q, HUO H Y, et al. Selection scheme for energy storage to participate in primary frequency regulation of new energy based on AHP and TOPSIS methods[J]. Modern Electric Power, 2024, 41(1): 134-143. DOI: 10.19725/j.cnki.1007-2322.2022.0175.
22	陆秋瑜, 杨银国, 陈俊生, 等. 考虑选型的混合储能平抑海上风电波动的容量优化研究[J]. 武汉大学学报(工学版), 2024, 57(6): 782-791. DOI: 10.14188/j.1671-8844.2024-06-010.
	LU Q Y, YANG Y G, CHEN J S, et al. Research on capacity optimization of hybrid energy storage to stabilize offshore wind power fluctuation considering energy storage selection[J]. Engineering Journal of Wuhan University, 2024, 57(6): 782-791. DOI: 10.14188/j.1671-8844.2024-06-010.
23	郭久亿. 基于电化学储能选型的不同典型用户储能配置评估与运行优化研究[D]. 成都: 四川大学, 2021.
	GUO J Y. Research on energy storage configuration evaluation and operation optimization of different typical users based on electrochemical energy storage selection[D]. Chengdu: Sichuan University, 2021.
24	薛金花, 叶季蕾, 陶琼, 等. 电力储能技术的适用性评价模型与方法研究[J]. 高电压技术, 2018, 44(7): 2239-2246. DOI: 10.13336/j.1003-6520.hve.20180628017.
	XUE J H, YE J L, TAO Q, et al. Feasibility evaluation model and method of energy storage technologies in power system[J]. High Voltage Engineering, 2018, 44(7): 2239-2246. DOI: 10.13336/j.1003-6520.hve.20180628017.
25	冯喜春, 张松岩, 朱天曈, 等. 基于区间二型模糊多属性决策方法的大规模储能选型分析[J]. 高电压技术, 2021, 47(11): 4123-4136. DOI: 10.13336/j.1003-6520.hve.20201110.
	FENG X C, ZHANG S Y, ZHU T T, et al. Large-scale energy storage selection analysis based on interval type-2 fuzzy multiple attribute decision making method[J]. High Voltage Engineering, 2021, 47(11): 4123-4136. DOI: 10.13336/j.1003-6520.hve. 20201110.
26	刘昊然. 计及复杂运行场景的储能系统适用性综合评价方法研究[D]. 吉林: 东北电力大学, 2021. DOI: 10.27008/d.cnki.gdbdc. 2021.000131.
	LIU H R. Research on comprehensive evaluation method of applicability of energy storage system considering complex operation scenarios[D]. Jilin: Northeast Dianli University, 2021. DOI: 10.27008/d.cnki.gdbdc.2021.000131.
27	刘洋恺, 孙伟卿, 刘唯. 基于AHP和TOPSIS-模糊综合评价的多场景储能选型方法[J]. 储能科学与技术, 2024, 13(2): 691-701. DOI: 10.19799/j.cnki.2095-4239.2023.0624.
	LIU Y K, SUN W Q, LIU W. Multiscenario energy storage selection method based on AHP and TOPSIS-fuzzy comprehensive evaluation[J]. Energy Storage Science and Technology, 2024, 13(2): 691-701. DOI: 10.19799/j.cnki.2095-4239.2023.0624.
28	文云峰, 杨伟峰, 汪荣华, 等. 构建100%可再生能源电力系统述评与展望[J]. 中国电机工程学报, 2020, 40(6): 1843-1856. DOI: 10.13334/j.0258-8013.pcsee.192031.
	WEN Y F, YANG W F, WANG R H, et al. Review and prospect of toward 100% renewable energy power systems[J]. Proceedings of the CSEE, 2020, 40(6): 1843-1856. DOI: 10.13334/j.0258-8013.pcsee.192031.
29	张智刚, 康重庆. 碳中和目标下构建新型电力系统的挑战与展望[J]. 中国电机工程学报, 2022, 42(8): 2806-2819. DOI: 10.13334/j. 0258-8013.pcsee.220467.
	ZHANG Z G, KANG C Q. Challenges and prospects for constructing the new-type power system towards a carbon neutrality future[J]. Proceedings of the CSEE, 2022, 42(8): 2806-2819. DOI: 10.13334/j.0258-8013.pcsee.220467.
30	舒印彪, 陈国平, 贺静波, 等. 构建以新能源为主体的新型电力系统框架研究[J]. 中国工程科学, 2021, 23(6): 61-69. DOI: 10.15302/J-SSCAE-2021.06.003.
	SHU Y B, CHEN G P, HE J B, et al. Building a new electric power system based on new energy sources[J]. Strategic Study of CAE, 2021, 23(6): 61-69. DOI: 10.15302/J-SSCAE-2021.06.003.
31	曾繁宏, 张俊勃. 电力系统惯性的时空特性及分析方法[J]. 中国电机工程学报, 2020, 40(1): 50-58, 373. DOI: 10.13334/j.0258-8013.pcsee.190084.
	ZENG F H, ZHANG J B. Temporal and spatial characteristics of power system inertia and its analysis method[J]. Proceedings of the CSEE, 2020, 40(1): 50-58, 373. DOI: 10.13334/j.0258-8013.pcsee.190084.
32	文云峰, 张武其, 郭威. 电力系统惯量需求: 概念、指标及评估方法[J]. 电力系统自动化, 2024, 48(8): 30-41. DOI: 10.7500/AEPS20230823001.
	WEN Y F, ZHANG W Q, GUO W. Inertia requirement of power system: Concepts, indexes, and evaluation method[J]. Automation of Electric Power Systems, 2024, 48(8): 30-41. DOI: 10.7500/AEPS20230823001.
33	孙伟卿, 尹向阳, 秦艳辉. 基于有效惯量分布的电力系统惯量不足概率评估[J]. 电力自动化设备, 2022, 42(8): 111-118. DOI: 10.16081/j.epae.202206019.
	SUN W Q, YIN X Y, QIN Y H. Probability assessment of power system inertia shortage based on available inertia distribution[J]. Electric Power Automation Equipment, 2022, 42(8): 111-118. DOI: 10.16081/j.epae.202206019.
34	张武其, 文云峰, 迟方德, 等. 电力系统惯量评估研究框架与展望[J]. 中国电机工程学报, 2021, 41(20): 6842-6856. DOI: 10.13334/j.0258-8013.pcsee.202461.
	ZHANG W Q, WEN Y F, CHI F D, et al. Research framework and prospect on power system inertia estimation[J]. Proceedings of the CSEE, 2021, 41(20): 6842-6856. DOI: 10.13334/j.0258-8013.pcsee.202461.
35	魏路平. 浙江电网机组一次调频问题的分析研究[J]. 浙江电力, 2004, 23(1): DOI: 10.19585/j.zjdl.2004.01.008.
	WEI L P. Analysis and study on the Generators'Performance of primary frequency modulation in Zhejiang power pool[J]. Zhejiang Electric Power, 2004, 23(1): DOI: 10.19585/j.zjdl.2004.01.008.
36	李瑶, 王程, 毕天姝. 考虑随机-极端扰动的新型电力系统一次调频备用优化[J/OL]. 中国电机工程学报, 2023 [2023-06-09]. https://kns.cnki.net/kcms/detail/11.2107.TM.20230608.1734.017.html.
37	郝玲, 陈磊, 黄怡涵, 等. 新型电力系统下燃煤火电机组一次调频面临的挑战与展望[J]. 电力系统自动化, 2024, 48(8): 14-29. DOI: 10.7500/AEPS20230822001.
	HAO L, CHEN L, HUANG Y H, et al. Challenges and prospects of primary frequency regulation of coal-fired thermal power units for new power system[J]. Automation of Electric Power Systems, 2024, 48(8): 14-29. DOI: 10.7500/AEPS20230822001.
38	李卫东, 常烨骙, 陈兆庆, 等. 区域控制偏差的动态内涵[J]. 电力系统自动化, 2016, 40(24): 146-150, 163.
	LI W D, CHANG Y K, CHEN Z Q, et al. Dynamic contents of area control error[J]. Automation of Electric Power Systems, 2016, 40(24): 146-150, 163.
39	宋少群, 熊嘉丽, 张伟骏, 等. 电化学储能参与调频市场的贡献评估方法[J]. 中国电力, 2023, 56(1): 87-95.
	SONG S Q, XIONG J L, ZHANG W J, et al. A method to evaluate the contribution of electrochemical energy storage participating in frequency regulation market[J]. Electric Power, 2023, 56(1): 87-95.
40	邹杰, 周占平, 邵国栋. 用于火储联合调频的多元混合储能系统应用及经济性分析[J]. 山东电力技术, 2024, 51(6): 20-26. DOI: 10.20097/j.cnki.issn1007-9904.2024.06.003.
	ZOU J, ZHOU Z P, SHAO G D. Application and economic analysis of multi-element hybrid energy storage system joint with thermal power units for frequency regulation[J]. Shandong Electric Power, 2024, 51(6): 20-26. DOI: 10.20097/j.cnki.issn1007-9904.2024.06.003.

趋势及挑战	惯量	一次调频	日内调峰
需求规模	随着新能源渗透率提升，对具有快速调节能力的资源需求上升；惯量响应、一次调频、二次调频在时间上相互耦合，规模上相互影响，应整体考虑三者之间的协同		随着新能源渗透率提升，规模将迅速增大，时长逐渐增加

提供主体	目前主要由传统同步机组提供，未来新能源、储能和负荷侧资源均可提供

提供方式	目前，惯量、一次调频由之前的机组义务提供转向“两个细则”下的补偿，二次调频已在部分省份实现市场化；未来各品种逐渐向市场化转变，义务提供和市场化采购并存		调峰与现货融合后，通过现货市场实现日内调峰

技术挑战	目前电力系统运行一般不考虑系统惯性需求；虚拟惯量可能造成设备有效使用寿命缩短^[28]	调频与调峰、保供电等多种调节需求叠加, 有必要建立机组分钟级与小时级耦合的建模方法；缺乏火电与新能源、储能不同类型调频资源之间调频功率优化分配的研究^[37]	现货市场申报、出清时间间隔缩小，节点数量增多，需要软件具备更强的计算能力

市场化挑战	现有标准无法准确地单独评价控制区域的一次与二次频率调节控制性能^[38]，单个主体调频贡献的评价与整个区域频率质量的评价脱节^[39]；是否采用市场化尚未有统一意见，目前未有惯量现货市场实践		现货电能量和调频、备用辅助服务之间的衔接机制

场景类型	技术需求	功率需求	时长需求	响应时间要求
惯量支撑	光伏+储能	几兆瓦~几十兆瓦	10 s内	几百毫秒内
惯量支撑	风电+储能	几兆瓦~几十兆瓦	10 s内	几百毫秒内

一次调频	火电+储能	几兆瓦~几十兆瓦	1 min内	2 s内
	风电+储能	风电装机容量的10%	1 min内	2 s内
	光伏+储能	光伏装机容量的10%	飞轮、超级电容：30秒~1分钟钛酸锂：5分钟磷酸铁锂：1小时以上	1 s内
	独立储能	几十兆瓦以上	1 h以上	1 s内

二次调频	火电+储能	几兆瓦~几十兆瓦	锂电：30 min~1 h	几秒~1 min
二次调频	独立储能	磷酸铁锂：(几十兆瓦以上) 磷酸铁锂+飞轮：(几十兆瓦以上+几兆瓦) 磷酸铁锂+超级电容：(几十兆瓦以上+几兆瓦)	锂电：1 h以上锂电(1 h)+飞轮(5~15 min内)	几秒内

调峰	火电+储能	几十兆瓦~百兆瓦级	几个小时	min级
调峰	独立储能	几十兆瓦以上	目前2~4小时为主	min级

调峰+二次调频	火电+储能	几十兆瓦~百兆瓦级	1小时以上	几秒~1 min
调峰+二次调频	独立储能	几十兆瓦以上	目前2~4小时为主	几秒内

调峰+一次调频	火电+储能	几十兆瓦~百兆瓦级	1小时以上	2 s内
调峰+一次调频	独立储能	几十兆瓦以上	目前2~4小时为主	1 s内

场景类别	典型配置方案	选用的储能技术	储能功率/MW	储能时长
能量型场景(独立储能调峰)	配置一	压缩空气	100	4 h
	配置二	磷酸铁锂	100	4 h
	配置三	全矾液流	100	4 h
	配置四	重力储能	100	4 h

功率型场景(火储调频)	配置五	磷酸铁锂	20	1 h
	配置六	磷酸铁锂+钛酸锂	15+5	1 h+20 min
	配置七	磷酸铁锂+超级电容	15+5	1 h+15 min
	配置八	磷酸铁锂+飞轮	15+5	1 h+15 min

综合场景(独立储能调峰和一次调频)	配置九	磷酸铁锂	100	2 h
	配置十	磷酸铁锂+飞轮	94+6	2 h+2 min
	配置十一	磷酸铁锂+超级电容	94+6	2 h+4 min

指标名称	指标类型	获取方式	说明
安全等级	定性指标	调研获取	需采用专家打分
发展阶段	定性指标	调研获取	需采用专家打分
放电倍率	定量指标(区间型)	调研获取	最优区间由具体场景需求决定
响应时间	定量指标(极小型)	调研获取	需转换成极大型
运行寿命	定量指标(极大型)	基于调研预估	混合储能由寿命长的技术决定项目周期
单位等年值成本	定量指标(极小型)	计算获取	需转换成极大型，调度关注
内部收益率	定量指标(极大型)	计算获取	运营方关注
预期市场占有率	定量指标(极大型)	基于调研预估	设备方、投资方关注
成本下降空间	定量指标(极大型)	基于调研预估	设备方、投资方、运营方、调度关注
关键资源制约	定性指标	调研获取	需采用专家打分
核心技术自主化	定性指标	调研获取	需采用专家打分

准则	准则权重	指标名称	分权重	综合权重	排名
技术指标	0.213	安全等级	0.267	0.057	6
		发展阶段	0.467	0.099	3
		放电倍率	0.083	0.018	10
		响应时间	0.050	0.011	11
		运行寿命	0.133	0.028	9
经济性指标	0.566	等年值成本	0.6	0.340	1
经济性指标	0.566	内部收益率	0.4	0.226	2
潜力指标	0.123	预期市场占有率	0.6	0.074	4
潜力指标	0.123	成本下降空间	0.4	0.049	7
可持续性指标	0.098	关键资源制约	0.3	0.029	8
可持续性指标	0.098	核心技术自主化	0.7	0.069	5