基于过程划分的可再生能源技术扩散模型

doi:10.16381/j.cnki.issn1003-207x.2019.0307

中国管理科学 ›› 2022, Vol. 30 ›› Issue (2): 217-225.doi: 10.16381/j.cnki.issn1003-207x.2019.0307

• 论文 • 上一篇

基于过程划分的可再生能源技术扩散模型

周德群^1,2, 丁浩^1,2, 周鹏³, 王群伟^1,2

1.南京航空航天大学经济与管理学院，南京211106；2.南京航空航天大学能源软科学研究中心，南京211106；3.中国石油大学华东经济管理学院，青岛266555

收稿日期:2019-03-08 修回日期:2020-12-29 发布日期:2022-03-02
通讯作者: February,2022 E-mail:dding2009@nuaa.edu.cn
基金资助:
丁浩

Renewable Energy Technology Diffusion Model Based on Process Division

ZHOU De-qun^1,2, DING Hao^1,2, ZHOU Peng³, WANG Qun-wei^1,2

1. College of Economics and Management Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China;2. Research Center for Soft Energy Science Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China;3. School of Economics and Management China University of Petroleum, Qingdao 266580, China

Received:2019-03-08 Revised:2020-12-29 Published:2022-03-02
Contact: 丁浩(1991-)，男(汉族)，江苏盐城人，南京航空航天大学经济与管理学院，博士，研究方向：可再生能源发展管理，Email: dding2009@nuaa.edu.cn. E-mail:dding2009@nuaa.edu.cn
Supported by:
国家自然科学基金资助项目(71834003，71573121)；江苏省研究生科研实践创新基金资助项目(KYZZ16_0158)

摘要/Abstract

摘要： 把握技术扩散的机理和主要特征是可再生能源发展规划和管理优化的基础。本文基于信息扩散、技术经济、社会认可度等因素对可再生能源技术的影响及其作用机理分析，根据投资者状态变化，将技术扩散过程划分为技术获悉、效益计算和技术认可三个阶段，构建了可再生能源技术扩散(即潜在投资者转变为最终投资者)过程的数理模型。探讨了可再生能源技术扩散的速度有限性、政策效率递减性以及区域差异性三个关键特征，结合算例验证模型并进一步分析了技术扩散的不同驱动力，从而为可再生能源技术扩散机理政策制定提供理论支持和意见建议。

关键词: 可再生能源；技术扩散；系统建模；能源转型

Abstract: Identifying the mechanism and characteristics of renewable energy technology (RET) diffusion is the basis for the planning and management of RET development. Based on the influences of information dissemination, technology economy, as well as social acceptance etc. on RET diffusion, RET diffusion process is divided into Technology Learned, Benefit Calculation, and Technology acceptance in accordance to the situation’s changes of investors. Then a mathematical model is constructed to present the RET diffusion process, which reflects how potential investors transform to final investors. Then the characteristics of RET diffusion are studied, including the limitation of diffusion rate, the decline of policy efficiency, and the differences among sectors. Different drivers of RET diffusion are discussed through a case study. Based on this research, theoretical supports and policy implications are provided for promoting the diffusion or RETs.

Key words: renewable energy; technology diffusion; system modeling; energy transition

中图分类号:

周德群, 丁浩, 周鹏, 王群伟. 基于过程划分的可再生能源技术扩散模型[J]. 中国管理科学, 2022, 30(2): 217-225.

ZHOU De-qun, DING Hao, ZHOU Peng, WANG Qun-wei. Renewable Energy Technology Diffusion Model Based on Process Division[J]. Chinese Journal of Management Science, 2022, 30(2): 217-225.

参考文献

［1］ Popp D, Hascic I, Medhi N. Technology and the diffusion of renewable energy［J］. Energy Economics, 2011, 33(4): 648-662.
［2］ Strantzali E, Aravossis K. Decision making in renewable energy investments: A review［J］. Renewable & Sustainable Energy Reviews, 2016, 55: 885-898.
［3］ Strupeit L, Palm A. Overcoming barriers to renewable energy diffusion: business models for customer-sited solar photovoltaics in Japan, Germany and the United States［J］. Journal of Cleaner Production, 2016, 123: 124-136.
［4］ Matteson S, Williams, E. Residual learning rates in lead-acid batteries: Effects on emerging technologies［J］. Energy Policy, 2015, 85: 71-79.
［5］ Kumar R, Agarwala A. Renewable energy technology diffusion model for techno-economics feasibility［J］. Renewable & Sustainable Energy Reviews, 2016, 54: 1515-1524.
［6］ Mcdonald A, Schrattenholzer, L. Learning rates for energy technologies［J］. Energy Policy,2001, 29(4): 255-261.
［7］ Junginger M, Faaij A, Turkenburg W C. Global experience curves for wind farms［J］. Energy Policy, 2005, 33(2): 133-150.
［8］ Nemet G F. Beyond the learning curve: factors influencing cost reductions in photovoltaics［J］. Energy Policy, 2006, 34(17): 3218-3232.
［9］邵云飞, 谭劲松. 区域技术创新能力形成机理探析［J］. 管理科学学报, 2006, 9(4): 1-11.Shao Yunfei, Tan Jinsong. Research on mechanism and forcapacity ofregionaltechnologicalinnovation［J］. Journal of Management Science in China, 2006, 34(17): 3218-3232.
［10］ Jimenez M, Franco C J, Dyner I. Diffusion of renewable energy technologies: The need for policy in Colombia［J］. Energy, 2016, 111: 818-829.
［11］ Jacobsson S, Lauber V. The politics and policy of energy system transformation—explaining the German diffusion of renewable energy technology［J］. Energy Policy, 2006, 34(3): 256-276.
［12］ Boie I. Determinants for the market diffusion of renewable energy technologies: An analysis of the framework conditions for non-residential photovoltaic and onshore wind energy deployment in Germany, Spain and the UK［D］. Devon: University of Exeter, 2016.
［13］张国兴,张绪涛,汪应洛,等. 节能减排政府补贴的最优边界问题研究［J］. 管理科学学报, 2014, 17(11): 129-138.Zhang Guoxing, Zhang Xutao, Wang Yingluo, et al. Analysis of optimal boundary of government subsidies for energy conservation and emission reduction［J］. Journal of Management Science in China, 2014, 17(11): 129-138.
［14］钟渝,刘名武, 马永开. 基于实物期权的光伏并网发电项目成本补偿策略研究［J］. 中国管理科学, 2010, 18(3): 68-74.Zhong Yu, Liu Mingwu, Ma Yongkai. Research on cost-recovery policy for grid-connected PV project based on the real option［J］. Chinese Journal of Management Science, 2010, 18(3): 68-74.
［15］李力, 朱磊, 范英.不确定条件下可再生能源项目的竞争性投资决策［J］.中国管理科学, 2017, 25(7): 11-17.Li Li, Zhu Lei, Fan Ying. Competitive investment strategy for renewable power generation under uncertainty［J］. Chinese Journal of Management Science, 2017, 25(7): 11-17.
［16］李庆, 赵新泉, 葛翔宇. 政策不确定性对可再生能源电力投资影响研究——基于实物期权理论证明与分析［J］.中国管理科学, 2015, 23(S1): 445-452.Li Qing, Zhao Xinquan, Ge Xiangyu. Study of real option decisions under the effects of policy uncertainty on investment of renewable energy electricity［J］. Chinese Journal of Management Science, 2015, 23(S1): 445-452.
［17］ Bass F M. A new product growth for model consumer durables［J］. Management Science, 1969, 15(5): 215-227.
［18］ Mahajan V, Muller E, Bass F M. New product diffusion models in marketing: A review and directions for research［M］. Springer Berlin Heidelberg. 1991.
［19］ Geroski P. Models of technology diffusion［J］. Research Policy, 2000, 29: 603-625.
［20］ Rao K U, Kishore V V N. A review of technology diffusion models with special reference to renewable energy technologies［J］. Renewable & Sustainable Energy Reviews, 2010, 14(3): 1070-1078.
［21］ Alizamir S, Véricourt F D, Sun P. Efficient feed-in-tariff policies for renewable energy technologies［J］. Operations Research, 2016, 64(1): 52-66.
［22］赵保国，余宙婷. 基于人际关系视角的自助服务扩散研究［J］. 管理科学学报, 2016, 19(10): 101-116.Zhao Baoguo, Yu Zhouting. Self-service diffusion from the perspective of interpersonal relationship［J］. Journal of Management Sciences in China, 2016, 19(10): 101-116.
［23］ Bollinger B,Gillingham K. Peer effects in the diffusion of solar photovoltaic panels［J］. Marketing Science, 2012, 31(6): 900-912.
［24］ Viklund M. Energy policy options—from the perspective of public attitudes and risk perceptions［J］. Energy Policy, 2004, 32(10): 1159-1171.
［25］ Islam T. Household level innovation diffusion model of photo-voltaic (PV) solar cells from stated preference data［J］. Energy Policy, 2014, 65(65): 340-350.
［26］ Kardooni R, Yusoff S B, Kari F B. Renewable energy technology acceptance in Peninsular Malaysia［J］. Energy Policy, 2016, 88: 1-10.
［27］ Batley S L. Citizen versus consumer: Challenges in the UK green power market［J］. Energy Policy, 2001, 29(6): 479-487.
［28］ Jager W. Stimulating the diffusion of photovoltaic systems: A behavioural perspective［J］. Energy Policy, 2006, 34(14): 1935-1943.
［29］ Chu S, Majumdar A. Opportunities and challenges for a sustainable energy future［J］. Nature, 2012, 488(7411): 294.
［30］ Rubin E S, Azevedo I N L, Jaramillo P, et al. A review of learning rates for electricity supply technologies［J］. Energy Policy, 2015, 86: 198-218.
［31］ Yu C F, van Sark W G J H M, Alsema E A. Unraveling the photovoltaic technology learning curve by incorporation of input price changes and scale effects［J］. Renewable & Sustainable Energy Reviews, 2011, 15: 324-337.
［32］ Ghosh A, Ganesan K. Policy: Rethink India’s energy strategy［J］. Nature, 2015, 521: 156-157.
［33］ Li H, Jenkins-Smith H C, Silva C L, et al. Public support for reducing US reliance on fossil fuels: Investigating household willingness-to-pay for energy research and development［J］. Ecological Economics, 2009, 68(3): 731-742.
［34］ Mozumder P, Vásquez W F, Marathe A. consumers’ preference for renewable energy in the southwest USA［J］. Energy Economics, 2011, 33(6): 1119-1126.
［35］ Wilkerson J T, Cullenward D, Davidian D, et al.End use technology choice in the national energy modeling system (NEMS): An analysis of the residential and commercial building sectors［J］. Energy Economics, 2013, 40: 773-784.
［36］ Maslin M, Scott J. Carbon trading needs a multi-level approach［J］. Nature, 2011, 475(7357): 445-447.
［37］ Kopetz H. Renewable resources: Build a biomass energy market［J］. Nature, 2013,494(7435): 29.
［38］ Millstein D, Wiser R, Bolinger M,et al. The climate and air-quality benefits of wind and solar power in the United States［J］. Nature Energy, 2017, 2(9): nenergy2017134.
［39］马翠萍, 史丹, 丛晓男.太阳能光伏发电成本及平价上网问题研究［J］. 当代经济科学, 2014, 36(2): 85-94.Ma Cuiping, Shi Dan, Cong Xiaonan. The research on the generation cost of solar photovoltaic electricity and a pathway to grid parity［J］. Modern Economic Science, 2014, 36(2): 85-94.
［40］陈荣荣, 孙韵琳, 陈思铭,等. 并网光伏发电项目的LCOE分析［J］. 可再生能源, 2015, 33(5): 731-735.Chen Rongrong, Sun Yunlin, Chen Siming, et al. LCOE analysis of grid-connected photovoltaic power generation project［J］. Renewable Energy Resources, 2015, 33(5): 731-735.
［41］ Lai C S, Mcculloch M D, Yan J. Levelized cost of electricity for solar photovoltaic and electrical energy storage［J］. Applied Energy, 2017, 190: 191-203.
［42］ Ouyang X, Lin B. Levelized cost of electricity (LCOE) of renewable energies and required subsidies in China［J］. Energy Policy, 2014, 70(7): 64-73.

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基于过程划分的可再生能源技术扩散模型

Renewable Energy Technology Diffusion Model Based on Process Division

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