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題名 再生能源憑證市場定價分析：以台灣市場為例
Pricing Renewable Energy Certificate: Evidence from Taiwan Market ( T-REC )
作者 黃品誠
Huang, Pin-Cheng
貢獻者 林士貴<br>羅秉政
Lin, Shih-Kuei<br>Kendro Vincent
黃品誠
Huang, Pin-Cheng
關鍵詞 再生能源憑證
綠色憑證
太陽能發電
市場結清
Renewable energy certificates
Green certificates
Solar generation
Market clearing
日期 2023
上傳時間 2-Aug-2023 14:11:20 (UTC+8)
摘要 我國經由《再生能源發展條例》的修訂，正式推出「用電大戶條款」發展再生能源憑證市場，然而特殊的市場設計使得用電大戶成為潛在的再生能源憑證供給方，而能源憑證的供給又和系統發電量密不可分。因此本文的貢獻在於發展包含日照時數及模組溫度兩實質因子的發電量模型，透過季節性均數、變異數之均數復歸過程 (mean-reverting process with seasonal mean and variance，MR-SM-SV) 使模型能夠捕捉到季節性的變化，並透過與歷史發電量比較，確認模型能夠捕捉歷史趨勢且長期下優於過往文獻使用之發電量模型。進而推導出用電大戶在效用極大化條件下，配置於購買灰電、再生能源及設置再生能源系統的最適比例，並在納入灰電及再生能源生產商利潤最大化條件下，考量市場結清條件推導出再生能源憑證及能源價格間的關係式，以期能夠增加市場資訊透明度，促進市場交易活絡及合理評估發電系統價值，進而推動永續能源市場發展並接軌國際。
Under the revision of our country`s energy regulations, the " Energy-heavy Industries terms" has been officially introduced to develop the renewable energy certificate market. However, the special market design makes energy-heavy industries become potential suppliers of renewable energy certificates, and the supply of energy certificates is inextricably linked with system power generation. Therefore, the contribution of this paper is to develop a generation model that includes two real factors: sunshine hours and module temperature, and through the mean-reverting process with seasonal mean and variance (MR-SM-SV), the model will be able to capture seasonal variation and compare with historical generation to confirm that it is realistic and better than the generation models used in the past literature.
Furthermore, this study derives the optimal proportion for energy-heavy industries to allocate towards purchasing conventional electricity, renewable energy, and setting up renewable energy systems under the condition of utility maximization. Additionally, considering the maximization of profits for conventional and renewable energy producers, along with market clearing conditions, the relationship between renewable energy certificates and energy prices is derived. This aims to increase market transparency, promote active market trading, and facilitate a fair assessment of the value of power generation systems. Ultimately, these efforts aim to drive the development of the sustainable energy market and align it with international standards.
參考文獻 1. 陳膺仁（2022）。台灣再生能源憑證交易政策之探討〔未出版之碩士論文〕。國立成功大學財務金融研究所。
2. 劉偉宏（2020）。臺灣再生能源憑證於自願性市場之流動性分析〔未出版之碩士論文〕。國立政治大學行政管理碩士學程。
3. 張安興（2022）。永續能源資產定價分析：以太陽能電廠為例〔未出版之博士論文〕。國立政治大學金融學系。
4. Agliardi, E., & Agliardi, R. (2019). Financing environmentally-sustainable projects with green bonds. Environment and Development Economics, 24(6), 608-623.
5. Agliardi, E., Agliardi, R. (2021). Corporate Green Bonds: Understanding the Greenium in a Two-Factor Structural Model. Environmental and Resource Economics, 80, 257-278.
6. An, J., Kim, D.-K., Lee, J., & Joo, S.-K. (2021). Least Squares Monte Carlo Simulation-Based Decision-Making Method for Photovoltaic Investment in Korea. Sustainability, 13(19), 10613.
7. Arias-Gaviria, J., Carvajal-Quintero, S. X., & Arango-Aramburo, S. (2019). Understanding dynamics and policy for renewable energy diffusion in Colombia. Renewable Energy, 139, 1111-1119.
8. Baamonde-Seoane, M. A., Calvo-Garrido, M.-C., & Vázquez, C. (2023). Pricing renewable energy certificates with a Crank–Nicolson Lagrange–Galerkin numerical method. Journal of Computational and Applied Mathematics, 422, 114891.
9. Benth, F. E., & Šaltytė‐Benth, J. (2005). Stochastic Modelling of Temperature Variations with a View Towards Weather Derivatives. Applied Mathematical Finance, 12(1), 53-85.
10. Benth, F. E., & Šaltytė‐Benth, J. (2007). The volatility of temperature and pricing of weather derivatives. Quantitative Finance, 7(5), 553-561.
11. Branker, K., Pathak, M. J. M., & Pearce, J. M. (2011). A review of solar photovoltaic levelized cost of electricity. Renewable and Sustainable Energy Reviews, 15(9), 4470–4482.
12. Burns, J. E., & Kang, J.-S. (2012). Comparative economic analysis of supporting policies for residential solar PV in the United States: Solar Renewable Energy Credit (SREC) potential. Energy Policy, 44, 217-225.
13. Chang, Ming-Chung. (2019). The Effects of the Feed-In-Tariff System and the Renewable Energy Development Fund on Taiwan’s Power Market. Journal of Taiwan Energy, 6(4), 353-367.
14. Coulon, M., Khazaei, J., & Powell, W. B. (2015). SMART-SREC: A stochastic model of the New Jersey solar renewable energy certificate market. Journal of Environmental Economics and Management, 73, 13-31.
15. Dong, Y., & Shimada, K. (2017). Evolution from the renewable portfolio standards to feed-in tariff for the deployment of renewable energy in Japan. Renewable Energy, 107, 590-596.
16. Hulshof, D., Jepma, C., & Mulder, M. (2019). Performance of markets for European renewable energy certificates. Energy Policy, 128, 697-710.
17. Hustveit, M., Frogner, J. S., & Fleten, S.-E. (2017). Tradable green certificates for renewable support: The role of expectations and uncertainty. Energy, 141, 1717-1727.
18. Jensen, S. G., & Skytte, K. (2002). Interactions between the power and green certificate markets. Energy Policy, 30(5), 425-435.
19. Khazaei, J., Coulon, M., & Powell, W. B. (2017). ADAPT: A Price-Stabilizing Compliance Policy for Renewable Energy Certificates: The Case of SREC Markets. Operations Research, 65(6), 1429-1445.
20. Li, Y., Su, Y., & Shu, L. (2014). An ARMAX Model for Forecasting the Power Output of a Grid Connected Photovoltaic System. Renewable Energy, 66, 78-89.
21. Baamonde-Seoane, M. A., Calvo-Garrido, M. del C., Coulon, M., & Vázquez, C. (2021). Numerical solution of a nonlinear PDE model for pricing Renewable Energy Certificates (RECs). Applied Mathematics and Computation, 404, 126199.
22. Marchenko, O. V. (2008). Modeling of a green certificate market. Renewable Energy, 33(8), 1953-1958.
23. Mubiru, J. (2008). Predicting total solar irradiation values using artificial neural networks. Renewable Energy, 33(10), 2329-2332.
24. Nicolini, M., & Tavoni, M. (2017). Are renewable energy subsidies effective? Evidence from Europe. Renewable and Sustainable Energy Reviews, 74, 412-423.
25. Pillot, B., de Siqueira, S., & Dias, J. B. (2018). Grid parity analysis of distributed PV generation using Monte Carlo approach: The Brazilian case. Renewable Energy, 127, 974-988.
26. Reikard, G. (2009). Predicting solar radiation at high resolutions: A comparison of time series forecasts. Solar Energy, 83(3), 342-349.
27. Schaffer, L. M., & Bernauer, T. (2014). Explaining government choices for promoting renewable energy. Energy Policy, 68, 15-27.
28. Shrivats, A., & Jaimungal, S. (2020). Optimal Generation and Trading in Solar Renewable Energy Certificate (SREC) Markets. Applied Mathematical Finance, 27(1-2), 99-131.
29. Shrivats, A. V., Firoozi, D., Jaimungal, S. (2021). Principal agent mean field games in REC markets. arXiv, 2112.11963.
30. Shrivats, A. V., Firoozi, D., Jaimungal, S. (2022). A mean-field game approach to equilibrium pricing in solar renewable energy certificate markets. Mathematical Finance, 32(3), 779-824.
31. Wittenberg, I., & Matthies, E. (2016). Solar policy and practice in Germany: How do residential households with solar panels use electricity? Energy Research & Social Science, 21, 199-211.
32. Ying, Z., Xin-gang, Z., Xue-feng, J., & Zhen, W. (2021). Can the Renewable Portfolio Standards improve social welfare in China`s electricity market? Energy Policy, 152, 112242.
33. Zhang, H., Assereto, M., & Byrne, J. (2023). Deferring real options with solar renewable energy certificates. Global Finance Journal, 55, 100795.
描述 碩士
國立政治大學
金融學系
110352027
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0110352027
資料類型 thesis
dc.contributor.advisor 林士貴<br>羅秉政zh_TW
dc.contributor.advisor Lin, Shih-Kuei<br>Kendro Vincenten_US
dc.contributor.author (Authors) 黃品誠zh_TW
dc.contributor.author (Authors) Huang, Pin-Chengen_US
dc.creator (作者) 黃品誠zh_TW
dc.creator (作者) Huang, Pin-Chengen_US
dc.date (日期) 2023en_US
dc.date.accessioned 2-Aug-2023 14:11:20 (UTC+8)-
dc.date.available 2-Aug-2023 14:11:20 (UTC+8)-
dc.date.issued (上傳時間) 2-Aug-2023 14:11:20 (UTC+8)-
dc.identifier (Other Identifiers) G0110352027en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/146600-
dc.description (描述) 碩士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) 110352027zh_TW
dc.description.abstract (摘要) 我國經由《再生能源發展條例》的修訂，正式推出「用電大戶條款」發展再生能源憑證市場，然而特殊的市場設計使得用電大戶成為潛在的再生能源憑證供給方，而能源憑證的供給又和系統發電量密不可分。因此本文的貢獻在於發展包含日照時數及模組溫度兩實質因子的發電量模型，透過季節性均數、變異數之均數復歸過程 (mean-reverting process with seasonal mean and variance，MR-SM-SV) 使模型能夠捕捉到季節性的變化，並透過與歷史發電量比較，確認模型能夠捕捉歷史趨勢且長期下優於過往文獻使用之發電量模型。進而推導出用電大戶在效用極大化條件下，配置於購買灰電、再生能源及設置再生能源系統的最適比例，並在納入灰電及再生能源生產商利潤最大化條件下，考量市場結清條件推導出再生能源憑證及能源價格間的關係式，以期能夠增加市場資訊透明度，促進市場交易活絡及合理評估發電系統價值，進而推動永續能源市場發展並接軌國際。zh_TW
dc.description.abstract (摘要) Under the revision of our country`s energy regulations, the " Energy-heavy Industries terms" has been officially introduced to develop the renewable energy certificate market. However, the special market design makes energy-heavy industries become potential suppliers of renewable energy certificates, and the supply of energy certificates is inextricably linked with system power generation. Therefore, the contribution of this paper is to develop a generation model that includes two real factors: sunshine hours and module temperature, and through the mean-reverting process with seasonal mean and variance (MR-SM-SV), the model will be able to capture seasonal variation and compare with historical generation to confirm that it is realistic and better than the generation models used in the past literature.
Furthermore, this study derives the optimal proportion for energy-heavy industries to allocate towards purchasing conventional electricity, renewable energy, and setting up renewable energy systems under the condition of utility maximization. Additionally, considering the maximization of profits for conventional and renewable energy producers, along with market clearing conditions, the relationship between renewable energy certificates and energy prices is derived. This aims to increase market transparency, promote active market trading, and facilitate a fair assessment of the value of power generation systems. Ultimately, these efforts aim to drive the development of the sustainable energy market and align it with international standards.		en_US
dc.description.tableofcontents 第一章 緒論 1
1.1 研究背景 1
1.2 研究動機與目的 3
1.3 研究架構 5
第二章 文獻回顧 7
2.1 再生能源政策發展 7
2.2 再生能源憑證定價模型 9
第三章 模型設計 13
3.1 用電大戶效用極大化模型 13
3.2 傳統發電系統模型 14
3.3 日照時數及模組溫度發電量模型 15
3.3.1 日照時數 16
3.3.2 模組溫度 17
3.4 市場均衡求解 18
3.4.1 消費者（用電大戶） 19
3.4.2 傳統灰電發電商 20
3.4.3 再生能源發電商 21
3.4.4 灰電及再生能源憑證價格關係 22
3.4.5 能源市場均衡 23
第四章 實證分析 27
4.1 樣本說明 27
4.1.1 資料選取說明 27
4.1.2 敘述統計量 27
4.2 參數估計 28
4.2.1 日照時數模型 29
4.2.2 模組溫度模型 30
4.3 模擬結果 31
4.4 敏感度分析 32
4.4.1 日照時數 32
4.4.2 模組溫度 33
第五章 結論 34
REFERENCE 36
圖 41
表 51		zh_TW
dc.format.extent 3911430 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0110352027en_US
dc.subject (關鍵詞) 再生能源憑證zh_TW
dc.subject (關鍵詞) 綠色憑證zh_TW
dc.subject (關鍵詞) 太陽能發電zh_TW
dc.subject (關鍵詞) 市場結清zh_TW
dc.subject (關鍵詞) Renewable energy certificatesen_US
dc.subject (關鍵詞) Green certificatesen_US
dc.subject (關鍵詞) Solar generationen_US
dc.subject (關鍵詞) Market clearingen_US
dc.title (題名) 再生能源憑證市場定價分析：以台灣市場為例zh_TW
dc.title (題名) Pricing Renewable Energy Certificate: Evidence from Taiwan Market ( T-REC )en_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 1. 陳膺仁（2022）。台灣再生能源憑證交易政策之探討〔未出版之碩士論文〕。國立成功大學財務金融研究所。
2. 劉偉宏（2020）。臺灣再生能源憑證於自願性市場之流動性分析〔未出版之碩士論文〕。國立政治大學行政管理碩士學程。
3. 張安興（2022）。永續能源資產定價分析：以太陽能電廠為例〔未出版之博士論文〕。國立政治大學金融學系。
4. Agliardi, E., & Agliardi, R. (2019). Financing environmentally-sustainable projects with green bonds. Environment and Development Economics, 24(6), 608-623.
5. Agliardi, E., Agliardi, R. (2021). Corporate Green Bonds: Understanding the Greenium in a Two-Factor Structural Model. Environmental and Resource Economics, 80, 257-278.
6. An, J., Kim, D.-K., Lee, J., & Joo, S.-K. (2021). Least Squares Monte Carlo Simulation-Based Decision-Making Method for Photovoltaic Investment in Korea. Sustainability, 13(19), 10613.
7. Arias-Gaviria, J., Carvajal-Quintero, S. X., & Arango-Aramburo, S. (2019). Understanding dynamics and policy for renewable energy diffusion in Colombia. Renewable Energy, 139, 1111-1119.
8. Baamonde-Seoane, M. A., Calvo-Garrido, M.-C., & Vázquez, C. (2023). Pricing renewable energy certificates with a Crank–Nicolson Lagrange–Galerkin numerical method. Journal of Computational and Applied Mathematics, 422, 114891.
9. Benth, F. E., & Šaltytė‐Benth, J. (2005). Stochastic Modelling of Temperature Variations with a View Towards Weather Derivatives. Applied Mathematical Finance, 12(1), 53-85.
10. Benth, F. E., & Šaltytė‐Benth, J. (2007). The volatility of temperature and pricing of weather derivatives. Quantitative Finance, 7(5), 553-561.
11. Branker, K., Pathak, M. J. M., & Pearce, J. M. (2011). A review of solar photovoltaic levelized cost of electricity. Renewable and Sustainable Energy Reviews, 15(9), 4470–4482.
12. Burns, J. E., & Kang, J.-S. (2012). Comparative economic analysis of supporting policies for residential solar PV in the United States: Solar Renewable Energy Credit (SREC) potential. Energy Policy, 44, 217-225.
13. Chang, Ming-Chung. (2019). The Effects of the Feed-In-Tariff System and the Renewable Energy Development Fund on Taiwan’s Power Market. Journal of Taiwan Energy, 6(4), 353-367.
14. Coulon, M., Khazaei, J., & Powell, W. B. (2015). SMART-SREC: A stochastic model of the New Jersey solar renewable energy certificate market. Journal of Environmental Economics and Management, 73, 13-31.
15. Dong, Y., & Shimada, K. (2017). Evolution from the renewable portfolio standards to feed-in tariff for the deployment of renewable energy in Japan. Renewable Energy, 107, 590-596.
16. Hulshof, D., Jepma, C., & Mulder, M. (2019). Performance of markets for European renewable energy certificates. Energy Policy, 128, 697-710.
17. Hustveit, M., Frogner, J. S., & Fleten, S.-E. (2017). Tradable green certificates for renewable support: The role of expectations and uncertainty. Energy, 141, 1717-1727.
18. Jensen, S. G., & Skytte, K. (2002). Interactions between the power and green certificate markets. Energy Policy, 30(5), 425-435.
19. Khazaei, J., Coulon, M., & Powell, W. B. (2017). ADAPT: A Price-Stabilizing Compliance Policy for Renewable Energy Certificates: The Case of SREC Markets. Operations Research, 65(6), 1429-1445.
20. Li, Y., Su, Y., & Shu, L. (2014). An ARMAX Model for Forecasting the Power Output of a Grid Connected Photovoltaic System. Renewable Energy, 66, 78-89.
21. Baamonde-Seoane, M. A., Calvo-Garrido, M. del C., Coulon, M., & Vázquez, C. (2021). Numerical solution of a nonlinear PDE model for pricing Renewable Energy Certificates (RECs). Applied Mathematics and Computation, 404, 126199.
22. Marchenko, O. V. (2008). Modeling of a green certificate market. Renewable Energy, 33(8), 1953-1958.
23. Mubiru, J. (2008). Predicting total solar irradiation values using artificial neural networks. Renewable Energy, 33(10), 2329-2332.
24. Nicolini, M., & Tavoni, M. (2017). Are renewable energy subsidies effective? Evidence from Europe. Renewable and Sustainable Energy Reviews, 74, 412-423.
25. Pillot, B., de Siqueira, S., & Dias, J. B. (2018). Grid parity analysis of distributed PV generation using Monte Carlo approach: The Brazilian case. Renewable Energy, 127, 974-988.
26. Reikard, G. (2009). Predicting solar radiation at high resolutions: A comparison of time series forecasts. Solar Energy, 83(3), 342-349.
27. Schaffer, L. M., & Bernauer, T. (2014). Explaining government choices for promoting renewable energy. Energy Policy, 68, 15-27.
28. Shrivats, A., & Jaimungal, S. (2020). Optimal Generation and Trading in Solar Renewable Energy Certificate (SREC) Markets. Applied Mathematical Finance, 27(1-2), 99-131.
29. Shrivats, A. V., Firoozi, D., Jaimungal, S. (2021). Principal agent mean field games in REC markets. arXiv, 2112.11963.
30. Shrivats, A. V., Firoozi, D., Jaimungal, S. (2022). A mean-field game approach to equilibrium pricing in solar renewable energy certificate markets. Mathematical Finance, 32(3), 779-824.
31. Wittenberg, I., & Matthies, E. (2016). Solar policy and practice in Germany: How do residential households with solar panels use electricity? Energy Research & Social Science, 21, 199-211.
32. Ying, Z., Xin-gang, Z., Xue-feng, J., & Zhen, W. (2021). Can the Renewable Portfolio Standards improve social welfare in China`s electricity market? Energy Policy, 152, 112242.
33. Zhang, H., Assereto, M., & Byrne, J. (2023). Deferring real options with solar renewable energy certificates. Global Finance Journal, 55, 100795.		zh_TW