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Title: | 永續能源資產定價分析:以太陽能電廠為例 The Analysis of Assets Pricing for Sustainable Energy : The Case of Solar Photovoltaic Plant |
Authors: | 張安興 Chang, An-Hsing |
Contributors: | 林士貴 Lin, Shi-Gui 張安興 Chang, An-Hsing |
Keywords: | 太陽能電廠 均數復歸 日照時數 Solar power plant Peak sunshine hours Mean-reverting process |
Date: | 2023 |
Issue Date: | 2023-03-09 18:35:21 (UTC+8) |
Abstract: | 本文以太陽能電廠爲例,探討永續能源資產之定價與相關財務金融問題。首先,本研究使用均數復歸過程刻畫電廠日照時數,並透過傅立葉級數描繪其季節性特徵,同時將模組溫度對發電系統之影響納入考量,建立合理時間序列模型以有效模擬案址於殘存合約期間之可能日照時數。其次,本文結合風險貼水概念探討電廠應投入資金成本,結合案場設備串聯隻基本衰退率、利率及匯率評定太陽能電廠之資產價格,並評估電廠投資可行性與效益分析。最後更進一步將評價得到之結果與實際電廠分割化模式之市售價格比較,實證結果表明,本研究之評價結果貼近終端電廠購買之投資者的願購價格,可供未來相關學術研究之延伸發展、投資者與相關單位之實務操作提供參考與借鑒。 By taking the solar power plant as an example, this thesis discusses about the pricing of sustainable energy asset and related financial issues. First of all, I model the peak sunshine hours (PSH) as following the mean-reverting process and describe the seasonal characteristic by Fourier series. To simulate the possible sunshine hours during the remaining contract period, I establish a time series model with the influence of module temperature considered. Secondly, by discussing about the risk premium and the capital cost of the power plant, I evaluate the investment feasibility and draw benefit analysis of the plant with considerations of equipment decline rate, interest rate, and the exchange rate. Finally, I further compare the pricing results with the market price of the power plant segments. The empirical results show that the simulated prices are close to the willing purchase prices from the investors of the plants, which indicates that this thesis can provide reference for further academic researches and investors in practical operations. |
Reference: | 1.Alaton, P., Djehince, B., and Stillberg, D., (2002), On Modelling and Pricing Weather Derivatives. Applied Mathematical Finance, 9(1), 1-20. 2.Benth, F.E., and Šaltytė-Benth, J., (2005), Stochastic Modelling of Temperature Variations with a View Towards Weather Derivatives. Applied Mathematical Finance, 12(1), 53-85. 3.Benth, F.E., and Šaltytė-Benth, J., (2007), The Volatility of Temperature and Pricing of Weather Derivatives. Quantitative Finance, 7(5), 553-561. 4.Bibby, B. M. and Srensen, M., (1995), Martingale Estimation Functions for Discretely Observed Diffusion Processes, Bernoulli, 1(1/2), 17-39. 5.Black, F. and Scholes, M., (1973), The Pricing of Options and Corporate Liabilities, Journal of Political Economy, 81(3), 637-654. 6.Branker, K., Pathak, M.J.M., and Pearce, J.M., (2011), A Review of Solar Photovoltaic Levelized Cost of Electricity, Renewable and Sustainable Energy Reviews, 1364(1), 4470-4482. 7.Brody, D.C., Syroka, J., and Zervos, M., (2002), Dynamical Pricing of Weather Derivatives. Quantitative Finance, 2(3), 189-198. 8.Cao, M. and Wei, J., (2004), Weather Derivatives Valuation and Market Price of Weather Risk. Journal of Futures Markers, 24(11), 1065-1089. 9.Campbell, S.D. and Diebold, F.X., (2005), Weather Forecasting for Weather Derivatives. Journal of the American Statistical Association, 100(469), 6-16. 10.David, M. and Lauret, P., (2018), Solar Radiation Probabilistic Forecasting. In Wind Field and Solar Radiation Characterization and Forecasting A Numerical Approach for Complex Terrain, Springer: Berlin/Heidelberg, Germany. 11.Diebold, F.X. and Li, C., (2006), Forecasting the Term Structure of Government Bond Yields. Journal of Econometrics, 130(2), 337-364. 12.Huang, R., Huang, T., Gadh, R., and Li, N., (2012), Solar Generation Prediction Using the ARMA Model in a Laboratory-Level Micro-grid. 2012 IEEE Third International Conference on Smart Grid Communications, 528-533. 13.Hull, J. and White, A., (1990), Pricing Interest Rate Derivative Securities, Review of Financial Studies, 3(4), 573-592. 14.Tao, H., Ebtehaj, I., Bonakdari, H., Heddam, S., Voyant, C., Al-Ansari, N., ... and Yaseen, Z. M. (2019). Designing a new data intelligence model for global solar radiation prediction: application of multivariate modeling scheme. Energies, 12(7), 1365. 15.Li, Y., Su, Y., and Shu, L., (2014), An ARMAX Model for Forecasting the Power Output of a Grid Connected Photovoltaic System. Renewable Energy, 66, 78-89. 16.Mellit, A., and Pavan, A.M., (2010), A 24-h Forecast of Solar Irradiance Using Artificial Neural Network: Application for Performance Prediction of a Grid-connected PV Plant at Trieste. Solar Energy, 84(5), 807-821. 17.Pillot, B., Siqueira, S., and Dias, J. B., (2018), Grid Parity Analysis of Distributed PV Generation Using Monte Carlo Approach: The Brazilian Case, Renewable Energy, 127, 974-988. 18.Reikard, G., (2009), Predicting Solar Radiation at High Resolutions: A Comparison of Time Series Forecasts. Solar Energy, 83(3), 342-349. 19.Bakhshi-Jafarabadi, R., Sadeh, J., & Dehghan, M. (2020). Economic evaluation of commercial grid-connected photovoltaic systems in the Middle East based on experimental data: A case study in Iran. Sustainable energy technologies and assessments, 37, 100581. 20.Saadi, N., Miketa, A., and Howells, M., (2015), African Clean Energy Corridor: Regional Integration to Promote Renewable Energy Fueled Growth, Energy Research & Social Science, 5, 130–132. 21.Chukwujindu, N. S. (2017). A comprehensive review of empirical models for estimating global solar radiation in Africa. Renewable and Sustainable Energy Reviews, 78, 955-995. 22.Vasicek, O. A., (1977), An Equilibrium Characterization of the Term Structure, Journal of Financial Economics, 5(2), 177-188. 23.Zografidou, E., Petridis, K., Petridis, N.E., and Arabatzis, G., (2017), A Financial Approach to Renewable Energy Production in Greece Using Goal Programming, Renewable Energy, 108, 37-51. 24.台灣電力公司 (2018),台灣電力股份有限公司再生能源發電系統併聯技術要點。2022/9/27,取自https://www.taipower.com.tw/tc/download.aspx?mid= 228&cid= 480&cchk=bf53eb5f-1a45-4271-8d02-df8f0f93292b 25.行政院經濟部 (2019),再生能源發展條例。2022/9/12,取自https://www.moeaboe.gov.tw/ECW/populace/Law/LawsList.aspx?kind=6&menu_id=3302 26.蕭國鑫 (2017) ,再生能源之相關成本分析 - 太陽光電與風力發電成本將持續下降。2022/9/18,取自https://km.twenergy.org.tw/Document/reference_more?id=142 27.勤業眾信 (2018),IFRS 13公允價值衡量。2022/9/19,取自chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://www2.deloitte.com/content/dam/Deloitte/tw/Documents/audit/IFRS/tw-standards-IFRS13.pdf 28.林明村 (2013), 建置太陽光電發電系統結合銀行融資之投資效益研究, 中華大學營建管理學系碩士學位論文,未出版,新竹。 29.經濟部能源局 (2006) 經濟部五年內將提高綠色能源產業產值至1,610億,財訊出版社。台北:財訊出版社股份有限公司。 30.熊佳苓 (2016), 考慮營運風險下分散式太陽能電站投資評估模型之模擬研究-以A公司為例, 逢甲大學科技管理研究所碩士學位論文,未出版,台中。 |
Description: | 博士 國立政治大學 金融學系 100352506 |
Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0100352506 |
Data Type: | thesis |
Appears in Collections: | [金融學系] 學位論文
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