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題名 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
作者 張安興
Chang, An-Hsing
貢獻者 林士貴
Lin, Shi-Gui
張安興
Chang, An-Hsing
關鍵詞 太陽能電廠
均數復歸
日照時數
Solar power plant
Peak sunshine hours
Mean-reverting process
日期 2023
上傳時間 9-Mar-2023 18:35:21 (UTC+8)
摘要 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.
參考文獻 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。
描述 博士
國立政治大學
金融學系
100352506
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0100352506
資料類型 thesis
dc.contributor.advisor 林士貴zh_TW
dc.contributor.advisor Lin, Shi-Guien_US
dc.contributor.author (Authors) 張安興zh_TW
dc.contributor.author (Authors) Chang, An-Hsingen_US
dc.creator (作者) 張安興zh_TW
dc.creator (作者) Chang, An-Hsingen_US
dc.date (日期) 2023en_US
dc.date.accessioned 9-Mar-2023 18:35:21 (UTC+8)-
dc.date.available 9-Mar-2023 18:35:21 (UTC+8)-
dc.date.issued (上傳時間) 9-Mar-2023 18:35:21 (UTC+8)-
dc.identifier (Other Identifiers) G0100352506en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/143827-
dc.description (描述) 博士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 金融學系zh_TW
dc.description (描述) 100352506zh_TW
dc.description.abstract (摘要) 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。zh_TW
dc.description.abstract (摘要) 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.en_US
dc.description.tableofcontents 第一章 緒論 1
第一節 研究動機 1
第二節 研究目的 6
第三節 研究架構 10
第二章 文獻回顧 12
第一節 太陽日照幅度與溫度模型 13
第二節 溫度相關衍生性商品之評價 19
第三節 太陽能電廠收益預估 20
第三章 研究方法 25
第一節、等效日照時數模型 25
一、自我迴歸(AR)模型 25
二、移動平均模型(MA)模型 28
三、自我迴歸移動平均(ARMA)模型 29
四、具季節性均數、變異數之均數復歸過程(MR-SM-SV)模型 32
第二節、組串發電量及價格模擬過程 35
一、串列發電量估算 35
二、短期利率、即期匯率之動態過程 36
第四章 實證分析 38
第一節、樣本說明 38
一、資料選取說明 38
二、敘述統計量說明 40
第二節、參數估計 43
一、日照模型參數估計 43
二、利率與匯率模型參數估計 53
三、風險貼水 56
第三節、評價結果 57
第四節、敏感度分析 61
一、風險貼水敏感度分析 61
二、日照平均數及變異數對於電廠價格敏感度分析 62
三、模組溫度平均數及變異數季節性對於電廠價格敏感度分析 65
第五章 結論 67
參考文獻 70		zh_TW
dc.format.extent 1519284 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0100352506en_US
dc.subject (關鍵詞) 太陽能電廠zh_TW
dc.subject (關鍵詞) 均數復歸zh_TW
dc.subject (關鍵詞) 日照時數zh_TW
dc.subject (關鍵詞) Solar power planten_US
dc.subject (關鍵詞) Peak sunshine hoursen_US
dc.subject (關鍵詞) Mean-reverting processen_US
dc.title (題名) 永續能源資產定價分析：以太陽能電廠為例zh_TW
dc.title (題名) The Analysis of Assets Pricing for Sustainable Energy : The Case of Solar Photovoltaic Planten_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 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。		zh_TW