主管:中国科学院
主办:中国优选法统筹法与经济数学研究会
   中国科学院科技战略咨询研究院
论文

不确定条件下可再能源项目的竞争性投资决策

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  • 1. 中国科学院科技战略咨询研究院能源与环境政策研究中心, 北京 100190;
    2. 天津师范大学管理学院, 天津 300387;
    3. 北京航空航天大学经济与管理学院, 北京 100190

收稿日期: 2016-10-08

  修回日期: 2017-02-15

  网络出版日期: 2017-09-25

基金资助

国家自然科学基金资助项目(71273253)

Competitive Investment Strategy for Renewable Power Generation Under Uncertainty

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  • 1. Center for Energy and Environment Policy Research, Institute of Policy and Management, Chinese Academy of Sciences, Beijing 100190, China;
    2. Tianjin Normal University, School of Management, Tianjin 300387, China;
    3. Beihang University, School of economics and management, Beijing 100190, China

Received date: 2016-10-08

  Revised date: 2017-02-15

  Online published: 2017-09-25

摘要

固定上网电价政策是促进可再生能源部署的重要政策手段。固定上网电价政策下,政府以市场电价和固定上网电价之和收购绿色电力。基于固定上网电价政策,本文建立了实物期权框架下的多主体完全抢滩博弈模型,以刻画投资者在竞争可再生能源项目时的投资决策。模型中,我们使用几何布朗运动刻画了市场电价的动态演化,同时考虑到了关于竞争对手投资时间的不完全信息对于投资决策的影响。理论方面,对手们的抢滩使得投资者的投资触发较垄断市场提早,而投资风险又使得其大于净现值原则的投资触发。数值分析显示独立于市场电价的固定上网电价越高,投资时间越早。本文的模型,可以帮助投资者在参与可再生能源项目投资时确定投资触发,同时政府可以依据投资者的反馈,制定合理的补贴价格。

本文引用格式

李力, 朱磊, 范英 . 不确定条件下可再能源项目的竞争性投资决策[J]. 中国管理科学, 2017 , 25(7) : 11 -17 . DOI: 10.16381/j.cnki.issn1003-207x.2017.07.002

Abstract

In this paper, complete preemption game model based on the real option theory is provided to analyze the optimal investment timing for investors when they compete with counterparts for the renewable energy projects under the feed in tariff policies. A geometric Brownian motion is adopted to characterize the dynamic variation of electricity spot pricing. Also, the influence of incomplete information related to rivals' investment timings on the decisions is stuaied. Theoretically, it is proved that the investment timing is within the range with upper bound of the timing for a monopolist and lower bound of the timing decided by zero-net present value. Numerical results reveal that the more FIT level and the earlier investment timing. The proposed model is able to help investors to determine the optimal timing of the feed in tariff policies when they participate in investigating, and the policy makers can draft a reasonable FIT level according to the feedback of the investors.

参考文献

[1] Seneviratne S I, Nicholls N, Easterling D,et al. Managing the risks of extreme events and disasters to advance climate change adaptation: Changes in climate extremes and their impacts on the natural physical environment [M]. Cambridge: Cambridge University Press, 2012.

[2] Gross R, Blyth W, Heptonstall P. Risks, revenues and investment in electricity generation: Why policy needs to look beyond costs [J]. Energy Economics, 2010, 32(4): 796-804.

[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] 李庆, 陈敏. 中国风电固定上网电价政策的实物期权理论与实证分析[J]. 中国管理科学, 2016, 24(5): 65-73.

[5] Sawin J L, Martinot E, Barnes D, et al. Renewables 2016-Global status report [R].Working Paper,Renewable Energy Ploicy Network for the 21st Century, 2016.

[6] Myers S C. Determinants of corporate borrowing [J]. Journal of Financial Economics, 1977, 5(2): 147-175.

[7] Ritzenhofen I, Birge J R, Spinler S. The structural impact of renewable portfolio standards and feed-in tariffs on electricity markets[J]. European Journal of Operational Research, 2016, 255(1): 224-242.

[8] Azevedo A, Paxson D. Developing real option game models [J]. European Journal of Operational Research, 2014, 237(3): 909-920.

[9] Lambrecht B, Perraudin W. Real options and preemption under incomplete information [J]. Journal of Economic Dynamics and Control, 2003, 27(4): 619-643.

[10] Weeds H. Strategic delay in a real options model of R&D competition [J]. The Review of Economic Studies, 2002, 69(3): 729-747.

[11] Graham J R, Hanlon M, Shevlin T. Real effects of accounting rules: Evidence from multinational firms' investment location and profit repatriation decisions [J]. Journal of Accounting Research, 2011, 49(1): 137-185.

[12] 朱磊, 范英, 张晓兵. 基于期权博弈的中国风电投资分析[J]. 数理统计与管理, 2010, 29(2):328-335.

[13] Alizamir S, Véricourt F D, Sun Peng. Efficient Feed-In-Tariff Policies for Renewable Energy Technologies [J]. Operations Research, 2016, 64(1): 52-66.

[14] Ritzenhofen I, Spinler S. Optimal design of feed-in-tariffs to stimulate renewable energy investments under regulatory uncertainty — A real options analysis [J]. Energy Economics, 2016, 53: 76-89.

[15] 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.

[16] International Energy Agency. World energy outlook 2015[EB/OL]. http://www.worldenergyoutlook.org/media/weowebsite/2015/WEO2015_ToC.pdf.

[17] International Renewable Energy Agency. Adapting renewable energy policies to dynamic market conditions[EB/OL].http://www.ourenergypolicy.org/adapting-renewable-energy-policies-to-dynamic-market-conditions/.

[18] Bobtcheff C, Villeneuve S. Technology choice under several uncertainty sources [J]. European Journal of Operational Research, 2010, 206(3): 586-600.

[19] Siddiqui A, Fleten S E. How to proceed with competing alternative energy technologies: A real options analysis [J]. Energy Economics, 2010, 32(4): 817-830.

[20] Kucsera D, Rammerstorfer M. Regulation and grid expansion investment with increased penetration of renewable generation [J]. Resource and Energy Economics, 2014, 37: 184-200.

[21] Harrison J M, Kreps D M. Martingales and arbitrage in multipored securities markets [J]. Journal of Economic Theory, 1979, 20(3): 381-408.

[22] International Renewable Energy Agency. Data quality for the Global Renewable Energy Atlas-Solar and Wind[EB/OL]. http://www.irena.org/DocumentDownloads/Publications/Global%20Atlas_Data%20_Quality.pdf.
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