不确定条件下固定上网电价政策的优化和评估

doi:10.13190/j.jbupt.2017.04.007

北京邮电大学学报 ›› 2017, Vol. 40 ›› Issue (4): 41-47.doi: 10.13190/j.jbupt.2017.04.007

不确定条件下固定上网电价政策的优化和评估

李力, 张昕

天津师范大学管理学院, 天津 300387

收稿日期:2017-02-13 出版日期:2017-08-28 发布日期:2017-07-10
作者简介:李力(1985-),女,研究实习员;张昕(1978-),女,副教授,E-mail:zhangxin0614@126.com.
基金资助:
天津市哲学社会科学规划一般项目（TJGL16-032）

Evaluation and Optimization of Feed-in Tariffs under Uncertainty

LI Li, ZHANG Xin

School of Management, Tianjin Normal University, Tianjin 300387, China

Received:2017-02-13 Online:2017-08-28 Published:2017-07-10

摘要/Abstract

摘要： 为准确评价固定上网电价（FIT）政策效果和优化FIT水平，提出了优化投资者决策行为的量化模型，研究了可再生能源产出的不确定性对投资者最优投资时间和装机容量的影响，同时以政策成本最小化的方式，优化政府完成预期装机部署目标时所需的FIT水平.实证方面，使用实物期权方法和最小二乘蒙特卡洛模拟，以中国近海离岸风电投资成本等参数，评价和优化了当前的FIT水平.结果表明，目前中国近海离岸风电的补贴水平0.137 USD/kWh不足以覆盖投资者产量波动带来的风险溢价，政府应将补贴水平提高至0.146 USD/kWh才可有效刺激投资.

关键词: 固定上网电价, 近海离岸风电投资, 实物期权, 不确定性

Abstract: Based on the strategic behaviors of investor, this article presents a policy evaluation model in which a specific Feed-in tariff (FIT) level can be evaluated and optimized. The influence of uncertain generation from RES on the optimal investment time and sizing (capacity choice) of investor was evaluated by using the proposed model. Also, from the view of a policy maker, the optimal tariff level was determined in the way of minimizing the total burden on ratepayers to achieve RES goals. By dint of the current tariff of offshore wind in China, empirical studies through the least squares Monte Carlo method and the real option theory assess and optimize the current level of FIT. It is revealed that the current tariff level of 0.137 USD/kWh for offshore wind in China would not be enough to offset the risk premium resulting from the output uncertainty. Improving the tariff level to 0.146 USD/kWh could take effect to stimulate investment.

Key words: feed-in tariff, offshore wind investment, real option, uncertainty

中图分类号:

F224.5

李力, 张昕. 不确定条件下固定上网电价政策的优化和评估[J]. 北京邮电大学学报, 2017, 40(4): 41-47.

LI Li, ZHANG Xin. Evaluation and Optimization of Feed-in Tariffs under Uncertainty[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2017, 40(4): 41-47.

参考文献

[1] IRENA. Renewable energy target setting[EB/OL]. Abu Dhabi:IRENA, 2015[2017-03-02]. http://www.irena.org/menu/index.aspx?mnu=Subcat&PriMenuID=36&CatID=141&SubcatID=602.
[2] Klessmann C, Rathmann M, De Jager D, et al. Policy options for reducing the costs of reaching the European renewables target[J]. Renewable Energy, 2013(57):390-403.
[3] Couture T, Gagnon Y. An analysis of feed-in tariff remuneration models:implications for renewable energy investment[J]. Energy Policy, 2010, 38(2):955-965.
[4] Haas R, Eichhammer W, Huber C, et al. How to promote renewable energy systems successfully and effectively[J]. Energy Policy, 2004, 32(6):833-839.
[5] IRENA. Adapting renewable energy policies to dynamic market conditions[EB/OL]. Abu Dhabi:IRENA, 2014[2017-03-02]. http://www.irena.org/DocumentDownloads/Publications/policy_adaptation.pdf.
[6] Alizamir S, Véricourt F D, Sun Peng. Efficient feed-in-tariff policies for renewable energy technologies[J]. Operations Research, 2016, 64(1):52-66.
[7] Kalkuhl M, Edenhofer O, Lessmann K. Learning or lock-in:optimal technology policies to support mitigation[J]. Resource and Energy Economics, 2012, 34(1):1-23.
[8] Lemming J K, Morthorst P E, Clausen N E. Offshore wind power experiences, potential and key issues for deployment[M]. Denmark:Technical University of Denmark, 2009.
[9] Harrison J M, Kreps D M. Martingales and arbitrage in multipored securities markets[J]. Journal of Economic Theory, 1979, 20(3):381-408.
[10] Takashima R, Siddiqui A S, Nakada S. Investment timing, capacity sizing, and technology choice of power plants, in handbook of networks in power systems[M]. Berlin:Springer Berlin Heidelberg, 2012:303-321.
[11] Longstaff F A, Schwartz E S. Valuing American options by simulation:a simple least-squares approach[J]. Review of Financial Studies, 2001, 14(1):113-147.
[12] Chen Shen, Insley M C. Regime switching in stochastic models of commodity prices:an application to an optimal tree harvesting problem[J]. Journal of Economic Dynamics and Control, 2012, 36(2):201-219.
[13] Boomsma T K, Meade N, Fleten S E. Renewable energy investments under different support schemes:a real options approach[J]. European Journal of Operational Research, 2012, 220(1):225-237.
[14] NREL (National Renewable Energy Laboratory). Renewable energy technologies:cost analysis series[EB/OL]. Washington DC:U. S. Department of Energy, 2016[2017-03-02]. http://www.nrel.gov/analysis/tech_lcoe_re_cost_est.html.
[15] Mancheva M. China Long yuan plugs in 4 turbines at 400-MW offshore wind park[EB/OL]. Bulgaria:Renewables Now, 2016[2017-03-02]. https://renewablesnow.com/news/china-longyuan-plugs-in-4-turbines-at-400-mw-offshore-wind-park-507775/.

不确定条件下固定上网电价政策的优化和评估

Evaluation and Optimization of Feed-in Tariffs under Uncertainty

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 6

编辑推荐

Metrics

本文评价

[1]	黄河, 袁超伟. 非同分布高斯信号背景下基于增强能量检测的协作感知[J]. 北京邮电大学学报, 2018, 41(6): 39-44.
[2]	任晓敏. 物质波动性与粒子性的科学表征及光子与姗子的相互转化[J]. 北京邮电大学学报, 2015, 38(1): 1-10.
[3]	陈明, 王树鹏, 云晓春, 吴广君. 基于二维云模型过滤的重复图像发现[J]. 北京邮电大学学报, 2014, 37(s1): 60-65.
[4]	祝希路,王柏. 支持区间型QoS的Web服务选择[J]. 北京邮电大学学报, 2011, 34(4): 80-84.
[5]	李静宋召青左斌. 鲁棒自适应控制的远程网络控制器设计[J]. 北京邮电大学学报, 2009, 32(3): 18-21.
[6]	张川 1,王柏 1,艾波 2. 分布式系统的不确定性及其对Uni-CRM测试的影响 [J]. 北京邮电大学学报, 2003, 26(S2): 96-101.