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Title:	指數型保險對再生能源風險管理之應用：以太陽能輻射發電為例 Application of Index Insurance to renewable energy risk management：A case study of Solar Power Generation
Authors:	柯廷漢 Ko, Ting-Han
Contributors:	張士傑 Chang, Shih-Chieh 柯廷漢 Ko, Ting-Han
Keywords:	太陽能發電太陽能風險管理指數型保險電量預測保費計算 Solar power generation Risk management of solar power generation Index insurance Forecasting power generation Premium calculation
Date:	2019
Issue Date:	2019-08-07 16:17:06 (UTC+8)
Abstract:	本研究介紹國內外太陽能發展與太陽能發電之相關風險管理，其中保險扮演風險管理重要角色。本文著重於指數型保險的研究，運用指數型保險之設計，為太陽能電廠受日照影響而導致發電量不足時，訂定賠付門檻(Trigger)，當觸及該門檻與保單其他條件時，指數型保險將會填補該發電量不足所導致之損失。本研究預測台灣彰濱太陽能電廠之發電量，並利用預測的太陽能發電量進行保費試算。本文以NASA的1984年至2018年位於彰濱地區之每日太陽輻射資料進行分析，模擬出彰濱太陽能發電廠之歷史發電量數據，並以時間序列模型進行發電量預測，使用前述2018年預測的太陽能每月發電量的機率分布和1984年至2017年發電量之百分位數，計算2018年每月太陽能指數型保險之純保費，利用1984年至2017年歷史發電量之百分位數設定每月賠付門檻和理賠限額，計算結果顯示年純保險費率以P50和P75為賠付門檻分別為21.82%、8.60%。 This study updates the development of solar energy in Taiwan and other countries and risk management of solar power generation, in which, insurance plays a major role in risk management. This paper applied index insurance concept to design the insurance to compensate the shortfall of power generation due to lack of sunshine once the agreed index is triggered This study used the forecast of solar power generation of Changbin Power Plant in Taiwan as reference to do pricing for this index insurance , we analyzed the daily solar radiation data of NASA from 1984 to 2018 in Changbin area to simulate the historical power generation of Changbin Solar Power Plant, and used time series model to forecast power generation. We set P50 and P75 as trigger for the index insurance, the pure premium rate is 21.82% and 8.60% respectively.
Reference:	李鴻洲，2018。台電月刊666期。臺北：台灣電力股份有限公司。戴寶通、鄭晃忠，1998。太陽能電池技術手冊。新竹：台灣電子材料與元件協會饒瑞琦(2011)。太陽光電發電系統效能與可用度之研究，清雲科技大學電機工程系所學位論文。 Alsharif, M. H., Younes, M. K., & Kim, J. (2019). Time Series ARIMA Model for Prediction of Daily and Monthly Average Global Solar Radiation: The Case Study of Seoul, South Korea. Symmetry, 11(2), 240. Europe, S. (2018). Global Market Outlook for Solar Power 2018–2022. Solar Power Europe: Brussels, Belgium. Ghofrani, M., & Alolayan, M. (2017). Time Series and Renewable Energy Forecasting. In Time Series Analysis and Applications: IntechOpen. Jahanshahi, A., Jahanianfard, D., Mostafaie, A., & Kamali, M. (2019). An Auto Regressive Integrated Moving Average (ARIMA) Model for prediction of energy consumption by household sector in Euro area. Lowder, T., Mendelsohn, M., Speer, B., & Hill, R. (2013). Continuing developments in PV risk management: strategies, solutions, and implications. Retrieved from Mapfumo, S., Groenendaal, H., & Dugger, C. (2017). Risk Modeling for Appraising Named Peril Index Insurance Products: A Guide for Practitioners: The World Bank. Sawin, J. L., Rutovitz, J., Sverrisson, F., Aberg, E., Adib, R., Appavou, F., . . . Wuester, H. (2018). Renewables 2018. Global status report 2018
Description:	碩士國立政治大學風險管理與保險學系 106358025
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G1063580251
Data Type:	thesis
DOI:	10.6814/NCCU201900360
Appears in Collections:	[Department of Risk Management and Insurance] Theses

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